WO2023057650A1 - Procédé de prédiction d'effets secondaires de médicaments et de vaccins - Google Patents
Procédé de prédiction d'effets secondaires de médicaments et de vaccins Download PDFInfo
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Classifications
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- G—PHYSICS
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- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
Definitions
- the present invention relates to a method for predicting adverse side effects of drugs and vaccines.
- the present invention relates to a method for predicting side effects of a coronavirus vaccine and to a method for selecting a subject qualifying for vaccination with a coronavirus vaccine.
- Instant methods are particularly useful wherein the coronavirus vaccine is a SARS-CoV2 vaccine.
- Vaccine adverse effects are correlated with the activity of the immune system, which in turn is closely related to sex, age, medical background and underlying disorders, and drug history.[4] In 2018 Kopsaftis Z et al. reported enhanced injection site side effects of influenza vaccines in elderly and Chronic obstructive pulmonary disease (COPD) patients.[5] Immunocompromised patients with primary immunodeficiency and hematological malignancies might be susceptible to vaccine-derived infections and stronger levels of adverse effects. [6, 7]
- Document AU 2020/102250 discloses certain COVID-19 early warning system for senior citizens.
- Document AU 2020/101336 discloses certain machine learning technique for tracking the COVID-19 patients and their primary and secondary contacts to prevent the spread of COVID-19.
- Document EU2601197C2 discloses certain software system for predicting results of the treatment.
- Document CN111768873A discloses certain solution for predicting real-time risk of COVID-19 based on epidemiological findings.
- the present invention further relates to a method for predicting side effects of drugs, for example adverse drug reactions (ADRs).
- ADRs have always been a major challenge in healthcare, particularly recently with the increasing complexity of therapeutics that may increase the occurrences of various ADRs, an aging population that may need multiple medications administered together in order to survive, and rising multimorbidity which can complicate the action mechanisms of drugs resulting in excess AD Rs.
- ADR adverse drug reaction
- ADR can also include reactions occurring as a result of an error, misuse, or abuse of various medicines that may be unlicensed or available for off-label use in addition to the authorized medicinal products that are being administrated and prescribed in various doses by healthcare professionals. For both of these situations, the clinical practice should enhance its approach to managing ADRs.
- ADRs are a common manifestation in clinical practice, including as a cause of unscheduled hospital admissions, occurring during hospital admission and manifesting after discharge. [11-13] The incidence of ADRs is also directly related to the type of drug being administrated and each serious adverse reaction may need specialized preventative efforts. [14]
- Medicinal products that have been notably implicated in serious ADR-related hospital admissions include antiplatelets, anticoagulants, cytotoxics, immunosuppressants, diuretics, antidiabetics, and antibiotics.
- Fatal ADRs, when they occur, are often attributable to haemorrhage, which has caused a huge burden on professional healthcare management systems and patients.
- Targeted educational interventions to address underreporting of ADRs are essential to improve public health safety. There are many reasons for underreporting ADRs, especially in children is paramount to improving patient safety.
- Personalized medicine is an approach for specializing specific treatments for individual patients to optimize the results of treatment strategies. One might argue that one of the goals of personalized medicine should be the decrease in the number of ADRs as well.
- Document JP 2016020385A discloses certain Methods and compositions for the reduction of side effects of therapeutic treatments.
- the objective technical problem of the present invention is to provide a method for predicting side effects of a coronavirus in a subject.
- the present inventors have surprisingly found that the medical and personal records of vaccine or drug recipients can significantly support a personalized estimate of each individual’s immune response and adverse effects after vaccination. It has been further unexpectedly found by the present inventors that age had the highest negative influence on the probability of adverse side effects to occur among the factors studied by the present inventors. Thus, the probability of the side effects occurrence is inversely proportional to the age of a subject as younger individuals have more intense immune responses, and thus witness more side effects following administration of drugs and vaccines, in particular witness more vaccine side effects. The present inventors have further established the impact of the past medical history including underlying medical conditions and indications or use of specific drugs on the occurrence of drug or vaccine-related adverse effects.
- the present inventors have established the course of the past coronavirus disease, in particular COVID-19 infection as the second most influential factor when considering occurrence of side effects upon administration of COVID-19 vaccine.
- the subjects with a history of severe COVID-19 infection experienced more severe vaccine-related adverse effects. It has been further established that severe vaccine-related adverse effects were visible in subjects with a positive past medical history of cancer.
- the present invention addresses the unmet medical need and thereby shall help the healthcare authorities worldwide to select to select the safest drug or vaccine for subjects considering the predicted side effects that every individual subject would experience because of various drug or vaccine types, for example to select the safest vaccine for subjects considering the predicted side effects that every individual subject would experience because of various coronavirus, herein COVID-19 vaccine types.
- the method of the present invention can make drug and vaccine allocation smarter and safer for the general public.
- this invention provides a sorting mechanism for drugs and vaccines based on their predicted adverse side effects for every individual. This process can ultimately curb the concerns of patients being administrated with various medicinal products.
- the method of the present invention can make vaccine allocation smarter and safer for the general public.
- this invention provides a sorting mechanism for the COVID-19 vaccines based on their predicted adverse side effects for every individual. This process can ultimately help and encourage unvaccinated individuals to undergo vaccination.
- the invention will be summarized in the following embodiments.
- the present invention relates to a method for predicting side effects of a coronavirus vaccine in a subject, the method comprising (a) providing subject-specific health and life-style data; and (b) predicting the side effects of a coronavirus vaccine in a subject based on the subject-specific health and life-style data using a previously trained mathematical model.
- said method of the present invention is to be executed as a computer-implemented method.
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the subject-specific health and life-style data comprise subject physical parameters, course of the past coronavirus disease, data on medication(s) administered to the subject, medical background, and/or life style data, preferably wherein the subject-specific health and life-style data is collected by using a questionnaire.
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the subject physical parameters comprise weight, height, BMI, blood group, age, and/or sex.
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the course of the past coronavirus disease comprises data on severity, vertigo, sore throat, headache, chest pain, feeling paralyzed, loss of consciousness, breathing difficulties, loss of smell, digestive difficulties, cough, pain, fatigue and/or fever.
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the data on medication(s) administered to the subject comprise data on immunosuppressive therapy, chemotherapy, steroids (preferably cortone), respiratory spray, and/or hormone drugs.
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the medical background comprises the data on pregnancy, allergy, psychological issues, skeletal system, liver diseases, kidney diseases, digestive tract diseases, blood diseases, immune system diseases, lung diseases, neurological diseases, active cancer diseases, past history of cancer disease, hypertension, heart disease, and/or diabetes.
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the life-style data comprise the data on alcohol consumption, use of narcotics, and/or smoking.
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the subjectspecific health and life-style data further comprise data on side effects of a previous dose of a coronavirus vaccine.
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the data on side effects of the previous dose of a coronavirus vaccine comprise data on muscle pain, join paint, chills, nausea, headache, fatigue, fever and/or local side effects.
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the previously trained mathematical model is a machine learning model.
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the machine learning model is selected from Logistic Regression (LR), Random Forest (RF), MultiLayer Perceptron (MLP), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Gradient Boosted Decision Trees (XGBoost).
- LR Logistic Regression
- RF Random Forest
- MLP MultiLayer Perceptron
- KNN K-Nearest Neighbors
- SVM Support Vector Machine
- XGBoost Gradient Boosted Decision Trees
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the machine learning model is Logistic Regression (LR).
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the previously trained mathematical model has been trained using hyperparameter tuning.
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the predicted side effects of administering a vaccine to a subject include fever, fatigue, headache, nausea, chills, joint pain, muscle pain and/or local side effects.
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the method further comprises the step of (a’) training of the previously trained mathematical model.
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the previously trained mathematical model is trained using the database comprising subject-specific data on side effects of a coronavirus vaccine in a subject and subject-specific health and life-style data, wherein the database has been compiled for subjects that were previously vaccinated with a coronavirus vaccine.
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the data on side effects of a coronavirus vaccine in a subject is as described herein.
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the subjectspecific health and life-style data are as described herein.
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the previously trained mathematical model relies on predictors selected from subject-specific health and life-style data, wherein the subject specific health and life-style data are as described herein.
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the coronavirus vaccine is selected from DNA vaccine, RNA vaccine, adenovirus vector vaccine, inactivated virus vaccine, and subunit vaccine.
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the predicted side effects of a coronavirus vaccine are provided in a form of a subject-specific factsheet.
- the present invention relates to a method for selecting a subject qualifying for vaccination with a coronavirus vaccine, the method comprising (a) predicting the side effects of a coronavirus vaccine in a subject using the method according to the present invention, and (b) selecting a subject qualifying for vaccination with a coronavirus vaccine based on predicted side effects of a coronavirus vaccine in said subject.
- the method for predicting side effects of a coronavirus vaccine in a subject or the method for selecting a subject qualifying for vaccination with a coronavirus vaccine relates to an embodiment, wherein the coronavirus is SARS- CoV2.
- the present invention relates to a method for predicting side effects of a drug or a vaccine in a subject, the method comprising (a) providing subjectspecific health and life-style data; and (b) predicting the side effects of said drug or said vaccine in a subject based on the subject-specific health and life-style data using a previously trained mathematical model.
- said method of the present invention is to be executed as a computer-implemented method.
- the present invention relates to a method for predicting side effects of a drug or a vaccine in a subject, wherein the drug or the vaccine is a drug.
- said drug is selected from analgesics, antacids, antianxiety drugs, antiarrhythmics drugs, antibacterial drugs, antibiotics, anticoagulant and thrombolytic drugs, anticonvulsants drugs, antidepressants drugs, antidiarrheals drugs, antiemetics drugs, antifungals drugs, antihistaminic drugs, anti-inflammatory drugs, antineoplastic drugs, antipsychotic drugs, antipyretic drugs, antivirals, beta-blockers, bronchodilators, “cold cure” drugs, corticosteroids, cough drugs, suppressant drugs, cytotoxic drugs, decongestant drugs, diuretic drugs, expectorant drugs, hormone drugs, hypoglycemic (oral) drugs, immunosuppressive drugs, laxatives, muscle relaxant drugs, sedatives, sex hormones (f
- the present invention relates to a method for predicting side effects of a drug or a vaccine in a subject, wherein the drug or the vaccine is a vaccine.
- said vaccine is selected from DNA vaccine, RNA vaccine, adenovirus vector vaccine, inactivated virus vaccine, subunit vaccine, viruslike particles (VLP) vaccine, non-replicating viral vector vaccine, replicating viral vector vaccine, live-attenuated vaccine, toxoid vaccine, conjugate vaccine, recombinant protein vaccine, and outer Membrane vesicles (OMV) vaccine.
- VLP viruslike particles
- OMV outer Membrane vesicles
- the present invention relates to the method for predicting side effects of a drug or a vaccine in a subject, wherein the subject-specific health and life-style data comprise subject physical parameters, data on medication(s) administered to the subject, medical background, and/or life style data, preferably wherein the subject-specific health and life-style data is collected by using a questionnaire.
- the present invention relates to the method for predicting side effects of a drug or a vaccine in a subject, wherein the subject physical parameters comprise weight, height, BMI, blood group, age, and/or sex.
- the present invention relates to the method for predicting side effects of a drug or a vaccine in a subject, wherein the data on medication(s) administered to the subject comprise data on analgesics, antacids, antianxiety drugs, antiarrhythmics drugs, antibacterials drugs, antibiotics, anticoagulant and thrombolytic drugs, anticonvulsants drugs, antidepressants drugs, antidiarrheals drugs, antiemetics drugs, antifungals drugs, antihistaminic drugs, antiinflammatory drugs, antineoplastic drugs, antipsychotic drugs, antipyretic drugs, antivirals, beta-blockers, bronchodilators, “cold cure” drugs, corticosteroids, cough suppressant drugs, cytotoxic drugs, decongestant drugs, diuretic drugs, expectorant drugs, hormone drugs, hypoglycemic (Oral) drugs, immunosuppressive drugs, laxatives, muscle relaxant drugs, sedatives, sex hormones (Female), s
- the present invention relates to the method for predicting side effects of a drug or a vaccine in a subject, wherein the medical background comprises the data on pregnancy, allergy, psychological issues, skeletal system, liver diseases, kidney diseases, digestive tract diseases, blood diseases, immune system diseases, lung diseases, neurological diseases, active cancer diseases, past history of cancer disease, hypertension, heart disease, diabetes, endocrine diseases, rheumatologic diseases, reproductive and obstetrics diseases, and/or dermatologic diseases.
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the previously trained mathematical model is a machine learning model.
- the present invention relates to the method for predicting side effects of a drug or a vaccine in a subject, wherein the machine learning model is Random Forest (RF).
- RF Random Forest
- the present invention relates to the method for predicting side effects of a drug or a vaccine in a subject, wherein the previously trained mathematical model has been trained using hyperparameter tuning.
- the present invention relates to the method for predicting side effects of a drug or a vaccine in a subject, wherein the predicted side effects of administering said drug or said vaccine to a subject include abdominal pain, acute kidney injury, increased alanine aminotransferase level, anaemia, arthralgia, atrial fibrillation, back pain, balance disorder, increased blood creatinine level, increased diastolic blood pressure, increased systolic blood pressure, brain edema, cardiac failure congestive, cardiovascular related side effect, cerebrovascular accident, chest pain, chills, Clostridium difficile infection, confusional state, cough, death, decreased appetite, deep vein thrombosis, dermatological side effect, diarrhoea, dizziness, dry skin, dysphonia, dyspne
- the present invention relates to the method for predicting side effects of a drug or a vaccine in a subject, wherein the method further comprises the step of (a’) training of the previously trained mathematical model.
- the present invention relates to the method for predicting side effects of a drug or a vaccine in a subject, wherein the previously trained mathematical model is trained using the database comprising subject-specific data on side effects of a drug or a vaccine in a subject and subject-specific health and life-style data, wherein the database has been compiled for subjects that were previously vaccinated with a particular vaccine or treated with a particular drug.
- the present invention relates to the method for predicting side effects of a drug or a vaccine in a subject, wherein the data on side effects of a drug or a vaccine in a subject is as described herein.
- the present invention relates to the method for predicting side effects of a drug or a vaccine in a subject, wherein the subjectspecific health and life-style data are as described herein.
- the present invention relates to the method for predicting side effects of a drug or a vaccine in a subject, wherein the previously trained mathematical model relies on predictors selected from subject-specific health and life-style data, wherein the subject specific health and life-style data are as described herein.
- the present invention relates to the method for predicting side effects of a drug or a vaccine in a subject, wherein the predicted side effects are provided in a form of a subject-specific factsheet.
- Figure 1 presents the flow diagram of the incorporation of data for designing and improving machine learning and statistical analysis techniques.
- Figure 2 presents the flow diagram of the machine learning and statistical analysis technique for end users.
- Figure 3 presents an example of a personalized fact sheet, which is one of the applications of the invention.
- the Figure provides a possibility for each predicted adverse side effect between 0 and 1 . It may also consist of other popular and typical parts of fact sheet, such as a brief description of the administrated vaccine (or drug).
- Figure 4 presents the description of side effects and predictors features that have been used as input data to the models: A) frequency of occurrence of each side effect for first and second dose of a Covid-19 vaccine. B) frequency of binary features including sex, smoking and prior Covid-19 infection. C) distribution of continuous features including age and BMI, error bars shows standard deviation.
- Figure 5 presents performance of different models for the side effect prediction for the first dose of Sputnik V vaccine. Once the best hyperparameter is found for each model type for each side effect, model average performance is calculated on training, validation and test sets by using ROC-AUC parameter.
- Figure 6 presents performance of different models for the side effect prediction for the first dose of AstraZeneca vaccine. Once the best hyperparameter is found for each model type for each side effect, model average performance is calculated on training, validation and test sets by using ROC-AUC parameter.
- Figure 7 presents performance of different models for the side effect prediction for the second dose of Sputnik V vaccine. Once the best hyperparameter is found for each model type for each side effect, model average performance is calculated on training, validation and test sets by using ROC-AUC parameter.
- Figure 8 presents contribution of each feature to the side effect prediction for the first dose of Sputnik V vaccine.
- the Logistic regression model with the hyperparameter that has been found using hyperparameter tuning has been trained on a training set. To show each feature contribution to the final outcome Logistic regression coefficient was used. Negative coefficient means that feature has decreasing effect on the side effect probability. The positive coefficient shows features with increasing effect on the probability. In other words, the higher the numbers the more impact it had on the side effect probability.
- Figure 9 presents contribution of each feature to the side effect prediction for the second dose of Sputnik V vaccine.
- the feature set additionally comprises the side effects of the first dose (see Figure 8 hereinabove), which is positively correlated with side effects for the second dose of the vaccine.
- the Logistic regression model with the hyperparameter that has been found using hyperparameter tuning has been trained on a training set. To show each feature contribution to the final outcome Logistic regression coefficient was used. Negative coefficient means that feature has decreasing effect on the side effect probability.
- the positive coefficient shows features with increasing effect on the probability. In other words, the higher the numbers the more impact it had on the side effect probability.
- Figure 10 presents contribution of each feature to the side effect prediction for the first dose of AstraZeneca vaccine.
- the Logistic regression model with the hyperparameter that has been found using hyperparameter tuning has been trained on a training set. To show each feature contribution to the final outcome Logistic regression coefficient was used. Negative coefficient means that feature has decreasing effect on the side effect probability.
- the positive coefficient shows features with increasing effect on the probability. In other words, the higher the numbers the more impact it had on the side effect probability.
- the present invention relates to a method for predicting side effects of a coronavirus vaccine in a subject.
- the method of the present invention comprises the following steps:
- the subject is preferably a human subject.
- the method for predicting side effects of a coronavirus vaccine in a subject comprises step (a) of providing subject-specific health and life-style data.
- the invention is not limited with respect of how the subject-specific health and life-style data are collected and any means of collecting the said data from the subject as can be envisaged by the skilled person is encompassed by the present invention.
- the subject-specific health and life-style data is collected by using a questionnaire. Any type of questionnaire as to be envisaged by the skilled person can be used herein.
- questionnaire may be in a paper form to be filled by the subject, or questionnaire may be filled by the subject as an online form.
- the said questionnaire may be filled by a third party, for example by a physician or by a nurse, upon direct interview with the subject.
- the subject specific health and life-style data are represented by features that may be binary features, discrete features or continuous features.
- the features representing the subject specific health and life-style data are binary features.
- a binary feature is represented by a Boolean variable, i.e. its value may be 0 (negative) or 1 (positive).
- An example of such a binary feature is whether the subject is currently suffering from cancer, wherein the value of 0 would answer the question in the negative, and the value of 1 would answer the question in the affirmative.
- Certain features are continuous features, which means that they can take more than two values. These include height, weight, age, and BMI of a subject.
- these features may also be represented using a binary variable.
- BMI of a subject may be represented as being less than 35 or at least 35, which would correspond to a binary variable value of 0 (negative) and 1 (positive) and could be considered indicative e.g. of obesity status of a subject.
- a feature may also be a discrete variable, for example the blood group of a subject, which may constitute a selection from a finite (but higher than 2) number of elements. Similar to the representation of a continuous feature with a binary feature, continuous feature may also be represented by using a discrete feature.
- a non-limiting example would be classification of subject to predefined groups according to their age.
- the subjectspecific health and life-style data comprise subject physical parameters, course of the past coronavirus disease, data on medication(s) administered to the subject, medical background, and/or life style data. It is deemed that any selection of parameters as encompassed in the list recited hereinabove is encompassed within the scope of the present invention.
- the subject-specific health and life-style data comprise subject physical parameters.
- the subject physical parameters as defined hereinabove and within the scope of the present invention preferably comprise weight, height, BMI, blood group, age, and/or sex. It is noted that these parameters are well known to the person skilled in the art and are understood to be represented by the standard definition of each term.
- the subject physical parameters as defined herein comprise age, BMI and sex. Even more preferably, the subject physical parameters as defined herein comprise age. The present inventors have surprisingly found that younger people are more likely to have adverse side effects than the elderly.
- the present inventors postulate it may be due to the differences in the immune systems of the young and the elderly, which in turn cause inconsistencies in response to the vaccine.
- young people are preferably of not more than 30 years of age.
- elderly people are preferably at least 60 years old.
- the subject physical parameters as defined herein comprise sex. Sex is herein preferably defined as biological gender and is represented by a binary variable. The present inventors have surprisingly found that the women are more likely than men to get coronavirus vaccine-related side effects. Without wishing to be bound by the theory, the present inventors postulate that it can be due to a more robust female immune system.
- the subject-specific health and life-style data comprise course of the past coronavirus disease.
- the course of the past coronavirus disease comprises data on severity, vertigo, sore throat, headache, chest pain, feeling paralyzed, loss of consciousness, breathing difficulties, loss of smell, digestive difficulties, cough, pain, fatigue and/or fever.
- the course of the past coronavirus disease comprises data on vertigo, fever, digestive difficulties and/or fatigue. More preferably, the course of the past coronavirus disease comprises data on vertigo, fever, and/or digestive difficulties. Even more preferably, the course of the past coronavirus disease comprises data on vertigo, and/or fever. Most preferably, the course of the past coronavirus disease comprises data on vertigo.
- severity is preferably assessed by the subject as severe and not severe, it is thus represented by a binary feature.
- the severe is defined as need for hospitalization and not severe is defined as no need for hospitalization. It is thus preferably represented by a binary feature.
- vertigo is preferably assessed by the subject as having or not having a sensation of feeling off balance. It is thus preferably represented by a binary feature.
- sore throat is preferably assessed by the subject as having or not having pain, scratchiness, and/or irritation of the throat, any of which often worsens when swallowing.
- sore throat is preferably represented by a binary feature.
- headache is preferably assessed by the subject as having or not having pain in any region of the head. It is thus preferably represented by a binary feature.
- chest pain is preferably assessed by the subject as the presence or the lack of the presence of abnormal pain or discomfort in the chest, preferably between the diaphragm and the base of the neck.
- it is preferably represented by a binary feature.
- feeling paralyzed is preferably assessed by the subject as the feeling (or the lack thereof) of a loss of strength in and control over a muscle or group of muscles in a part of the body.
- a binary feature As understood herein, loss of consciousness is preferably assessed by the subject as the occurrence (or the lack thereof) of a partial or complete loss of consciousness with interruption of awareness of oneself and one's surroundings.
- a binary feature As understood herein, feeling paralyzed is preferably assessed by the subject as the feeling (or the lack thereof) of a loss of strength in and control over a muscle or group of muscles in a part of the body.
- loss of consciousness is preferably assessed by the subject as the occurrence (or the lack thereof) of a partial or complete loss of consciousness with interruption of awareness of oneself and one's surroundings.
- a binary feature As understood herein, feeling paralyzed is preferably assessed by the subject as the feeling (or the lack thereof) of a loss of strength in and control over a muscle or group of muscles in a part of
- breathing difficulties are preferably assessed by the subject as a subjective experience of breathing discomfort, which as known to the skilled person may consists of qualitatively distinct sensations that vary in intensity, or the lack thereof. Thus, it is preferably represented by a binary feature.
- loss of smell is preferably assessed by the subject as the loss of the ability to detect one or more smells, or the lack thereof. Thus, it is preferably represented by a binary feature.
- digestive difficulties are preferably assessed by the subject as presence of any health problem that occurs in the digestive tract (or the lack thereof). Thus, it is preferably represented by a binary feature.
- cough is preferably assessed by the subject as the condition of expelling air from the lungs suddenly with a sharp, short noise, or the lack thereof. Thus, it is preferably represented by a binary feature.
- pain is preferably assessed by the subject as the presence of unpleasant physical sensation caused by illness or injury, or the lack thereof.
- it is preferably represented by a binary feature.
- fatigue is preferably assessed by the subject by the presence of an overall feeling of tiredness or lack of energy.
- it is preferably represented by a binary feature.
- fever is preferably assessed by the subject as occurrence of an increase in body temperature, often due to an illness, preferably above 38 °C. Thus, it is preferably represented by a binary feature.
- the subject-specific health and life-style data comprise data on medication(s) administered to the subject.
- the data on medication(s) administered to the subject comprise data on immunosuppressive therapy, chemotherapy, steroids (preferably cortone), respiratory spray, and/or hormone drugs.
- the data on medication(s) administered to the subject comprise data on immunosuppressive therapy.
- the subject-specific health and life-style data does not comprise data on medication(s) administered to the subject.
- Immunosuppressive therapy as understood herein is administration of a drug or substance that lowers the activity of the body’s immune system. Lowering the activity of the body’s immune system is for example necessary for patients that have undergone organ transplant in order to prevent rejection of the transplant by the patient’s own immune system. Lowering the activity of the body immune system may also be necessary for the treatment of certain conditions, including autoimmune conditions, for example lupus erythematosus.
- Non-limiting examples of medications used for immunosuppressive therapy include azathioprine, mycophenolate mofetil, and cyclosporine.
- Chemotherapy as preferably understood herein is administration of a drug or substance (which also may be referred to as chemotherapeutic) for the treatment of cancer, preferably by destroying the cancer cells.
- chemotherapeutic a drug or substance
- chemotherapy works by preventing the cancer cells from dividing.
- Typical chemotherapeutics include alkylatic agents, antimetabolites, anti-microtubule agents, topoisomerase inhibitors and cytotoxic antibiotics.
- Treatment with steroids as defined herein is preferably broadly defined as administration of drugs that comprise the carbon skeleton of gonane, as shown in the formula:
- Respiratory spray as understood herein preferably relates to administration of a drug or substance in a form of aerosol by the means of a respiratory spray.
- anti-asthma medications are administered in such a way.
- Hormone drugs as referred to herein preferably refer to pregnancy or contraception drugs for females that contain hormonal substances such as estrogen and progestin.
- the subject-specific health and life-style data comprise medical background of a subject.
- the medical background comprises the data on pregnancy, allergy, psychological issues, skeletal system, liver diseases, kidney diseases, digestive tract diseases, blood diseases, immune system diseases, lung diseases, neurological diseases, active cancer diseases, past history of cancer disease, hypertension, heart disease, and/or diabetes.
- the medical background comprises the data on digestive tract diseases, past history of cancer disease and/or allergy. More preferably, the medical background comprises the data on allergy and/or past history of cancer disease. Even more preferably, the medical background comprises the data on allergy.
- the terms pregnancy, allergy, psychological issues, skeletal system diseases, liver diseases, kidney diseases, digestive tract diseases, blood diseases, immune system diseases, lung diseases, neurological diseases, active cancer diseases, past history of cancer disease, hypertension, heart disease, and/or diabetes are understandable to the person skilled in the art.
- the medical background with respect to any of these conditions preferably refers to having ever been diagnosed with a said condition.
- the subject with a medical background of allergy, psychological issues, skeletal system diseases, liver diseases, lung diseases, neurological diseases, hypertension, heart diseases and/or diabetes is a subject previously diagnosed with allergy, psychological issues, skeletal system diseases, liver diseases, lung diseases, neurological diseases, hypertension, heart diseases and/or diabetes, respectively.
- a subject with medical background of said conditions is a subject that is currently characterized by the said conditions. This applies to pregnancy and active cancer disease.
- a subject with a medical background of pregnancy is currently pregnant, and a subject with a medical background of an active cancer disease has currently cancer.
- the medical background of previous cancer disease relates to a subject that had had a cancer disease before but cannot be characterized as a subject with medical background of an active cancer disease.
- the features discussed hereinabove are preferably to be represented by binary features within the scope of the methods of the present invention.
- psychological issues preferably may include bipolar disorder, depression, psychosis, and schizophrenia.
- skeletal system diseases preferably may include arthritis, gout, osteoporosis, Paget's disease, and tendonitis.
- liver diseases preferably may include fatty liver, gallstone, hepatitis, liver cirrhosis, and liver failure.
- kidney diseases preferably may include chronic kidney disorder, glomerulonephritis, and polycystic kidney disease.
- digestive tract diseases preferably may include inflammatory bowel disorders, intestinal polyps, irritable bowel syndrome, malabsorption disorders, stomach reflex, and ulcers.
- blood diseases preferably may include anemia, coagulation disorders, favism, alpha thalassemia, and beta thalassemia.
- immune system diseases preferably may include AIDS, lupus, and vasculitis.
- lung diseases preferably may include asthma, chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, and pulmonary tuberculosis.
- neurological diseases preferably may include Alzheimer’s disease, cerebral palsy, dementia, epilepsy, multiple sclerosis, Parkinson’s disease, and stroke.
- heart diseases preferably may include coronary artery disease, heart valve disorder, hypertrophic cardiomyopathy, heart ischemia, myocarditis, and cardiac rheumatism.
- the subject specific health and life-style data comprise the life-style data.
- the life-style data comprise the data on alcohol consumption, use of narcotics, and/or smoking.
- the subject specific health and life-style data does not comprise the life-style data as defined herein.
- the life-style data is to be represented as binary feature(s).
- the method for predicting side effects of a coronavirus vaccine in a subject relates to an embodiment, wherein the subject-specific health and life-style data further comprise data on side effects of a previous dose of a coronavirus vaccine.
- the data on side effects of a previous dose of a coronavirus vaccine can be taken into account when the subject has been previously administered with at least one dose of any coronavirus vaccine.
- the previous dose of a coronavirus vaccine may refer to the same coronavirus vaccine that the method of the present invention predicts the side effect thereof, or it may refer to a different coronavirus vaccine.
- the method of the present invention for predicting side effects of a coronavirus vaccine in a subject as described herein wherein the subject-specific health and life-style data further comprise data on side effects of a previous dose of a coronavirus vaccine is also applicable to subjects that have not received any dose of a coronavirus vaccine.
- the data on side effects of a previous dose of a coronavirus vaccine preferably encompass the data on side effects of the previous dose of a coronavirus vaccine comprise data on muscle pain, join paint, chills, nausea, headache, fatigue, fever and/or local side effects.
- the local side effects refer to pain at the injection site, redness at the injection site, and/or swelling at the injection site.
- side effects, in particular adverse side effects are one of the main concerns for any vaccine following its administration to a subject.
- Adverse side effects of different vaccines can range from mild instances to those that may require hospitalization. Adverse side effects may include pain, redness, and swelling at the injection site, joint pain, muscle aches, headache, fatigue, nausea, fever, chills. Other more advanced complications may occur, such as forming blood clots and even anaphylactic shock.
- muscle pain, joint pain, chills, nausea, headache, fatigue, and/or fever are known to the skilled person and are preferably reported by the subject, as described herein.
- the subject is preferably to report in the affirmative or in the negative if any of the said adverse side effects as defined hereinabove has occurred to him/her, preferably by using a questionnaire.
- the local side effects preferably refer to pain at the injection site, redness at the injection site, and/or swelling at the injection site. They are preferably reported by the subject, as described hereinabove.
- the method for predicting side effects of a coronavirus vaccine in a subject relates to an embodiment, wherein the previously trained mathematical model is a machine learning model.
- the machine learning model is selected from Logistic Regression (LR), Random Forest (RF), Multi-Layer Perceptron (MLP), K- Nearest Neighbors (KNN), Support Vector Machine (SVM), and Gradient Boosted Decision Trees (XGBoost). More preferably, the machine learning model is Logistic Regression (LR).
- the Logistic Regression is a classification model, a statistical method for binary classification that can be generalized to multiclass classification.
- Scikit-learn has a highly optimized version of logistic regression implementation, which supports multiclass classification tasks. (Practical Machine Learning for Data Analysis Using Python. https://www.sciencedirect.com/book/9780128213797/practical-machine-learning-for- data-analysis-using-python).
- Random Forest is a supervised learning algorithm.
- the "forest” it builds, is an ensemble of decision trees, usually trained with the “bagging” method.
- the general idea of the bagging method is that a combination of learning models increases the overall result.
- Random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction.
- a multilayer perceptron is a feedforward artificial neural network that generates a set of outputs from a set of inputs.
- An MLP is characterized by several layers of input nodes connected as a directed graph between the input and output layers.
- MLP uses backpropagation for training the network.
- MLP is a deep learning method. (Techopedia. Multilayer Perceptron (MLP). http://www.techopedia.com/definition/20879/multilayer-perceptron-mlp (2017))
- K-Nearest Neighbors relies only on the most basic assumption underlying all predictions: that observations with similar characteristics will tend to have similar outcomes.
- Nearest Neighbor methods assign a predicted value to a new observation based on the plurality or mean (sometimes weighted) of its k “Nearest Neighbors” in the training set. Given an infinite amount of data, any observation will have many “neighbors” that are arbitrarily near with respect to all measured characteristics, and the variability of their outcomes will provide as precise a prediction as is theoretically possible barring a perfectly and completely specified model.
- Support Vector Machine is a supervised machine learning algorithm that can be used for both classification or regression challenges. However, it is mostly used in classification problems. In the SVM algorithm, each data item is plotted as a point in n-dimensional space (where n is a number of features) with the value of each feature being the value of a particular coordinate. Then, classification by finding the hyper-plane that differentiates the two classes very well is performed. (Ray, S. Understanding Support Vector Machine (SVM) algorithm from examples (along with code), https://www.analyticsvidhya.com/blog/2017/15/15/15staing- support-vector-machine-example-code/ (2017))
- SVM Support Vector Machine
- Gradient-boosted decision trees are a machine learning technique for optimizing the predictive value of a model through successive steps in the learning process.
- Each iteration of the decision tree involves adjusting the values of the coefficients, weights, or biases applied to each of the input variables being used to predict the target value, with the goal of minimizing the loss function (the measure of the difference between the predicted and actual target values).
- the gradient is the incremental adjustment made in each step of the process; boosting is a method of accelerating the improvement in predictive accuracy to a sufficiently optimum value.
- GDT Gradient-Boosted Decision Trees
- the previously trained mathematical model has been trained using hyperparameter tuning.
- the hyperparameter tuning is a process in machine learning, wherein the hyperparameter, a parameter that is used to control the learning process, is used. At least one parameter is not optimized in the learning process but is set so that the machine learning model can optimally solve the machine learning problem.
- one or more hyperparameter(s) is used. To this end, the present inventors have run a separate hyperparameter tuning job for each machine learning model as disclosed hereinabove and for each side effect to be predicted.
- the method for predicting side effects of a coronavirus vaccine in a subject further comprises the step (a’) of training of the previously trained mathematical model.
- the previously trained mathematical model is so trained so that it relies on the predictors.
- the term predictor is known to the skilled person and may also be referred to as an independent variable.
- the predictors are preferably selected from the subject-specific health and life-style data, and may comprise the features selected from subject physical parameters, course of the past coronavirus disease, data on medication(s) administered to the subject, medical background, and/or life style data, as referred to herein.
- the predictors are used as an input of a previously trained mathematical model, as discussed hereinbelow.
- the training of the previously trained mathematical model relies on one or more hyperparameter, as described hereinabove.
- the previously trained mathematical model is trained using the database.
- the said database has been compiled for subjects that were previously vaccinated with a coronavirus vaccine.
- the database preferably comprises subject-specific data on side effects of a coronavirus vaccine in a subject.
- This data corresponds to the data output which may also be referred to as predicted side effects and is as discussed hereinbelow.
- the subject-specific data on side effects of a coronavirus vaccine in a subject are selected from fever, fatigue, headache, nausea, chills, joint pain, muscle pain and local side effects.
- the local side effects refer to pain at the injection site, redness at the injection site, and/or swelling at the injection site.
- the said database further comprises subject-specific health and life-style data.
- the said subject-specific health and life-style data is herein used as input of the previously trained mathematical model.
- the subject-specific health and life-style data used as an input of the previously trained mathematical model comprise subject physical parameters, course of the past coronavirus disease, data on medication(s) administered to the subject, medical background, and/or life style data.
- the subject physical parameters as defined hereinabove and within the scope of the present invention preferably comprise weight, height, BMI, blood group, age, and/or sex.
- the subject physical parameters as defined herein comprise age, BMI and sex. Even more preferably, the subject physical parameters as defined herein comprise age.
- the subject-specific health and life-style data used as an input of a previously trained mathematical model comprise preferably the course of the past coronavirus disease.
- the course of the past coronavirus disease comprises data on severity, vertigo, sore throat, headache, chest pain, feeling paralyzed, loss of consciousness, breathing difficulties, loss of smell, digestive difficulties, cough, pain, fatigue and/or fever.
- the course of the past coronavirus disease comprises data on vertigo, fever, digestive difficulties and/or fatigue. More preferably, the course of the past coronavirus disease comprises data on vertigo, fever, and/or digestive difficulties. Even more preferably, the course of the past coronavirus disease comprises data on vertigo, and/or fever.
- the course of the past coronavirus disease comprises data on vertigo.
- the subject-specific health and life-style data used as an input of a previously trained mathematical model comprise preferably data on medication(s) administered to the subject.
- the data on medication(s) administered to the subject comprise data on immunosuppressive therapy, chemotherapy, steroids (preferably cortone), respiratory spray, and/or hormone drugs.
- the data on medication(s) administered to the subject comprise data on immunosuppressive therapy.
- the subject-specific health and life-style data does not comprise data on medication(s) administered to the subject.
- the subject-specific health and life-style data used as an input of a previously trained mathematical model comprise preferably medical background of a subject.
- the medical background comprises the data on pregnancy, allergy, psychological issues, skeletal system, liver diseases, kidney diseases, digestive tract diseases, blood diseases, immune system diseases, lung diseases, neurological diseases, active cancer diseases, past history of cancer disease, hypertension, heart disease, and/or diabetes.
- the medical background comprises the data on digestive tract diseases, past history of cancer disease and/or allergy. More preferably, the medical background comprises the data on allergy and/or past history of cancer disease. Even more preferably, the medical background comprises the data on allergy.
- the subject specific health and life-style data used as an input of a previously trained mathematical model comprise preferably the life-style data.
- the life-style data comprise the data on alcohol consumption, use of narcotics, and/or smoking.
- the subject specific health and life-style data used as an input of a previously trained mathematical model does not comprise the life-style data as defined herein.
- the subject-specific health and life-style data used as an input of a previously trained mathematical model may further comprise data on side effects of a previous dose of a coronavirus vaccine.
- the data on side effects of a previous dose of a coronavirus vaccine preferably encompass the data on side effects of the previous dose of a coronavirus vaccine comprise data on muscle pain, join paint, chills, nausea, headache, fatigue, fever and/or local side effects.
- the local side effects refer to pain at the injection site, redness at the injection site, and/or swelling at the injection site.
- certain parameters of the said subject-specific health and lifestyle data used as an input of a previously trained mathematical model are preferably used by the previously trained mathematical model as predictors.
- certain parameters of the said subject-specific health and life-style data used as an input of a previously trained mathematical model are used to train the previously trained mathematical model.
- Figure 1 illustrates the flow diagram of the incorporation of data for designing and improving the previously trained mathematical model (herein preferably a machine learning model) and statistical analysis techniques.
- the training relies on the data of previously vaccinated recipients (100).
- the input of the system (104) in particular includes data of the dose and type of the vaccine administrated (101 ) and subject specific health and life-style data (102) for each individual.
- the said input (104) is subjected to the preanalysis of the input data (105) to make it suitable for the training of the previously trained mathematical model (106).
- this step (105) may include translating obtained data into binary or discrete features.
- the data of previously vaccinated recipients (100) is further use to provide the training of the previously trained mathematical model (106) with the data of the side effects that previously occurred (103) for the training purposes.
- an output is provided which may also be referred to as a finalized system that can predict the possibility of outcomes solely based on the input data.
- the flow diagram shown in Figure 1 may be considered generalizable to an embodiment wherein the side effects of a drug are predicted, provided than (100) is replaced with recipients previously administered with said drug and (101) with dose and type of drug administered.
- the method of the present invention may be implemented as a previously trained mathematical model that is available for use by an end user.
- said trained mathematical model is computer- implemented.
- Figure 2 illustrates the flow diagram of the previously trained mathematical model (preferably herein machine learning model) and statistical analysis technique for end users.
- the end user for example, a subject that may be eligible for vaccination with a Coronavirus vaccine
- a system input may include subject specific health data (201 ), subject specific life-style data (202) and data on the side effects of any previous coronavirus vaccine (or any other vaccine or a drug) administered to the said subject (203).
- the system performs then analysis of the input data by using the previously trained mathematical model (204) and predicts the possible adverse side effects of the vaccines or drugs (205), according to the input (201-203) of the end user (200). Finally, the results of the said predictions are provided (206). In certain embodiments of the present invention, the said results (206) can be provided in a form of a personalized fact sheet.
- the present invention relates to the method for predicting side effects of a coronavirus vaccine in a subject, wherein the predicted side effects of a coronavirus vaccine are provided in a form of a subject-specific factsheet.
- the methods of the present invention may include in certain embodiments generating reports, such as a personalized fact sheet that can be provided to vaccine recipients to handle the concerns of vaccines’ safety
- the method for predicting side effects of a coronavirus vaccine in a subject predicts side effects that are selected from fever, fatigue, headache, nausea, chills, joint pain, muscle pain and local side effects.
- the said predicted side effects include fever, fatigue, headache, nausea, chills, joint pain, muscle pain and/or local side effects.
- the predicted side effects may differ depending on whether the prediction is performed for a first vaccination or any subsequent vaccination.
- the predicted side effects may be provided as binary features for each side effect, that is, whether the side effect is likely to occur (1) or unlikely to occur (0).
- the said predicted side effects may be provided in a form of probability of an occurrence of such a side effect, that is in the form of a continuous variable in a range from 0 to 1 .
- a personalized fact sheet includes preferably the predicted side effects (e.g. with a calculated possibility between 0 and 1 for each adverse side effect for each subject).
- Figure 3 illustrates an example of a personalized fact sheet, which is one of the applications of the invention. Figure 3 provides a possibility for each predicted adverse side effect between 0 and 1. It may also consist of other popular and typical parts of fact sheet, such as a brief description of the administrated vaccine.
- the present invention relates to a method for selecting a subject qualifying for vaccination with a coronavirus vaccine.
- the method comprises the step (a) of predicting the side effects of a coronavirus vaccine in a subject using the method for predicting the side effects of a coronavirus vaccine in a subject as disclosed herein.
- the method for selecting a subject qualifying for vaccination with a coronavirus vaccine further comprises the step (b) of selecting a subject qualifying for vaccination with a coronavirus vaccine based on predicted side effects of a coronavirus vaccine in said subject.
- the step (a) of the method for selecting a subject qualifying for vaccination with a coronavirus vaccine can be performed for different coronavirus vaccines. Comparison of predicted side effects for different vaccines may constitute the basis for the step (b) of the said method for selecting a subject qualifying for vaccination with a coronavirus vaccine.
- the method for selecting a subject qualifying for vaccination with a coronavirus vaccine may comprise obtaining personalized factsheets for a subject for different coronavirus vaccines in step (a) of the method for selecting a subject qualifying for vaccination with a coronavirus vaccine, and comparing them with each other in order to inform the step (b) of the method for selecting a subject qualifying for vaccination with a coronavirus vaccine.
- the method for predicting the side effects of a coronavirus vaccine in a subject of the present invention according to the embodiments as disclosed herein and/or the method for selecting a subject qualifying for vaccination with a coronavirus vaccine of the present invention according to the embodiments as disclosed herein is/are not limited with regard to any particular coronavirus vaccine.
- the coronavirus vaccine may be selected from DNA vaccine, RNA vaccine, adenovirus vector vaccine, inactivated virus vaccine, and subunit vaccine.
- the term AstraZeneca vaccine preferably refers to a Covid-19 vaccine known as Vaxzevria.
- the term Sputnik V vaccine preferably refers to a Covid-19 vaccine also known as Gam-COVID-Vac.
- the coronavirus is preferably SARS-CoV2.
- the coronavirus vaccine is preferably a vaccine against SARS-CoV2 coronavirus, in other words the coronavirus vaccine is preferably a Covid- 19 vaccine.
- the present invention relates to a method for predicting side effects of a drug or a vaccine in a subject, the method comprising:
- said method of the present invention is to be executed as a computer-implemented method.
- the subject is as defined hereinabove. Accordingly, preferably the subject is a human subject.
- the drug or the vaccine can be a drug.
- the present invention relates to a method for predicting side effects of a drug in a subject, the method comprising:
- said method of the present invention is to be executed as a computer-implemented method.
- said drug is selected from analgesics, antacids, antianxiety drugs, antiarrhythmics drugs, antibacterial drugs, antibiotics, anticoagulant and thrombolytic drugs, anticonvulsants drugs, antidepressants drugs, antidiarrheals drugs, antiemetics drugs, antifungals drugs, antihistaminic drugs, anti-inflammatory drugs, antineoplastic drugs, antipsychotic drugs, antipyretic drugs, antivirals, beta-blockers, bronchodilators, “cold cure” drugs, corticosteroids, cough drugs, suppressant drugs, cytotoxic drugs, decongestant drugs, diuretic drugs, expectorant drugs, hormone drugs, hypoglycemic (oral) drugs, immunosuppressive drugs, laxatives, muscle relaxant drugs, sedatives, sex hormones (female), sex hormones (male), sleeping drugs, tranquilizer drugs, and vitamins.
- analgesics antacids, antianxiety drugs, antiarrhythmics drugs, anti
- the drug or the vaccine can be a vaccine.
- the present invention relates to a method for predicting side effects of a vaccine in a subject, the method comprising:
- said method of the present invention is to be executed as a computer-implemented method.
- said vaccine is selected from DNA vaccine, RNA vaccine, adenovirus vector vaccine, inactivated virus vaccine, subunit vaccine, virus-like particles (VLP) vaccine, non-replicating viral vector vaccine, replicating viral vector vaccine, live- attenuated vaccine, toxoid vaccine, conjugate vaccine, recombinant protein vaccine, outer Membrane vesicles (OMV) vaccine.
- VLP virus-like particles
- OMV outer Membrane vesicles
- the subject-specific health and life-style data comprise subject physical parameters, data on medication(s) administered to the subject, medical background, and/or life style data. It is preferred that the subject-specific health and life-style data is collected by using a questionnaire.
- the invention is not limited with respect of how the subject-specific health and life-style data are collected and any means of collecting the said data from the subject as can be envisaged by the skilled person is encompassed by the present invention.
- the subject-specific health and life-style data is collected by using a questionnaire. Any type of questionnaire as to be envisaged by the skilled person can be used herein.
- questionnaire may be in a paper form to be filled by the subject, or questionnaire may be filled by the subject as an online form.
- the said questionnaire may be filled by a third party, for example by a physician or by a nurse, upon direct interview with the subject.
- the physical parameters of a subject are as described hereinabove.
- the subject physical parameters comprise weight, height, BMI, blood group, age, and/or sex.
- the data on medication(s) administered to the subject comprise data on analgesics, antacids, antianxiety drugs, antiarrhythmics drugs, antibacterials drugs, antibiotics, anticoagulant and thrombolytic drugs, anticonvulsants drugs, antidepressants drugs, antidiarrheals drugs, antiemetics drugs, antifungals drugs, antihistaminic drugs, anti-inflammatory drugs, antineoplastic drugs, antipsychotic drugs, antipyretic drugs, antivirals, beta-blockers, bronchodilators, “cold cure” drugs, corticosteroids, cough suppressant drugs, cytotoxic drugs, decongestant drugs, diuretic drugs, expectorant drugs, hormone drugs, hypoglycemic (Oral) drugs, immunosuppressive drugs, laxatives, muscle relaxant drugs, sedatives, sex hormones (Female), sex hormones (Male), sleeping drugs
- analgesics as defined herein are drugs that relieve pain.
- antacids as defined herein are drugs that relieve indigestion and heartburn by neutralizing stomach acid.
- antianxiety drugs are drugs that suppress anxiety and relax muscles.
- Said antianxiety drugs are sometimes also referred to as anxiolytics, sedatives, or minor tranquilizers, as apparent to the skilled person.
- antiarrhythmics drugs are drugs used to control irregularities of heartbeat.
- antibacterials drugs are drugs used to treat bacterial infections.
- antibiotics are drugs made from naturally occurring and synthetic substances that combat bacterial infection. Some antibiotics are effective only against limited types of bacteria. Others, known as broad spectrum antibiotics, are effective against a wide range of bacteria.
- anticoagulant drugs prevent blood from clotting.
- thrombolytics drugs as defined herein, help dissolve and disperse blood clots and may be prescribed for patients with recent arterial or venous thrombosis.
- anticonvulsants drugs are drugs that prevent epileptic seizures.
- antidepressants drugs are the mood lifting anti- depressants.
- mood-lifting antidepressants There are three main groups of mood-lifting antidepressants: tricyclics, monoamine oxidase inhibitors, and selective serotonin reuptake inhibitors (SSRIs).
- SSRIs selective serotonin reuptake inhibitors
- antidiarrheals drugs are drugs used for the relief of diarrhea.
- Two main types of antidiarrheal preparations are simple adsorbent substances and drugs that slow down the contractions of the bowel muscles so that the contents are propelled more slowly.
- antiemetics drugs are drugs used to treat nausea and vomiting.
- antifungals drugs are drugs used to treat fungal infections, the most common of which affect the hair, skin, nails, or mucous membranes.
- antihistaminic drugs are drugs used primarily to counteract the effects of histamine, one of the chemicals involved in allergic reactions.
- anti-inflammatory drugs are drugs used to reduce inflammation - the redness, heat, swelling, and increased blood flow found in infections and in many chronic noninfective diseases such as rheumatoid arthritis and gout.
- antineoplastic drugs are drugs used to treat cancer.
- antipsychotic drugs are drugs used to treat symptoms of severe psychiatric disorders. These drugs are sometimes called major tranquilizers.
- antipyretic drugs are drugs used to treat fever.
- antivirals are drugs used to treat viral infections or to provide temporary protection against infections such as influenza.
- beta-blockers also referred to as beta-adrenergic blocking agents, as defined herein, are drugs that reduce the oxygen needs of the heart by reducing heartbeat rate.
- bronchodilators are drugs that open up the bronchial tubes within the lungs when the tubes have become narrowed by muscle spasm. Bronchodilators ease breathing in diseases such as asthma.
- cold cure drugs are drugs that are used to treat the common cold. Although there is no drug that can cure a cold, the aches, pains, and fever that accompany a cold can be relieved by aspirin or acetaminophen often accompanied by a decongestant, antihistamine, and sometimes caffeine, which may be referred to as “cold cure” drugs.
- corticosteroids are hormonal preparations that are used primarily as anti-inflammatories in arthritis or asthma or as immunosuppressives, but they are also useful for treating some malignancies or compensating for a deficiency of natural hormones in disorders such as Addison's disease.
- cough suppressants are substances used to suppress cough.
- Simple cough medicines which contain substances such as honey, glycerine, or menthol, soothe throat irritation but do not actually suppress coughing. They are most soothing when taken as lozenges and dissolved in the mouth. As liquids they are probably swallowed too quickly to be effective. A few drugs are actually cough suppressants.
- cough suppressants There are two groups of cough suppressants: those that alter the consistency or production of phlegm such as mucolytics and expectorants; and those that suppress the coughing reflex such as codeine (narcotic cough suppressants), antihistamines, dextromethorphan and isoproterenol (non-narcotic cough suppressants).
- cytotoxic drugs are drugs that kill or damage cells. Cytotoxics are used as antineoplastics (drugs used to treat cancer) and also as immunosuppressives.
- decongestant drugs are drugs that reduce swelling of the mucous membranes that line the nose by constricting blood vessels, thus relieving nasal stuffiness.
- diuretic drugs are drugs that increase the quantity of urine produced by the kidneys and passed out of the body, thus ridding the body of excess fluid.
- Diuretics reduce water logging of the tissues caused by fluid retention in disorders of the heart, kidneys, and liver. They are useful in treating mild cases of high blood pressure.
- expectorant drugs are drugs that stimulate the flow of saliva and promote coughing to eliminate phlegm from the respiratory tract.
- hormone drugs are chemicals produced naturally by the endocrine glands (thyroid, adrenal, ovary, testis, pancreas, parathyroid).
- thyroid, adrenal, ovary, testis, pancreas, parathyroid In some disorders, for example, diabetes mellitus, in which too little of a particular hormone is produced, synthetic equivalents or natural hormone extracts are prescribed to restore the deficiency. Such treatment is known as hormone replacement therapy.
- hypoglycemic (oral) drugs are drugs that lower the level of glucose in the blood.
- Oral hypoglycemic drugs are used in diabetes mellitus if it cannot be controlled by diet alone, but does require treatment with injections of insulin.
- immunosuppressive drugs are drugs that prevent or reduce the body's normal reaction to invasion by disease or by foreign tissues. Immunosuppressives are used to treat autoimmune diseases (in which the body's defenses work abnormally and attack its own tissues) and to help prevent rejection of organ transplants.
- laxatives are drugs that increase the frequency and ease of bowel movements, either by stimulating the bowel wall (stimulant laxative), by increasing the bulk of bowel contents (bulk laxative), or by lubricating them (stoolsofteners, or bowel movement-softeners). Laxatives may be taken by mouth or directly into the lower bowel as suppositories or enemas. If laxatives are taken regularly, the bowels may ultimately become unable to work properly without them.
- muscle relaxant drugs are drugs that relieve muscle spasm in disorders such as backache.
- Antianxiety drugs minor tranquilizers
- muscle-relaxant action are used most commonly.
- sedatives as defined herein, as antianxiety drugs, as defined hereinabove.
- sex hormones are two groups of hormones (estrogens and progesterone), which are responsible for development of female secondary sexual characteristics. Small quantities are also produced in males.
- estrogens may be used to treat cancer of the breast or prostate, progestins (synthetic progesterone to treat endometriosis).
- sex hormones are responsible for development of male secondary sexual characteristics. Small quantities are also produced in females.
- male sex hormones are given to compensate for hormonal deficiency in hypopituitarism or disorders of the testes. They may be used to treat breast cancer in women, but either synthetic derivatives called anabolic steroids, which have less marked side- effects, or specific anti-estrogens are often preferred.
- Anabolic steroids also have a "body building" effect that has led to their (usually nonsanctioned) use in competitive sports, for both men and women.
- sleeping drugs are drugs used to induce sleep.
- the two main groups of drugs that are used to induce sleep are benzodiazepines and barbiturates. All such drugs have a sedative effect in low doses and are effective sleeping medications in higher doses.
- Benzodiazepines drugs are used more widely than barbiturates because they are safer, the side-effects are less marked, and there is less risk of eventual physical dependence.
- tranquilizer drugs is a term commonly used to describe any drug that has a calming or sedative effect.
- drugs that are sometimes called minor tranquilizers should be called antianxiety drugs, and the drugs that are sometimes called major tranquilizers should be called antipsychotics.
- vitamins are chemicals essential in small quantities for good health. Some vitamins are not manufactured by the body, but adequate quantities are present in a normal diet. People whose diets are inadequate or who have digestive tract or liver disorders may need to take supplementary vitamins.
- the medical background comprises the data on pregnancy, allergy, psychological issues, skeletal system, liver diseases, kidney diseases, digestive tract diseases, blood diseases, immune system diseases, lung diseases, neurological diseases, active cancer diseases, past history of cancer disease, hypertension, heart disease, diabetes, endocrine diseases, rheumatologic diseases, reproductive and obstetrics diseases, and/or dermatologic diseases.
- the endocrine diseases preferably include Goiter, Graves’ disease, Hashimoto’s thyroiditis, hyperthyroidism and hypothyroidism.
- the rheumatologic diseases preferably include fibromyalgia, gout, osteomalacia, rheumatoid arthritis, and vasculitis.
- the reproductive and obstetrics diseases preferably include cervicitis, gonorrhea, perimenopause and trichomoniasis.
- the dermatologic diseases as referred to herein, preferably include psoriasis exacerbation, melanoma, chronic urticaria and rosacea.
- the previously trained mathematical model is preferably a machine learning model.
- said model is a computer-implemented model.
- the machine learning model is as described hereinabove.
- Particularly preferred machine learning model is Random Forest (RF).
- RF Random Forest
- the previously trained mathematical model has been trained using hyperparameter tuning.
- the Random Forest (RF) model has preferably been trained using hyperparameter tuning.
- the predicted side effects of administering said drug or said vaccine to a subject is preferably selected from abdominal pain, acute kidney injury, increased alanine aminotransferase level, anaemia, arthralgia, atrial fibrillation, back pain, balance disorder, increased blood creatinine level, increased diastolic blood pressure, increased systolic blood pressure, brain edema, cardiac failure congestive, cardiovascular related side effect, cerebrovascular accident, chest pain, chills, Clostridium difficile infection, confusional state, cough, death, decreased appetite, deep vein thrombosis, dermatological side effect, diarrhoea, dizziness, dry skin, dysphonia, dyspnea, epistaxis, erythema, fall, fatigue, fever, general physical health deterioration, decreased hematocrit level, decreased hemoglobin level, hemorrhage, harsh, headache, decreased heart rate, hem
- the predicted side effects of administering said drug or said vaccine to a subject are preferably abdominal pain, acute kidney injury, increased alanine aminotransferase level, anaemia, arthralgia, atrial fibrillation, back pain, balance disorder, increased blood creatinine level, increased diastolic blood pressure, increased systolic blood pressure, brain edema, cardiac failure congestive, cardiovascular related side effect, cerebrovascular accident, chest pain, chills, Clostridium difficile infection, confusional state, cough, death, decreased appetite, deep vein thrombosis, dermatological side effect, diarrhoea, dizziness, dry skin, dysphonia, dyspnea, epistaxis, erythema, fall, fatigue, fever, general physical health deterioration, decreased hematocrit level, decreased hemoglobin level, hemorrhage, harsh, headache, decreased heart rate, hemi
- the predicted side effects of administering said drug or said vaccine to a subject are preferably selected from neutropenia, hemorrhage, dysphonia, brain edema, fall, oxygen saturation decreased, decreased weight, decreased hemoglobin level, decreased hematocrit level, increased monocyte count, increased weight, back pain, decreased platelet count, increased blood creatinine level, increased diastolic blood pressure, general physical health deterioration, nasopharyngitis, proteinuria, increased systolic blood pressure, malaise, seizure, confusional state, decreased heart rate, decreased lymphocyte count, malignant neoplasm progression, decreased mean platelet volume, pyrexia, decreased red blood cell count, urinary tract infection, diarrhea, hypertension, decreased mobility, fatigue, hemiparesis, increased mean cell volume increased, increased neutrophil count, rectal haemorrhage, decreased white blood cell count, intestinal perfor
- said side effects are neutropenia, hemorrhage, dysphonia, brain edema, fall, oxygen saturation decreased, decreased weight, decreased hemoglobin level, decreased hematocrit level, increased monocyte count, increased weight, back pain, decreased platelet count, increased blood creatinine level, increased diastolic blood pressure, general physical health deterioration, nasopharyngitis, proteinuria, increased systolic blood pressure, malaise, seizure, confusional state, decreased heart rate, decreased lymphocyte count, malignant neoplasm progression, decreased mean platelet volume, pyrexia, decreased red blood cell count, urinary tract infection, diarrhea, hypertension, decreased mobility, fatigue, hemiparesis, increased mean cell volume increased, increased neutrophil count, rectal haemorrhage, decreased white blood cell count, intestinal perforation, epistaxis, arthralgia, pain, increased mean cell hemoglobin, death, infusion related reaction, nausea, increased red cell distribution width, deep vein
- said side effects are selected from increased light chain analysis level, balance disorder, cough, joint swelling, paresthesia, acute kidney injury, decreased appetite, sepsis, insomnia, palpitations, decreased weight, dry skin, arthralgia, back pain, chest pain, neutropenia, muscular weakness, neuropathy peripheral, muscle spasms, nausea, deep vein thrombosis, urinary tract infection, increased blood creatinine level, anaemia, atrial fibrillation, decreased platelet count, pancytopenia, pulmonary embolism, malaise, epistaxis, erythema, loss of consciousness, pleural effusion, cerebrovascular accident, Hypoesthesia, pain, thrombocytopenia, decreased hemoglobin level, dizziness, vision blurred, rhinorrhea, confusional state, cardiac failure congestive, Clostridium difficile infection, fall, pruritus, rash maculo-papular, and myocardial infarction.
- said side effects are increased light chain analysis level, balance disorder, cough, joint swelling, paresthesia, acute kidney injury, decreased appetite, sepsis, insomnia, palpitations, decreased weight, dry skin, arthralgia, back pain, chest pain, neutropenia, muscular weakness, neuropathy peripheral, muscle spasms, nausea, deep vein thrombosis, urinary tract infection, increased blood creatinine level, anaemia, atrial fibrillation, decreased platelet count, pancytopenia, pulmonary embolism, malaise, epistaxis, erythema, loss of consciousness, pleural effusion, cerebrovascular accident, Hypoesthesia, pain, thrombocytopenia, decreased hemoglobin level, dizziness, vision blurred, rhinorrhea, confusional state, cardiac failure congestive, Clostridium difficile infection, fall, pruritus, rash maculo-papular, and myocardial infarction.
- abdominal pain acute kidney injury, increased alanine aminotransferase level, anaemia, arthralgia, atrial fibrillation, back pain, balance disorder, increased blood creatinine level, increased diastolic blood pressure, increased systolic blood pressure, brain edema, cardiac failure congestive, cardiovascular related side effect, cerebrovascular accident, chest pain, chills, Clostridium difficile infection, confusional state, cough, death, decreased appetite, deep vein thrombosis, dermatological side effect, diarrhoea, dizziness, dry skin, dysphonia, dyspnea, epistaxis, erythema, fall, fatigue, fever, general physical health deterioration, decreased hematocrit level, decreased hemoglobin level, hemorrhage, harsh, headache, decreased heart rate, hemiparesis, hypertension, hypoesthesia, hypotension, influenza like symptoms, infusion related reaction, insomnia, intestinal perforation, joint pain, joint swelling, increased light
- each of these side effects is represented by a binary feature.
- abdominal pain is defined as a feeling of pain that can be felt anywhere between the chest and groin which is often referred to as the stomach region or belly.
- acute kidney injury refers to an abrupt decrease in kidney function, resulting in the retention of urea and other nitrogenous waste products and in the dysregulation of extracellular volume and electrolytes.
- increased alanine aminotransferase level refers to a level of that enzyme higher than 56 U/L (units per liter).
- the common reference range for alanine aminotransferase blood test is 7 to 56 U/L.
- Alanine aminotransferase is an enzyme that mainly exists in the liver and increased alanine aminotransferase level in the blood may indicate a damage to the liver and/or a liver condition.
- anaemia is defined as a blood disorder in which the blood has a reduced ability to carry oxygen due to a lower than normal number of red blood cells, or a reduction in the amount of hemoglobin.
- arthralgia is defined as discomfort, pain, or inflammation arising from any part of a joint — including cartilage, bone, ligaments, tendons, or muscles.
- Atrial fibrillation is an irregular and often very rapid heart rhythm (arrhythmia) that can lead to blood clots in the heart.
- arrhythmia very rapid heart rhythm
- back pain is defined as discomfort or sometimes debilitating suffering associated with an injury or some other affliction of the back.
- balance disorder is defined as the inability to stay upright and move confidently which occurs when being unable to control body’s position or feeling unsteady.
- increased blood creatinine level relates to level of creatinine in blood exceeding 1.1 mg/dL in women and adolescents aged 16 and older, 1.3 mg/dL in men and adolescent aged 16 and older, and 0.2 mg/dL in infants.
- Creatinine is a chemical compound left over from energy-producing processes in the muscles and increased blood creatinine level signifies impaired kidney function or kidney disease.
- increased diastolic blood pressure is defined as an increase in the pressure in arteries when the heart rests between beats.
- increased systolic blood pressure is defined as an increase in the pressure in arteries when the heart beats.
- brain edema is defined as the excess accumulation of fluid (edema) in the intracellular or extracellular spaces of the brain.
- cardiac failure congestive is defined as a chronic progressive condition that affects the pumping power of your heart muscle and specifically refers to the stage in which fluid builds up within the heart and causes it to pump inefficiently.
- cardiovascular related side effect is defined as a heart related reaction to a drug such as tachycardia or arrhythmia.
- cerebrovascular accident is defined as the sudden death of some brain cells due to lack of oxygen when the blood flow to the brain is impaired by blockage or rupture of an artery to the brain.
- chest pain is defined as the presence the presence of abnormal pain or discomfort in the chest, preferably between the diaphragm and the base of the neck.
- chills are defined as a feeling of being cold without an apparent cause.
- Clostridium difficile infection is an infection with said bacterium that causes an infection of the large intestine (colon).
- confusional state is defined as occurrence of a partial or complete loss of consciousness with interruption of awareness of oneself and one's surroundings.
- cough is defined as a condition of expelling air from the lungs suddenly with a sharp, short noise.
- death is defined as the irreversible cessation of all vital functions especially as indicated by the permanent stoppage of the heart, respiration, and brain activity.
- decreased appetite is defined as follows: a decreased appetite is when the desire to eat is reduced and can further cause weight decrease.
- deep vein thrombosis is defined as a medical condition that occurs when a blood clot forms in a deep vein which usually develops in the lower leg, thigh, or pelvis, but they can also occur in the arm.
- dermatological side effect is defined as follows: dermatological side effect may include skin related reactions to a drug such as itching sensation, swelling and irritation.
- diarrhoea is defined as a condition of having at least three loose, liquid, or watery bowel movements each day.
- dizziness is defined as referring to a sense of disorientation in space, vertigo, or lightheadedness.
- dry skin is defined as a condition associated with the skin not having enough moisture content which is often accompanied by itching and flaking of the skin.
- dysphonia is defined as a voice disorder which causes involuntary spasms in the muscles of the voice box or larynx, which causes the voice to break.
- dyspnea is defined as a difficult, painful breathing or shortness of breath.
- epistaxis is defined as a loss of blood from the tissue that lines the inside of the nose.
- erythema is defined as abnormal redness of the skin or mucous membranes due to capillary congestion (as in inflammation).
- fall is defined as an occurrence of coming down inadvertently, usually under the influence of gravity.
- fatigue is defined as a presence of an overall feeling of tiredness or lack of energy.
- fever is defined as an occurrence of an increase in body temperature, often due to an illness, preferably above 38 °C.
- general physical health deterioration is defined as a change in clinical state to worse clinical state, which increases the individual risk of morbidity.
- decreased hematocrit level is defined as having too few red blood cells in the body to be considered healthy, preferably less than 41% for males, less than 36% for females, and less than 30% for children.
- Hematocrit level is preferably defined as an amount of red blood cells per unit volume of blood.
- Hemoglobin is a protein inside red blood cells that carries oxygen from the lungs to tissues and organs in the body and carries carbon dioxide back to the lungs.
- a decreased hemoglobin level means that the oxygen-carrying capacity of the blood is reduced which may lead to anemia. Accordingly, preferably the decreased hemoglobin level corresponds to a level of less than 14.0 g/dL for males, and less than 12.3 g/dL for females.
- hemorrhage is defined as a loss of blood from a damaged blood vessel.
- harsh also referred to as a harsh side effects
- headache is defined as having pain in any region of the head.
- decreased heart rate refers to a slow resting heart rate, commonly under 60 beats per minute as determined by an electrocardiogram.
- hemiparesis is defined as a weakness or the inability to move on one side of the body, making it hard to perform everyday activities like eating or dressing.
- hypertension is a common condition in which the long-term force of the blood against your artery walls is high enough that it may eventually cause health problems, such as heart disease.
- hypertension is a blood pressure of 140/90 mmHg (millimetre of mercury) or higher (or 150/90 mmHg or higher if the patient is over the age of 80).
- hypoesthesia is defined as a partial or total loss of sensation in a part of the body.
- hypotension is defined as a low blood pressure which can deprive the brain and other vital organs of oxygen and nutrients, leading to a life-threatening condition.
- hypotension is a blood pressure of less than 90/60 mmHg (millimetre of mercury).
- influenza like symptoms is defined as a group of symptoms that are similar to those caused by influenza (flu) virus which may include fever, chills, headache, muscle or body aches, cough, sore throat, runny nose, fatigue, nausea, vomiting, and/or diarrhea.
- influenza (flu) virus may include fever, chills, headache, muscle or body aches, cough, sore throat, runny nose, fatigue, nausea, vomiting, and/or diarrhea.
- infusion related reaction is defined as a disorder characterized by an adverse reaction to the infusion of pharmacological or biological substances which may be resulting from the release of histamine and histamine-like substances from mast cells.
- insomnia is defined as a common sleep disorder that can make it hard to fall asleep, hard to stay asleep, or cause to wake up too early and not be able to get back to sleep.
- intestinal perforation is defined as a loss of continuity of the bowel wall which is a potentially devastating complication that may result from a variety of disease processes.
- joint pain is defined as a discomfort, pain or inflammation arising from any part of a joint — including cartilage, bone, ligaments, tendons or muscles.
- joint swelling is defined as a buildup of fluid in the soft tissue surrounding the joint which may be related to Inflammation of a joint.
- increased light chain analysis level is defined as follows.
- Light chains are proteins made by plasma cells, a type of white blood cell.
- An increased light chain analysis level can be a sign of multiple myeloma or another serious disorder.
- ranges for free light chains of more than 19.4 mg/L (milligrams per liter) kappa free light chains, and/or more than 26.3 mg/L lambda free light chains, and or ratio of kappa free light chains to lambda free light chain of more than 1.65 are considered an increased light chain analysis level.
- local side effects are defined as symptoms occurring upon the local administration of a drug or vaccine which may include pain at the injection site, redness at the injection site, and/or swelling at the injection site.
- loss of consciousness is defined as an occurrence of a partial or complete loss of consciousness with interruption of awareness of oneself and one's surroundings.
- lymphadenopathy refers to the enlargement of one or more lymph nodes.
- decreased lymphocyte count is defined as follows. Lymphocyte is a type of immune cell that is made in the bone marrow and is found in the blood and in lymph tissue. The decreased lymphocyte count may be defined as less than 1 ,000 lymphocytes per microliter of blood.
- malaise as defined herein is a general feeling of discomfort, illness, or lack of well-being.
- malignant neoplasm progression is defined as a cancer progression which occurs when cancer spreads (increases growth speed) or becomes worse.
- increased mean cell hemoglobin is defined as follows.
- Mean cell hemoglobin is a calculated value derived from the measurement of hemoglobin and the red cell count.
- An increased mean cell hemoglobin means that the hemoglobin has an increased oxygen-carrying capacity than normal.
- increased mean cell hemoglobin is defined as more than 33.2 pg.
- increased mean cell volume is defined as follows.
- Mean cell volume is a number that describes the average size of red blood cells circulating in the bloodstream and an increased mean cell volume would mean that the red blood cells are larger than average.
- increased mean cell volume refers to a mean cell volume exceeding 100 fL.
- decreased mean platelet volume is defined as follows.
- Mean platelet volume is a measurement of the average size of the platelets and a decreased mean platelet volume means the platelets are smaller than average.
- decreased mean platelet volume refers to a mean platelet volume of less than 9.4 fL.
- mental side effects refer to group of disorders of cognitive, behavioral, and emotional capabilities.
- decreased mobility is defined as a state in which an individual has a limitation in independent, purposeful physical movement of the body or of one or more extremities.
- increased monocyte count is defined as follows. Monocyte is a type of immune cell that is made in the bone marrow and travels through the blood to tissues in the body where it becomes a macrophage or a dendritic cell. An increased monocyte count is the elevation of monocyte counts in the blood which is often associated with chronic or sub-acute infections or some types of cancer. Preferably, increased monocyte count refers to a monocyte count of more than 800 monocytes per microliter of blood.
- muscle pain is defined as including muscle aches and pain, which can involve ligaments, tendons, and fascia.
- muscle spasms are defined as an involuntary contraction of a muscle that can cause a great deal of pain.
- muscular weakness is defined as a lack of strength in the muscles.
- myalgia is defined as including muscle aches and pain, which can involve ligaments, tendons, and fascia, which may be due to muscle overuse or, in some cases, it can be a symptom of a medical condition.
- myocardial infarction is defined as a deadly medical emergency where the heart muscle begins to die because it is not getting enough blood flow and is usually caused by a blockage in the arteries that supply blood to the heart.
- nasopharyngitis is defined as a swelling of the nasal passages and the back of the throat.
- nausea is a feeling of sickness or discomfort in the stomach that may come with an urge to vomit.
- neuropathy peripheral is defined as a nerve problem that causes pain, numbness, tingling, swelling, or muscle weakness in different parts of the body.
- neutropenia is defined as follows.
- Neutrophil is a type of white blood cell that is an important part of the immune system and helps the body fight infection and neutropenia is a condition in which there is a lower-than-normal number of neutrophils in the blood which might happen due to an infection but can result from cancer treatment.
- neutropenia refers to less than 1500 neutrophiles per microliter of blood.
- neutrophil count increased is defined as follows.
- Neutrophil is a type of white blood cell that is an important part of the immune system and helps the body fight infection and a neutrophil count increased is a condition in which there is a lower- than-normal number of neutrophils in the blood which might happen due to an infection but can result from cancer treatment.
- neutrophil count increased refers to more than 7500 neutrophils per microliter of blood.
- oxygen saturation decreased is defined as lower than normal level of oxygen in the blood, which is usually, an oxygen saturation level of lower than 90%.
- pain is defined as an unpleasant sensation that can range from mild, localized discomfort to agony. Pain has both physical and emotional components.
- palpitations is defined as feeling of having a fast-beating, fluttering or pounding heart. Stress, exercise, medication or, rarely, a medical condition can trigger them.
- pancytopenia is defined as a condition in which there is a lower-than- normal number of red and white blood cells and platelets in the blood.
- pancytopenia refers to an occurrence of at least one of anemia, thrombocytopenia and neutropenia, as defined herein.
- paresthesia is defined as an abnormal touch sensation, such as burning or prickling, that occurs without an outside stimulus.
- decreased platelet count is defined as follows. Platelets (thrombocytes) are colorless blood cells that help blood clot. Decreased platelet count is a condition of having a low blood platelet count which may be a result of a bone marrow disorder such as leukemia or an immune system problem or it can be a side effect of taking certain medications. Preferably, a decreased platelet count refers to a platelet count of less than 150 000 platelets per microliter of blood.
- pleural effusion is defined as an abnormal collection of fluid between the thin layers of tissue (pleura) lining the lung and the wall of the chest cavity.
- proteinuria is defined as an elevated level of protein in the urine and is a symptom of certain conditions that are affecting the kidneys.
- pruritus is defined as a severe itching which may be a side effect of some cancer treatments and a symptom of some types of cancers.
- pulmonary embolism is defined as a sudden blockage of an artery (blood vessel) in the lung which usually occurs when a blood clot in a deep vein in the leg or pelvis breaks loose and travels through the blood to the lungs.
- pyrexia is defined as a complex physiologic response to disease mediated by pyrogenic cytokines and characterized by a rise in core temperature.
- rash maculo-papular is defined a type of rash characterized by a flat, red area on the skin that is covered with small confluent bumps. It may only appear red in lighter-skinned people.
- rectal hemorrhage refers to any free blood that passes from the anus, although rectal bleeding is usually assumed to refer to bleeding from the lower colon or rectum.
- decreased red blood cell count is defined as a blood disorder in which the red blood count is decreased and therefore the blood has a reduced ability to carry oxygen.
- decreased red blood cell count refers to a blood cell count lower than in any of the following ranges: for female adults is 4.2 - 5.4 million cells/pL (million cells per microlitre), for male adults, 4.7 - 6.1 million cells/pL, for children 1 - 18 years at 4.0 - 5.5, for infants 6 - 12 months at 3.5 - 5.2, for infants, 2 - 6 months at 3.5 - 5.5, for infants, 2 - 8 weeks at 4.0 - 6.0, and for newborns at 4.8 - 7.1.
- increased red cell distribution width is defined as follows.
- Red cell distribution width is a measurement of the differences in the volume and size of the red blood cells and it could be an indication of a nutrient deficiency, such as a deficiency of iron, folate, or vitamin B-12.
- increased red cell distribution width refers to a red cell distribution width higher than 16.1 percent in adult females and higher than 14.5 percent in adult males.
- rhinorrhea is defined as a thin, mostly clear nasal discharge. It may occur because of the inflammation of nasal tissues.
- seizure is defined as a sudden, uncontrolled body movements and changes in behavior that occur because of abnormal electrical activity in the brain.
- sepsis is defined as an extreme body response to an infection which is a life-threatening medical emergency and happens when an infection triggers a chain reaction throughout the body.
- speech disorder is defined as a condition in which a person has problems creating or forming the speech sounds needed to communicate with others.
- swelling is defined as the enlargement of organs, skin, or other body parts which is caused by a buildup of fluid in the tissues.
- thrombocytopenia is defined as a condition in which there is a lower-than- normal number of platelets in the blood and may result in easy bruising and excessive bleeding from wounds or bleeding in mucous membranes and other tissues.
- thrombosis is defined as a blood clot that forms on the wall of a blood vessel or in the heart when blood platelets, proteins, and cells stick together and ultimately may block the flow of blood.
- urinary tract infection is a condition in which bacteria invade and grow in the urinary tract (the kidneys, ureters, bladder, and urethra) and mostly occurs in the bladder or urethra.
- vision blurred is defined as a lack of sharpness of vision which, can result to, the inability to see fine detail.
- vomiting is clinically defined as the oral eviction of gastrointestinal contents, due to contractions of the gut and the muscles of the thoracoabdominal wall.
- increased weight is defined as an increase in body weight which can involve an increase in muscle mass, fat deposits, excess fluids such as water or other factors.
- decreased weight is defined as a decrease in body weight which can involve a decrease in muscle mass, fat deposits, lower weights of fluids such as water or other factors.
- decreased white blood cell count is defined as a decrease in white blood cells (leukocytes) in the blood and it can be a symptom of various of underlying conditions.
- the method of the present invention for predicting side effects of a drug or a vaccine in a subject further comprises the step of (a’) training of the previously trained mathematical model.
- the previously trained mathematical model is trained using the database comprising subject-specific data on side effects of a drug or a vaccine in a subject and subjectspecific health and life-style data. It is to be understood that the database referred to herein has been compiled for subjects that were previously vaccinated with a particular vaccine or treated with a particular drug.
- side effects of a drug or a vaccine in a subject is as described hereinabove.
- said side effects are preferably selected from abdominal pain, acute kidney injury, increased alanine aminotransferase level, anaemia, arthralgia, atrial fibrillation, back pain, balance disorder, increased blood creatinine level, increased diastolic blood pressure, increased systolic blood pressure, brain edema, cardiac failure congestive, cardiovascular related side effect, cerebrovascular accident, chest pain, chills, Clostridium difficile infection, confusional state, cough, death, decreased appetite, deep vein thrombosis, dermatological side effect, diarrhoea, dizziness, dry skin, dysphonia, dyspnea, epistaxis, erythema, fall, fatigue, fever, general physical health deterioration, decreased hematocrit level, decreased hemoglobin level, hemorrhage, harsh, headache, decreased heart rate, hemi
- said side effects are abdominal pain, acute kidney injury, increased alanine aminotransferase level, anaemia, arthralgia, atrial fibrillation, back pain, balance disorder, increased blood creatinine level, increased diastolic blood pressure, increased systolic blood pressure, brain edema, cardiac failure congestive, cardiovascular related side effect, cerebrovascular accident, chest pain, chills, Clostridium difficile infection, confusional state, cough, death, decreased appetite, deep vein thrombosis, dermatological side effect, diarrhoea, dizziness, dry skin, dysphonia, dyspnea, epistaxis, erythema, fall, fatigue, fever, general physical health deterioration, decreased hematocrit level, decreased hemoglobin level, hemorrhage, harsh, headache, decreased heart rate, hemiparesis, hypertension, hypoesthesia, hypotension, influenza like symptoms, infusion related reaction, insomnia, intestinal perforation, joint pain, joint swelling, increased
- said side effects are selected from neutropenia, hemorrhage, dysphonia, brain edema, fall, oxygen saturation decreased, decreased weight, decreased hemoglobin level, decreased hematocrit level, increased monocyte count, increased weight, back pain, decreased platelet count, increased blood creatinine level, increased diastolic blood pressure, general physical health deterioration, nasopharyngitis, proteinuria, increased systolic blood pressure, malaise, seizure, confusional state, decreased heart rate, decreased lymphocyte count, malignant neoplasm progression, decreased mean platelet volume, pyrexia, decreased red blood cell count, urinary tract infection, diarrhea, hypertension, decreased mobility, fatigue, hemiparesis, increased mean cell volume increased, increased neutrophil count, rectal haemorrhage, decreased white blood cell count, intestinal perforation, epistaxis, arthralgia, pain, increased mean cell hemoglobin, death, infusion related reaction, nausea, increased red cell distribution width
- said side effects are neutropenia, hemorrhage, dysphonia, brain edema, fall, oxygen saturation decreased, decreased weight, decreased hemoglobin level, decreased hematocrit level, increased monocyte count, increased weight, back pain, decreased platelet count, increased blood creatinine level, increased diastolic blood pressure, general physical health deterioration, nasopharyngitis, proteinuria, increased systolic blood pressure, malaise, seizure, confusional state, decreased heart rate, decreased lymphocyte count, malignant neoplasm progression, decreased mean platelet volume, pyrexia, decreased red blood cell count, urinary tract infection, diarrhea, hypertension, decreased mobility, fatigue, hemiparesis, increased mean cell volume increased, increased neutrophil count, rectal haemorrhage, decreased white blood cell count, intestinal perforation, epistaxis, arthralgia, pain, increased mean cell hemoglobin, death, infusion related reaction, nausea, increased red cell distribution width, deep vein
- said side effects are selected from increased light chain analysis level, balance disorder, cough, joint swelling, paresthesia, acute kidney injury, decreased appetite, sepsis, insomnia, palpitations, decreased weight, dry skin, arthralgia, back pain, chest pain, neutropenia, muscular weakness, neuropathy peripheral, muscle spasms, nausea, deep vein thrombosis, urinary tract infection, increased blood creatinine level, anaemia, atrial fibrillation, decreased platelet count, pancytopenia, pulmonary embolism, malaise, epistaxis, erythema, loss of consciousness, pleural effusion, cerebrovascular accident, Hypoesthesia, pain, thrombocytopenia, decreased hemoglobin level, dizziness, vision blurred, rhinorrhea, confusional state, cardiac failure congestive, Clostridium difficile infection, fall, pruritus, rash maculo-papular, and myocardial infarction.
- said side effects are increased light chain analysis level, balance disorder, cough, joint swelling, paresthesia, acute kidney injury, decreased appetite, sepsis, insomnia, palpitations, decreased weight, dry skin, arthralgia, back pain, chest pain, neutropenia, muscular weakness, neuropathy peripheral, muscle spasms, nausea, deep vein thrombosis, urinary tract infection, increased blood creatinine level, anaemia, atrial fibrillation, decreased platelet count, pancytopenia, pulmonary embolism, malaise, epistaxis, erythema, loss of consciousness, pleural effusion, cerebrovascular accident, Hypoesthesia, pain, thrombocytopenia, decreased hemoglobin level, dizziness, vision blurred, rhinorrhea, confusional state, cardiac failure congestive, Clostridium difficile infection, fall, pruritus, rash maculo-papular, and myocardial infarction.
- the previously trained mathematical model relies on predictors selected from subject-specific health and life-style data, wherein the subject specific health and life-style data are as described herein.
- the selection of predictors from subject-specific health and life-style data can be based on several aspects like availability of the data and significance of the predictor on side effects prediction based on previous scientific and professional understanding of the problem for the specific drug or vaccine. It is noted that preferably, in the method of the present invention, said predictors do not include laboratory results or the results of genetic testing and only include data that can be collected by providing answers by patients or their physicians to a questionnaire.
- a method for predicting side effects of a coronavirus vaccine in a subject comprising:
- subject-specific health and life-style data comprise subject physical parameters, course of the past coronavirus disease, data on medication(s) administered to the subject, medical background, and/or life style data, preferably wherein the subject-specific health and life-style data is collected by using a questionnaire.
- the method of any one of items 2 to 7, wherein the subject-specific health and life-style data further comprise data on side effects of a previous dose of a coronavirus vaccine.
- the method of item 8 wherein the data on side effects of the previous dose of a coronavirus vaccine comprise data on muscle pain, join paint, chills, nausea, headache, fatigue, fever and/or local side effects.
- the method of item 10 wherein the machine learning model is selected from Logistic Regression (LR), Random Forest (RF), Multi-Layer Perceptron (MLP), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Gradient Boosted Decision Trees (XGBoost).
- the method of item 11 wherein the machine learning model is Logistic Regression (LR).
- coronavirus vaccine is selected from DNA vaccine, RNA vaccine, adenovirus vector vaccine, inactivated virus vaccine, and subunit vaccine.
- a method for selecting a subject qualifying for vaccination with a coronavirus vaccine comprising
- the models to predict adverse side effects following the first and second doses of the Sputnik V vaccine and the first dose of AstraZeneca vaccine were created.
- the Side effects as the output data included eight parameters: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and local side effects.
- the prediction model for the first dose was trained using data from 3740 patients receiving the Sputnik V vaccine and 2537 patients receiving the AstraZeneca vaccine.
- the first dose prediction models were trained on age, gender, BMI, lifestyle variables, history and severity of past COVID-19 infection and its symptoms, and medical background of a subject.
- the total number of input parameters for this model was 46 features.
- the second dose prediction for Sputnik V was trained on the data from 2284 people who got both the first and second vaccine dose.
- For the prediction model of the adverse effects following the second dose of Sputnik V vaccine we included data from 2284 patients who had received both doses of the vaccine.
- the second dose prediction of Sputnik V the adverse side effects of the first dose of vaccination were also included as the input data, which made the total number of input parameters for the second prediction model 54 features.
- the prediction model for the second dose of Sputnik V also has access to vaccine receivers’ first dose side effects data.
- the total number of predictors for the first and second dose models is 46 and 54, respectively.
- the selection of variables as predictors was based on the available recorded data survey. All these predictors were recorded via an online form explicitly filled by the healthcare personnel three days or more after their vaccination.
- One-hot encoding is a method of creating binary parameters for and based on categorical parameters, indicating the presence of each possible value from the original data with 1 and the absence with 0. Continuous predictors including age and BMI were normalized using a MinMax scaler. To avoid feeding models with outlier values that may have been entered due to the unintentional mistakes while completing the form, any record that contains outlier values for age ( ⁇ 18 years and >120 years), height ( ⁇ 100 cm and >200 cm), and weight ( ⁇ 30 kg and >200 kg) was excluded.
- LR Logistic Regression
- RF Random Forest
- MLP Multi-Layer Perceptron
- KNN K-Nearest Neighbors
- SVM Support Vector Machine
- XGBoost Gradient Boosted Decision Trees
- Scikit-learn machine learning library was used to implement both preprocessing algorithms and models (Garreta, R., & Moncecchi, G. (2013). Learning scikit-learn: Machine Learning in Python. Packt Publishing) and the xgboost package was used for training Gradient Boosted Decision Trees ( http://doi.acm.org/10.1145/2939672.2939785.).
- Figure 4A The frequency of each side effect in our dataset has been shown in Figure 4A for both the first doses of AstraZeneca and Sputnik V vaccine and the second dose of Sputnik V.
- local side effects in the injection site were the most frequent (65% and 62% for first and second doses of Sputnik V vaccine, respectively and 77% for the first dose of AstraZeneca vaccine), and nausea was the least frequent side effect (10% for both doses of Sputnik V and 21 % for the first dose of AstraZeneca vaccine ).
- Figure 4B, and Figure 4C shows the distribution of some of these features, including sex, smoking, covid-19 prior infection, age, and BML.
- Sputnik V vaccine feature distributions are for 3740 persons that got at least one vaccine dose; the second dose model uses a subset of these data that also got their second dose.
- Astrazeneca vaccine feature distributions are for 2537 persons that got at least one vaccine dose.
- Dose1_Fever refers to the predicted side effects for the first dose of the AstraZeneca vaccine, namely fever, fatigue, headache, nausea, chills, joint pain, muscle pain and local side effects, respectively.
- C is herein understood as inverse of regularization strength and is preferably provided as a positive float number
- solver is an algorithm that performs the regression
- Ibfgs stands for limited-memory Broyden-Fletcher- Goldfarb-Shanno
- liblinear stands for library for large linear classification.
- C is herein understood as penalty parameter of the error term
- gamma is the parameter that defines how far the influence of a single training example reaches
- scale gamma parameter means that [please provide details]
- kernel is defined as function used in SVM for helping to solve problems
- linear kernel means that
- n_estimators relates to the number of decision trees
- min_child_weight relates to a minimum sum of instance weight (hessian) needed in a child, i.e. ich the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, the building process will give up further partitioning
- maxdepth is the maximum depth that the tree is allowed to grow to, in other words the longest path between the root node and the leaf node
- learning_rate parameter controls the weighting of new trees added to the model.
- max_depth defines the longest path between the root node and the leaf node
- min_samples_split defines minimum number of samples required to split an internal node
- n_estimators is the number of trees that are to be built before taking the maximum voting or averages of predictions.
- KNN n_neighbors is the number of the nearest neighbors; and weights defines the function that fives a weight to the nearest points, herein “distance” means that [please provide].
- activation refers to the activation function used in the hidden layers, herein tanh means that [please provide]; hidden_layer_sizes refers to [please provide], herein (100,100) means that [please provide]; learning_rate can be constant or adaptive and it is a parameter that [please provide]; solver is an algorithm that performs the regression, herein used sgd stands for Stochastic Gradient Descent. It is noted that the parameters as defined hereinabove and as used in Table 1 are known to the person skilled in the art.
- the receiver operating characteristic (ROC) curve is commonly used to assess binary classification algorithms' performance.
- the ROC curve is produced by calculating and plotting the true-positive rate against the falsepositive rate for a single classifier at various thresholds.
- the true-positive rate is calculated as the ratio between the number of positive events rightly categorized as positive (true positives) and the total number of actual positive events (regardless of classification, meaning true positives plus false negatives).
- the false-positive rate is calculated as the ratio between the number of negative events wrongly categorized as positive (false positives) and the total number of actual negative events (regardless of classification, meaning false positives plus true negatives).
- the ROC provides a graphical depiction of a classifier's performance rather than a single value like other metrics such as accuracy.
- AUC stands for the area under the (ROC) curve. Generally, the higher the AUC score, the better a classifier performs for the given task.
- the ROC-AUC curve helps us visualize how well our machine learning classifier is performing.
- the ROC-AUC curve for each method used has been calculated and reported for training, validation, and test groups.
- the criteria for best model is to have both the prediction strength and generalizability to unseen data, the model should not be overfitted on training data and should have almost equal performance on validation and training and test set.
- logistic regression has the best total performance and less overfitting to training data.
- Logistic regression due to its simplicity also has the advantage of powerful explainability compared to other models that are too complex to clearly explain their decision-making process such as multi-layer perceptron and Ensembl random decision tree.
- the training procedure for the second dose is similar to the first dose except here models have access to the first dose side effect data. As expected, these new features helped the models to have a better prediction for second dose side effects (Figure 7). Except for KNN that showed poor performance on the validation sets, other models showed an average ROC-AUC equal to 0.823.
- the logistic regression has been selected as the best model.
- Logistic regression model coefficient was used to demonstrate each predictor variable's effect on each side effect's outcome.
- Logistic regression calculates a probability P for in each input data X with following formula: where is the coefficient for feature i.
- Avastin (bevacizumab) is a biological therapeutic product approved to be used to treat a certain type of brain tumor, and certain types of cancers of the kidney, liver, lung, colon, rectum, cervix, ovary, or fallopian tube.
- This recombinant humanized monoclonal antibody blocks angiogenesis by inhibiting vascular endothelial growth factor A (VEGF-A).
- VEGF-A vascular endothelial growth factor A
- Avastin (bevacizumab) works differently than chemotherapy.
- the objective of Avastin is to prevent the growth of new blood vessels. This includes normal blood vessels and blood vessels that feed tumors. Avastin was first approved in February 2004 and is manufactured by Genentech, Inc.
- Revlimid (lenalidomide) is considered a chemotherapy drug with immunomodulatory, and antiangiogenic impacts. Revlimid can be used to treat multiple myeloma (bone marrow cancer), either in combination with additional medicines or after a stem cell transplant. Revlimid can also be used for other disorders such as myelodysplastic syndrome (MDS) or Mantle Cell Lymphoma (MCL). Revlimid is manufactured by Celgene Corp.
- the Side effects as the output data for our Avastin prediction model included 55 parameters: neutropenia, hemorrhage, dysphonia, brain edema, fall, oxygen saturation decreased, decreased weight, decreased hemoglobin level, decreased hematocrit level, increased monocyte count, increased weight, back pain, decreased platelet count, increased blood creatinine level, increased diastolic blood pressure, general physical health deterioration, nasopharyngitis, proteinuria, increased systolic blood pressure, malaise, seizure, confusional state, decreased heart rate, decreased lymphocyte count, malignant neoplasm progression, decreased mean platelet volume, pyrexia, decreased red blood cell count, urinary tract infection, diarrhea, hypertension, decreased mobility, fatigue, hemiparesis, increased mean cell volume increased, increased neutrophil count, rectal haemorrhage, decreased white blood cell count, intestinal perforation, epistaxis, arthralgia, pain, increased mean cell hemoglobin, death, infusion related
- the Side effects as the output data for our Revlimid model included 48 parameters: increased light chain analysis level, balance disorder, cough, joint swelling, paresthesia, acute kidney injury, decreased appetite, sepsis, insomnia, palpitations, decreased weight, dry skin, arthralgia, back pain, chest pain, neutropenia, muscular weakness, neuropathy peripheral, muscle spasms, nausea, deep vein thrombosis, urinary tract infection, increased blood creatinine level, anaemia, atrial fibrillation, decreased platelet count, pancytopenia, pulmonary embolism, malaise, epistaxis, erythema, loss of consciousness, pleural effusion, cerebrovascular accident, Hypoesthesia, pain, thrombocytopenia, decreased hemoglobin level, dizziness, vision blurred, rhinorrhea, confusional state, cardiac failure congestive, Clostridium difficile infection, fall, pruritus, rash maculo-papular, and myocardial infarction.
- the prediction models for the Avastin were trained using data from 1639 patients receiving the Avastin drug.
- the prediction models for the Revlimid were trained using data from 3034 patients receiving the Revlimid drug.
- Predictors The patients' age, sex, weight, height, and past medical history (PMH), including the existence of current indication that the drug has been prescribed for, and other comorbidities and use of specific drugs were used as the predictors for the models of Avastin and Revlimid drugs.
- PMH past medical history
- One-hot encoding is a method of creating binary parameters for and based on categorical parameters, indicating the presence of each possible value from the original data with 1 and the absence with 0. Continuous predictors including age, weight, and height were normalized using a MinMax scaler. To avoid feeding models with outlier values that may have been entered due to the unintentional mistakes, any record that contains outlier values for age ( ⁇ 18 years and >120 years), height ( ⁇ 100 cm and >200 cm), and weight ( ⁇ 30 kg and >200 kg) was excluded.
- a 5-fold cross-validation algorithm was performed. For this purpose, all of the records were split into five subsets at random. Four subsets were used as training data, and one subset was held back for model testing as a validation set. The cross-validation process was repeated four times more, with each of the five subsets being used exactly once as the validation data. Afterward, model performance metrics were calculated for the validation groups and finally were averaged.
- the receiver operating characteristic (ROC) curve is commonly used to assess binary classification algorithms' performance.
- the ROC curve is produced by calculating and plotting the true-positive rate against the falsepositive rate for a single classifier at various thresholds.
- the true-positive rate is calculated as the ratio between the number of positive events rightly categorized as positive (true positives) and the total number of actual positive events (regardless of classification, meaning true positives plus false negatives).
- the false-positive rate is calculated as the ratio between the number of negative events wrongly categorized as positive (false positives) and the total number of actual negative events (regardless of classification, meaning false positives plus true negatives).
- the ROC provides a graphical depiction of a classifier's performance rather than a single value like other metrics such as accuracy.
- AUC stands for the area under the (ROC) curve. Generally, the higher the AUC score, the better a classifier performs for the given task.
- the ROC-AUC curve helps us visualize how well our machine learning classifier is performing.
- Table 5A and 5B the ROC-AUC curve for the Random Forest method used has been calculated and reported for the Avastin and Revlimid respectively. By comparing models’ performance for various side effects it has been shown that the model can predict distinct side effects with various ROC-AUC results.
- Prevnar 13 (pneumococcal 13-valent vaccine) is a vaccine to help prevent disease caused by pneumococcal bacteria. This vaccine covers 13 different types of pneumococcal bacteria. Prevnar 13 works by exposing you to a slim amount of the bacteria or a protein from the bacteria, which drives the body’s immune system to develop immunity to the disease. Prevnar 13 vaccine can be used in adults and children of at least 6 weeks old. Prevnar 13 is manufactured by Pfizer.
- Fluarix Quadrivalent is an inactivated influenza virus vaccine for the prevention of influenza disease.
- the vaccine is redesigned yearly to contain specific strains of inactivated (killed) flu virus that are instructed by public health officials for that specific year.
- Fluarix Quadrivalent works by exposing a person to a small dose of the virus, which would help the body’s immune system to develop immunity.
- Fluarix Quadrivalent has been approved for use in adults and children who are at least 6 months old. Fluarix Quadrivalent is manufactured by GlaxoSmithKline pic.
- Shingrix is a vaccine used against and for preventing herpes zoster.
- Shingrix is approved to be used to prevent herpes zoster virus (shingles) in people 50 years of age and older, including people who formerly had received a live zoster vaccine (Zostavax); and in people 18 years and older at increased risk of herpes zoster virus (shingles) because of immunodeficiency or immunosuppression.
- Shingrix is manufactured by GlaxoSmithKline pic.
- the Side effects as the output data for our Prevnar 13 prediction model included 24 parameters: mental (including depression, excess stress, and crying), joint pain, nausea, headache, paresthesia, pain, chills, myalgia, lymphadenopathy, swelling, mobility decreased, vomiting, Harsh (i.e. harsh reactions including seizure, and anaphylactic shock), local side effects, diarrhea, cardiovascular related, dizziness, fatigue, fever, abdominal pain, muscle pain, insomnia, dermatological side effect, and dyspnea.
- the Side effects as the output data for our Fluarix Quadrivalent prediction model included 21 parameters: mental (including depression, excess stress, and crying), muscle pain, joint pain, influenza like symptoms, pain, fever, chills, harsh ( harsh reactions including seizure, and anaphylactic shock), cardiovascular related side effect, lymphadenopathy, mobility decreased, paresthesia, vomiting, dermatological, local side effects, dyspnea, dizziness, insomnia, myalgia, headache, and fatigue.
- the Side effects as the output data for our Shingrix prediction model included 21 parameters: abdominal pain, vomiting, pain, local side effects, mobility decreased, chills, headache, fever, paresthesia, cardiovascular related side effect, dizziness, joint pain, muscle pain, myalgia, dermatological side effect, diarrhea, dyspnea, nausea, lymphadenopathy, swelling, fatigue.
- the prediction models for the Prevnar 13 were trained using data from 3990 patients receiving the Prevnar 13 vaccine.
- the prediction models for the Fluarix Quadrivalent were trained using data from 3981 patients receiving the Fluarix Quadrivalent vaccine.
- the prediction models for the Shingrix were trained using data from 29608 patients receiving the Fluarix vaccine.
- VAERS Vaccine Adverse Event Reporting System
- One-hot encoding is a method of creating binary parameters for and based on categorical parameters, indicating the presence of each possible value from the original data with 1 and the absence with 0. Continuous predictors including age were normalized using a MinMax scaler. To avoid feeding models with outlier values that may have been entered due to the unintentional mistakes while completing the form, any record that contains outlier values for age ( ⁇ 18 years and >120 years), height ( ⁇ 100 cm and >200 cm), and weight ( ⁇ 30 kg and >200 kg) was excluded.
- a 5-fold cross-validation algorithm was performed. For this purpose, all of the records were split into five subsets at random. Four subsets were used as training data, and one subset was held back for model testing as a validation set. The cross-validation process was repeated four times more, with each of the five subsets being used exactly once as the validation data. Afterward, model performance metrics were calculated for the validation groups and finally were averaged.
- the Random Forest (RF) machine learning technique for the three vaccines was evaluated.
- Scikit-learn machine learning library was used to implement both preprocessing algorithms and models (Garreta, R., & Moncecchi, G. (2013). Learning scikit-learn: Machine Learning in Python. Packt Publishing). The overall efficiency of prediction models was calculated using ROC-AUC.
- the Local side effect was the most frequent (2901 occurrences meaning 72.71% of the total study population), and the Cardiovascular related side effect was the least frequent side effect (66 occurrences meaning 1 .65% of the total study population ).
- the receiver operating characteristic (ROC) curve is commonly used to assess binary classification algorithms' performance.
- the ROC curve is produced by calculating and plotting the true-positive rate against the falsepositive rate for a single classifier at various thresholds.
- the true-positive rate is calculated as the ratio between the number of positive events rightly categorized as positive (true positives) and the total number of actual positive events (regardless of classification, meaning true positives plus false negatives).
- the false-positive rate is calculated as the ratio between the number of negative events wrongly categorized as positive (false positives) and the total number of actual negative events (regardless of classification, meaning false positives plus true negatives).
- the ROC provides a graphical depiction of a classifier's performance rather than a single value like other metrics such as accuracy.
- AUC stands for the area under the (ROC) curve. Generally, the higher the AUG score, the better a classifier performs for the given task.
- the ROC-AUC curve helps us visualize how well our machine learning classifier is performing.
- Table 7A, 7B, and 7C the ROC-AUC curve for the Random Forest method used has been calculated and reported. By comparing models’ performance for various side effects it has been shown that the model can predict distinct side effects with various ROC-AUC results.
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Abstract
La présente invention concerne un procédé de prédiction des effets secondaires d'un médicament ou d'un vaccin, un procédé de prédiction des effets secondaires d'un vaccin à coronavirus et un procédé de sélection d'un sujet apte pour une vaccination avec un vaccin à coronavirus. Les procédés instantanés sont particulièrement utiles, le vaccin à coronavirus étant un vaccin contre le SARS-CoV2
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