CN111696667A - Common gynecological disease prediction model construction method and prediction system - Google Patents
Common gynecological disease prediction model construction method and prediction system Download PDFInfo
- Publication number
- CN111696667A CN111696667A CN202010532135.8A CN202010532135A CN111696667A CN 111696667 A CN111696667 A CN 111696667A CN 202010532135 A CN202010532135 A CN 202010532135A CN 111696667 A CN111696667 A CN 111696667A
- Authority
- CN
- China
- Prior art keywords
- probability
- examination
- gynecological disease
- text
- prediction model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 201000010099 disease Diseases 0.000 title claims abstract description 80
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 title claims abstract description 80
- 238000010276 construction Methods 0.000 title claims abstract description 8
- 208000024891 symptom Diseases 0.000 claims abstract description 28
- 238000010801 machine learning Methods 0.000 claims abstract description 5
- 238000000034 method Methods 0.000 claims description 23
- 238000007689 inspection Methods 0.000 claims description 11
- 239000000523 sample Substances 0.000 claims description 11
- 230000008569 process Effects 0.000 claims description 9
- 230000002159 abnormal effect Effects 0.000 claims description 8
- 230000011218 segmentation Effects 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000005065 mining Methods 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 238000012706 support-vector machine Methods 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 3
- 238000002372 labelling Methods 0.000 claims description 3
- 239000013610 patient sample Substances 0.000 claims description 3
- 230000004927 fusion Effects 0.000 claims 1
- 230000002265 prevention Effects 0.000 abstract description 2
- 241000224526 Trichomonas Species 0.000 description 6
- 244000052616 bacterial pathogen Species 0.000 description 5
- 210000004027 cell Anatomy 0.000 description 5
- 238000003745 diagnosis Methods 0.000 description 5
- 206010046914 Vaginal infection Diseases 0.000 description 4
- 201000008100 Vaginitis Diseases 0.000 description 4
- 150000001412 amines Chemical class 0.000 description 4
- 238000012216 screening Methods 0.000 description 4
- 241000894006 Bacteria Species 0.000 description 3
- 208000004926 Bacterial Vaginosis Diseases 0.000 description 3
- 206010008342 Cervix carcinoma Diseases 0.000 description 3
- KWYUFKZDYYNOTN-UHFFFAOYSA-M Potassium hydroxide Chemical compound [OH-].[K+] KWYUFKZDYYNOTN-UHFFFAOYSA-M 0.000 description 3
- 208000006105 Uterine Cervical Neoplasms Diseases 0.000 description 3
- 201000011510 cancer Diseases 0.000 description 3
- 201000010881 cervical cancer Diseases 0.000 description 3
- 230000003749 cleanliness Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 210000002919 epithelial cell Anatomy 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 241000233866 Fungi Species 0.000 description 2
- 208000007074 Trichomonas Vaginitis Diseases 0.000 description 2
- 241000224527 Trichomonas vaginalis Species 0.000 description 2
- 208000025206 Trichomonas vaginitis urogenital infection Diseases 0.000 description 2
- 206010046798 Uterine leiomyoma Diseases 0.000 description 2
- 208000037009 Vaginitis bacterial Diseases 0.000 description 2
- 206010008323 cervicitis Diseases 0.000 description 2
- 201000003988 chronic cervicitis Diseases 0.000 description 2
- 208000015181 infectious disease Diseases 0.000 description 2
- 201000010260 leiomyoma Diseases 0.000 description 2
- 206010003693 Atrophic vulvovaginitis Diseases 0.000 description 1
- 206010057687 Bloody discharge Diseases 0.000 description 1
- 208000004145 Endometritis Diseases 0.000 description 1
- 201000001342 Fallopian tube cancer Diseases 0.000 description 1
- 208000013452 Fallopian tube neoplasm Diseases 0.000 description 1
- 206010028980 Neoplasm Diseases 0.000 description 1
- 238000003556 assay Methods 0.000 description 1
- 235000013527 bean curd Nutrition 0.000 description 1
- 210000004369 blood Anatomy 0.000 description 1
- 239000008280 blood Substances 0.000 description 1
- 201000003505 cervical polyp Diseases 0.000 description 1
- 238000002591 computed tomography Methods 0.000 description 1
- 238000011109 contamination Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 229940011871 estrogen Drugs 0.000 description 1
- 239000000262 estrogen Substances 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000003902 lesion Effects 0.000 description 1
- 210000000265 leukocyte Anatomy 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000000813 microbial effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000017074 necrotic cell death Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000000624 ovulatory effect Effects 0.000 description 1
- 230000001575 pathological effect Effects 0.000 description 1
- 201000010808 postmenopausal atrophic vaginitis Diseases 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 238000002604 ultrasonography Methods 0.000 description 1
- 206010046766 uterine cancer Diseases 0.000 description 1
- 208000010579 uterine corpus leiomyoma Diseases 0.000 description 1
- 201000007954 uterine fibroid Diseases 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- 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
- G16H50/20—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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Medical Informatics (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Data Mining & Analysis (AREA)
- Biomedical Technology (AREA)
- Epidemiology (AREA)
- Pathology (AREA)
- Primary Health Care (AREA)
- Artificial Intelligence (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Computational Linguistics (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
The invention relates to a common gynecological disease prediction model construction method and a prediction system, wherein the prediction model construction method comprises the following steps: acquiring examination items, examination indexes, gynecological disease knowledge and medical record samples required by common gynecological diseases; deducing the probability of index data of each examination item and the overall similarity probability of each examination item according to the medical record sample; establishing a database containing the matching of gynecological disease knowledge, the probability of examination item index data and the clinical symptom probability of a patient; and extracting text features of the database, and calculating and constructing a prediction model through text similarity. The invention constructs a common gynecological disease prediction model and system with high accuracy by using a machine learning method based on a database constructed by multiple data sources, can be used for basic doctor learning and reference, is convenient for early prediction and prevention of patients, and has certain popularization and application values.
Description
Technical Field
The invention relates to the technical field of intelligent medical treatment and medical information, in particular to a common gynecological disease prediction model construction method and a prediction system.
Background
According to the survey of the World Health Organization (WHO), more than 96 percent of married women in China have gynecological diseases with different degrees, and the incidence rate of the common gynecological diseases is more than 87.6 percent. The incidence of cervical cancer in women accounts for about 1/3 worldwide. In real life, many women lack sufficient understanding and appreciation of their own body and gynecological diseases.
At present, the image technologies such as ultrasound, magnetic resonance, computed tomography, colposcope, hysteroscope and the like, and the technologies such as liquid-based cytology, gene detection, leucorrhea analysis, cervical section and the like are important means for screening, diagnosing, staging, evaluating curative effect, treating and follow-up visiting gynecological diseases, particularly malignant tumors of the gynecological diseases. These tests basically depend on various devices and conditions and the personal clinical experience of doctors, are easy to cause misdiagnosis and missed diagnosis, and have long screening, diagnosing and qualifying time. With the increasing workload and the imminent shortage of doctors, the self-examination, evaluation and prediction of common gynecological diseases need a simpler, faster and intelligent substitute which is not limited by various subjective and objective conditions, so that the system of the technology and the method, which is limited by the subjective and objective conditions, has longer diagnosis qualitative time and is inaccurate, causes the treatment work to be in a passive situation is changed.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a common gynecological disease prediction model construction method, which is used for obtaining examination items, examination indexes, gynecological disease knowledge and medical record samples required by common gynecological diseases; deducing the probability of index data of each examination item and the overall similarity probability of each examination item according to the medical record sample; establishing a database containing the matching of gynecological disease knowledge, the probability of examination item index data and the clinical symptom probability of a patient; and extracting text features of the database, and calculating and constructing a prediction model through text similarity.
In some embodiments of the present invention, in order to improve the accuracy of the data, the process of acquiring examination items, examination indexes, knowledge of gynecological diseases, and medical record samples required by common gynecological diseases further includes preprocessing the data. Further, the preprocessing comprises extracting a normal reference interval value and an abnormal reference interval value of the numerical value, and performing feature keyword extraction and normalization processing on the text description.
In some embodiments of the present invention, the process of inferring the probability of each inspection item indicator data according to the medical record sample specifically includes: the overall average index and the percentage of similarity of clinical symptom characteristics of the gynecological disease examination and analysis are calculated by each gynecological patient sample.
In some embodiments of the present invention, the process of extracting text features from the database is performed by a text mining method and machine learning.
In some embodiments of the invention, the text mining method comprises Adaboos or a support vector machine.
The invention also provides a common gynecological disease prediction system, which comprises an acquisition module, a database module and a database module, wherein the acquisition module is used for acquiring examination items, examination indexes, gynecological disease knowledge and medical record samples required by common gynecological diseases; the matching module is used for deducing the probability of index data of each inspection item and the overall similarity probability of each inspection item according to the medical record sample; establishing a database containing the matching of gynecological disease knowledge, the probability of examination item index data and the clinical symptom probability of a patient; the calculation module is used for extracting text features of the database and calculating and constructing a prediction model through text similarity; and the determining module is used for selecting and matching the clinical symptoms of the target patient and predicting the disease probability of the target patient.
In some embodiments of the invention, the acquisition module periodically updates the acquired data in order to improve the effectiveness and real-time of the data and the accuracy of the predictions.
In some embodiments of the invention, the matching module infers the overall likelihood probability of each inspection item from a bayesian model or a markov model. The determination module performs weight labeling on the clinical symptom characteristic word frequency of the patient, the text characteristic of the clinical symptom of the patient and the text characteristic of the gynecological disease information through a weighting algorithm to obtain labeled data, and fuses the labeled data to form the clinical symptom text characteristic word segmentation of the patient and the text characteristic word segmentation of the gynecological disease information.
The invention can realize the rapid screening and intelligent diagnosis of the common gynecological diseases by acquiring the possible probability through weighted calculation based on the basic standards of common gynecological disease samples and examination items and the knowledge required by the gynecological diseases and combining the clinical symptom characteristics for selection and matching, thereby solving various practical problems encountered in the actual life and work of women.
Drawings
FIG. 1 is a basic flow chart of the method for constructing the prediction model of common gynecological diseases.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Aiming at the technical problems in the prior art, the invention provides a common gynecological disease prediction model construction method, which is used for obtaining examination items, examination indexes, gynecological disease knowledge and medical record samples required by common gynecological diseases; deducing the probability of index data of each examination item and the overall similarity probability of each examination item according to the medical record sample; establishing a database containing the matching of gynecological disease knowledge, the probability of examination item index data and the clinical symptom probability of a patient; and extracting text features of the database, and calculating and constructing a prediction model through text similarity. For example, the various items required for detecting common gynecological diseases include B-ultrasonic examination indexes, leucorrhea analysis indexes, hysteroscopy indexes, clinical symptom indexes, specific gynecological disease concepts, knowledge and names, and the like.
In some embodiments of the present invention, in order to improve the accuracy of the data, the process of acquiring examination items, examination indexes, knowledge of gynecological diseases, and medical record samples required by common gynecological diseases further includes preprocessing the data. Further, the preprocessing comprises extracting a normal reference interval value and an abnormal reference interval value of the numerical value, and performing feature keyword extraction and normalization processing on the text description.
Specifically, if the acquired data is numerical values, extracting the normal range reference interval values and the abnormal range reference interval values of the element data (according to the diagnostic standard and the determined standard of international and domestic related index science, the relatively uniform normal range reference interval values and abnormal range reference interval values are included); if the text description is the text description, extracting characteristic keywords or phrases in the text, and performing normalization processing; for example, the routine examination of leucorrhea for gynecological diseases generally determines whether the female has abnormal leucorrhea by 5 examinations such as vaginal PH, vaginal cleanliness, vaginal microbial examination, etc. The method specifically comprises the following steps: 1. the pH value. The pH value is usually used for expressing the pH value during assay, the pH value is 4.5 in normal times, and the pH value of the leucorrhea is increased to be more than 5-6 when the trichomonas vaginitis or bacterial vaginitis is suffered; 2. vaginal cleanliness can be classified as class 4: degree I: a large number of vaginal epithelial cells and a large number of vaginal bacilli are seen under a microscope; II degree: vaginal epithelial cells, a small amount of white blood cells, a part of vaginal bacilli and a small amount of mixed bacteria or pus cells are found under the lens; and (3) III degree: a small amount of vaginal bacilli and a large amount of pus cells and mixed bacteria are found under the lens; IV degree: no vaginal bacilli are seen under the mirror, except a small amount of epithelial cells, pus cells and mixed bacteria are mainly seen. Wherein: i to II are normal. III-IV are abnormal and may be vaginitis, pathogenic bacteria, fungi, trichomonas vaginalis and the like can be found frequently, and the trichomonas vaginalis and the fungi are checked simultaneously when the cleanliness is checked; 3. after the mold and the trichomonas leucorrhea are treated, the existence of trichomonas or mold can be found under a microscope according to the form of the trichomonas or mold, for example, the existence of the trichomonas or mold is represented by "+" no matter how many the trichomonas or mold exists, and the symbol of "+" only indicates that the woman is infected with the trichomonas or mold and does not indicate the severity of the infection; 4. amine test: the leucorrhea with bacterial vaginosis can generate fishy smell, which is caused by volatilization of amine existing in the leucorrhea after the amine is alkalized by potassium hydroxide; 5. clue cells: the bacterial vaginosis can be diagnosed according to the positive amine test and the cabled cell.
In some embodiments of the present invention, the range reference interval values of the examination numerical indicators and the text description segmentation or phrase may correspond to diseases and causes, including the possible corresponding diseases and causes and the diseases and causes of different people, different age stages, and different indicators (states) abnormal changes, are collected and processed, and the data and knowledge are preprocessed. Another example, 1, purulent leucorrhea: the leucorrhea is yellow or yellow-green, like pus, with odor. Generally caused by infection, commonly seen in trichomonas vaginitis, chronic cervicitis, vaginitis, endometritis and the like; 2. colorless transparent mucous leucorrhea: the appearance is similar to the normal leucorrhea in the ovulatory period, and the dosage is large, and the common use is after the estrogen drugs are applied; 3. leucorrhea with bloody discharge: leucorrhea with blood contamination is alert to malignant tumors such as cervical cancer and cancer of uterine body. Benign lesions such as cervical polyp, severe chronic cervicitis, intrauterine device, senile vaginitis, submucosal uterine fibroid and the like can also have the symptoms; 4. bean curd residue-like leucorrhea: is characteristic of mycotic vaginitis; 5. yellow watery leucorrhea: mostly caused by necrosis of pathological tissues, the cancer is commonly seen in cervical cancer, submucosal uterine fibroids, fallopian tube cancer and the like; 6. pus sample leucorrhea: is characteristic of amoebic vaginitis.
In some embodiments of the present invention, the process of inferring the probability of each inspection item indicator data according to the medical record sample specifically includes: the overall average index and the percentage of similarity of clinical symptom characteristics of the gynecological disease examination and analysis are calculated by each gynecological patient sample. Further, a gynecological disease rapid diagnosis standard which is composed of the overall average index probability of average examination and analysis, the clinical symptom characteristic percentage of the patient and the specific gynecological disease name is established, and a gynecological disease rapid prediction database is established.
In some embodiments of the present invention, the process of extracting text features from the database is performed by a text mining method and machine learning. Specifically, the machine learning is performed by a Support vector machine (SV machine): vectorizing the acquired examination items, examination indexes, gynecological disease knowledge and medical record samples required by common gynecological diseases, and then mapping to a high-dimensional space, wherein a maximum interval plane is established in the high-dimensional space, and two parallel hyperplanes are established on two sides of the hyperplane for separating data. And the distance between the two parallel hyperplanes is maximized through continuous calculation and iteration of the model, so that the text features are extracted.
In some embodiments of the invention, the text mining method comprises Adaboos or a support vector machine. Specifically, according to symptoms, examination items and prediction examination indexes in a knowledge base, a Bayesian model or a Markov model is used for establishing forward and reverse classification different classifiers, and the models are verified through medical record samples; and further adaptively correcting the predicted weight of each classifier under different sample conditions according to the verification result, and performing multiple iterations on the process to form a gynecological disease prediction model by probability fitting of the classifiers in the model and the medical record samples. Optionally, the feedback of the target patient is incorporated into the model optimization.
The invention also provides a common gynecological disease prediction system, which comprises an acquisition module, a database module and a database module, wherein the acquisition module is used for acquiring examination items, examination indexes, gynecological disease knowledge and medical record samples required by common gynecological diseases;
the matching module is used for deducing the probability of index data of each inspection item and the overall similarity probability of each inspection item according to the medical record sample; establishing a database containing the matching of gynecological disease knowledge, the probability of examination item index data and the clinical symptom probability of a patient; the calculation module is used for extracting text features of the database and calculating and constructing a prediction model through text similarity; and the determining module is used for selecting and matching the clinical symptoms of the target patient and predicting the disease probability of the target patient.
In some embodiments of the invention, the acquisition module periodically updates the acquired data in order to improve the effectiveness and real-time of the data and the accuracy of the predictions.
In some embodiments of the invention, the matching module infers the overall likelihood probability of each inspection item from a bayesian model or a markov model. The determination module performs weight labeling on the clinical symptom characteristic word frequency of the patient, the text characteristic of the clinical symptom of the patient and the text characteristic of the gynecological disease information through a weighting algorithm to obtain labeled data, and fuses the labeled data to form the clinical symptom text characteristic word segmentation of the patient and the text characteristic word segmentation of the gynecological disease information.
In some embodiments of the invention, the invention obtains possible probabilities through weighting calculation based on common gynecological disease samples, basic standards of examination items and knowledge required by gynecological diseases, and then selects and matches the probability in combination with clinical symptom characteristics, so that the rapid screening and intelligent diagnosis of the common gynecological diseases can be realized, various practical problems encountered in the actual life and work of women can be solved, and the invention can also be used for the prevention, prediction, learning and reference of gynecological diseases of primary-level and first-line medical care personnel or community doctors.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A common gynecological disease prediction model construction method is characterized by comprising the following steps:
acquiring examination items, examination indexes, gynecological disease knowledge and medical record samples required by common gynecological diseases;
deducing the probability of index data of each examination item and the overall similarity probability of each examination item according to the medical record sample; establishing a database containing the matching of gynecological disease knowledge, the probability of examination item index data and the clinical symptom probability of a patient;
and extracting text features of the database, and calculating and constructing a prediction model through text similarity.
2. The method as claimed in claim 1, wherein the step of obtaining the examination items, examination indexes, gynecological disease knowledge and medical record samples required for gynecological diseases further comprises preprocessing the above data.
3. The method for constructing a model for predicting common gynecological diseases according to claim 2, wherein the preprocessing comprises extracting a normal reference interval value and an abnormal normal reference interval value of the values,
and extracting the feature key words of the text description and carrying out normalization processing.
4. The method for constructing a common gynecological disease prediction model according to claim 1 or 2, wherein the probability process for deducing index data of each examination item according to a medical record sample specifically comprises:
the overall average index and the percentage of similarity of clinical symptom characteristics of the gynecological disease examination and analysis are calculated by each gynecological patient sample.
5. The method for constructing a common gynecological disease prediction model according to claim 1 or 2, wherein the process of text feature extraction of the database is performed by a text mining method and machine learning.
6. The method for constructing a prediction model of common gynecological diseases according to claim 1, wherein the text mining method comprises Adaboos or a support vector machine.
7. A system for predicting common gynecological diseases, comprising:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring examination items, examination indexes, gynecological disease knowledge and medical record samples required by common gynecological diseases;
the matching module is used for deducing the probability of index data of each inspection item and the overall similarity probability of each inspection item according to the medical record sample; establishing a database containing the matching of gynecological disease knowledge, the probability of examination item index data and the clinical symptom probability of a patient;
the calculation module is used for extracting text features of the database and calculating and constructing a prediction model through text similarity;
and the determining module is used for selecting and matching the clinical symptoms of the target patient and predicting the disease probability of the target patient.
8. The system of claim 7, wherein the acquisition module updates the acquired data periodically.
9. The system according to claim 7 or 8, wherein the matching module deduces the overall similarity probability of each inspection item according to Bayesian model or Markov model.
10. The system of claim 8, wherein the determining module performs weighting labeling on the word frequency of the clinical symptom feature of the patient, the text feature of the clinical symptom of the patient and the text feature of the gynecological disease information through a weighting algorithm to obtain labeled data, and performs fusion on the labeled data to form the text feature segmentation of the clinical symptom of the patient and the text feature segmentation of the gynecological disease information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010532135.8A CN111696667A (en) | 2020-06-11 | 2020-06-11 | Common gynecological disease prediction model construction method and prediction system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010532135.8A CN111696667A (en) | 2020-06-11 | 2020-06-11 | Common gynecological disease prediction model construction method and prediction system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111696667A true CN111696667A (en) | 2020-09-22 |
Family
ID=72480521
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010532135.8A Pending CN111696667A (en) | 2020-06-11 | 2020-06-11 | Common gynecological disease prediction model construction method and prediction system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111696667A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112201357A (en) * | 2020-11-19 | 2021-01-08 | 吾征智能技术(北京)有限公司 | Disease cognitive system based on female hormone examination information |
| CN112259245A (en) * | 2020-10-21 | 2021-01-22 | 平安科技（深圳）有限公司 | Method, device and equipment for determining items to be checked and computer readable storage medium |
| CN113257409A (en) * | 2021-06-04 | 2021-08-13 | 杭州云呼医疗科技有限公司 | Clinical decision support system based on patient disease symptoms and medical examination reports |
| CN116665889A (en) * | 2023-07-28 | 2023-08-29 | 长春中医药大学 | Intelligent auxiliary diagnosis and treatment system applied to gynecological outpatient service |
| CN117116497A (en) * | 2023-10-16 | 2023-11-24 | 长春中医药大学 | Clinical care management system for gynecological diseases |
| CN117936012A (en) * | 2024-03-21 | 2024-04-26 | 四川省医学科学院·四川省人民医院 | Inspection project decision method, medium and system based on chronic pain |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105184103A (en) * | 2015-10-15 | 2015-12-23 | 清华大学深圳研究生院 | Virtual medical expert based on medical record database |
| CN106682412A (en) * | 2016-12-22 | 2017-05-17 | 浙江大学 | Diabetes prediction method based on medical examination data |
| CN106951719A (en) * | 2017-04-10 | 2017-07-14 | 荣科科技股份有限公司 | The construction method and constructing system of clinical diagnosis model, clinical diagnosing system |
| EP3306617A1 (en) * | 2016-10-06 | 2018-04-11 | Fujitsu Limited | Method and apparatus of context-based patient similarity |
| CN108257673A (en) * | 2018-01-12 | 2018-07-06 | 南通大学 | Risk value Forecasting Methodology and electronic equipment |
| CN108305690A (en) * | 2018-02-07 | 2018-07-20 | 王博冉 | The nervous system disease point examines the system for distribution of out-patient department of method and the nervous system disease |
| CN108565019A (en) * | 2018-04-13 | 2018-09-21 | 合肥工业大学 | Multidisciplinary applicable clinical examination combined recommendation method and device |
| US20180322954A1 (en) * | 2017-05-08 | 2018-11-08 | Hefei University Of Technology | Method and device for constructing medical knowledge graph and assistant diagnosis method |
| CN109036553A (en) * | 2018-08-01 | 2018-12-18 | 北京理工大学 | A kind of disease forecasting method based on automatic extraction Medical Technologist's knowledge |
| CN110175246A (en) * | 2019-04-09 | 2019-08-27 | 山东科技大学 | A method of extracting notional word from video caption |
| CN111180026A (en) * | 2019-12-23 | 2020-05-19 | 卫宁健康科技集团股份有限公司 | Special diagnosis and treatment view system and method |
| CN111261282A (en) * | 2020-01-21 | 2020-06-09 | 南京航空航天大学 | Sepsis early prediction method based on machine learning |
-
2020
- 2020-06-11 CN CN202010532135.8A patent/CN111696667A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105184103A (en) * | 2015-10-15 | 2015-12-23 | 清华大学深圳研究生院 | Virtual medical expert based on medical record database |
| EP3306617A1 (en) * | 2016-10-06 | 2018-04-11 | Fujitsu Limited | Method and apparatus of context-based patient similarity |
| CN106682412A (en) * | 2016-12-22 | 2017-05-17 | 浙江大学 | Diabetes prediction method based on medical examination data |
| CN106951719A (en) * | 2017-04-10 | 2017-07-14 | 荣科科技股份有限公司 | The construction method and constructing system of clinical diagnosis model, clinical diagnosing system |
| US20180322954A1 (en) * | 2017-05-08 | 2018-11-08 | Hefei University Of Technology | Method and device for constructing medical knowledge graph and assistant diagnosis method |
| CN108257673A (en) * | 2018-01-12 | 2018-07-06 | 南通大学 | Risk value Forecasting Methodology and electronic equipment |
| CN108305690A (en) * | 2018-02-07 | 2018-07-20 | 王博冉 | The nervous system disease point examines the system for distribution of out-patient department of method and the nervous system disease |
| CN108565019A (en) * | 2018-04-13 | 2018-09-21 | 合肥工业大学 | Multidisciplinary applicable clinical examination combined recommendation method and device |
| CN109036553A (en) * | 2018-08-01 | 2018-12-18 | 北京理工大学 | A kind of disease forecasting method based on automatic extraction Medical Technologist's knowledge |
| CN110175246A (en) * | 2019-04-09 | 2019-08-27 | 山东科技大学 | A method of extracting notional word from video caption |
| CN111180026A (en) * | 2019-12-23 | 2020-05-19 | 卫宁健康科技集团股份有限公司 | Special diagnosis and treatment view system and method |
| CN111261282A (en) * | 2020-01-21 | 2020-06-09 | 南京航空航天大学 | Sepsis early prediction method based on machine learning |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112259245A (en) * | 2020-10-21 | 2021-01-22 | 平安科技（深圳）有限公司 | Method, device and equipment for determining items to be checked and computer readable storage medium |
| CN112259245B (en) * | 2020-10-21 | 2023-07-25 | 平安科技（深圳）有限公司 | Method, device, equipment and computer readable storage medium for determining items to be checked |
| CN112201357A (en) * | 2020-11-19 | 2021-01-08 | 吾征智能技术(北京)有限公司 | Disease cognitive system based on female hormone examination information |
| CN113257409A (en) * | 2021-06-04 | 2021-08-13 | 杭州云呼医疗科技有限公司 | Clinical decision support system based on patient disease symptoms and medical examination reports |
| CN116665889A (en) * | 2023-07-28 | 2023-08-29 | 长春中医药大学 | Intelligent auxiliary diagnosis and treatment system applied to gynecological outpatient service |
| CN117116497A (en) * | 2023-10-16 | 2023-11-24 | 长春中医药大学 | Clinical care management system for gynecological diseases |
| CN117116497B (en) * | 2023-10-16 | 2024-01-12 | 长春中医药大学 | Clinical care management system for gynecological diseases |
| CN117936012A (en) * | 2024-03-21 | 2024-04-26 | 四川省医学科学院·四川省人民医院 | Inspection project decision method, medium and system based on chronic pain |
| CN117936012B (en) * | 2024-03-21 | 2024-05-17 | 四川省医学科学院·四川省人民医院 | Inspection project decision method, medium and system based on chronic pain |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111696667A (en) | Common gynecological disease prediction model construction method and prediction system | |
| Kumawat et al. | Prognosis of cervical cancer disease by applying machine learning techniques | |
| CN111353539A (en) | Cervical OCT image classification method and system based on double-path attention convolutional neural network | |
| Madhumitha et al. | Automated polycystic ovarian syndrome identification with follicle recognition | |
| CN110991536A (en) | Training method of early warning model of primary liver cancer | |
| Ferris et al. | Colposcopic accuracy in a residency training program: defining competency and proficiency | |
| CN114582496A (en) | Common gynecological disease prediction model construction method and prediction system | |
| CN113128654A (en) | Improved random forest model for coronary heart disease pre-diagnosis and pre-diagnosis system thereof | |
| Ananth et al. | An Advanced Low-cost Blood Cancer Detection System. | |
| CN113393938B (en) | Breast cancer risk prediction system fusing image and clinical characteristic information | |
| CN112017772B (en) | Method and system for constructing disease cognitive model based on female leucorrhea | |
| CN113674839A (en) | Combined detection system for noninvasive imaging screening and minimally invasive sampling nucleic acid typing | |
| Silman et al. | Breast cancer identification based on artificial intelligent system | |
| Ye et al. | Applications of vision transformers in retinal imaging: A systematic review | |
| CN113255718B (en) | Cervical cell auxiliary diagnosis method based on deep learning cascade network method | |
| Drokow et al. | Building a predictive model to assist in the diagnosis of cervical cancer | |
| RU2819094C1 (en) | Male reproductive function test method | |
| Narayan et al. | Cervical Cancer Detection Based on Novel Decision Tree Approach. | |
| Gowri et al. | An improved classification of MR images for cervical cancer using convolutional neural networks | |
| AU2021102832A4 (en) | System & method for automatic health prediction using fuzzy based machine learning | |
| Vijayalakshmi et al. | Diagnosis of infertility from adenomyosis and endometriosis through entroxon based intelligent water drop back propagation neural networks | |
| Meenakshisundaram et al. | A Novel and Effective method for Early Identification of Cervical Cancer based on Gradient Boosting Classifier | |
| Wang et al. | Segmentation of Thyroid Nodules Based on Hyperspectral Images | |
| Lee et al. | The Automated Diagnosis Architecture and Deep Learning Algorithm for Cervical Cancer Cell Images | |
| Zaylaa et al. | Early Diagnosis of Osteoporosis: An Artificial Intelligence-Based Framework |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200922 |