CN117497181B - Chronic disease information management system based on artificial intelligence - Google Patents
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Abstract
The invention relates to the field of chronic disease information management, and particularly discloses an artificial intelligence-based chronic disease information management system.
Description
Technical Field
The invention relates to the field of chronic disease information management, in particular to a chronic disease information management system based on artificial intelligence.
Background
The chronic obstructive pulmonary disease is chronic bronchitis or emphysema with airflow obstruction characteristics, is a chronic airway hyperreactivity and inflammatory disease, and is a common respiratory disease.
By carrying out statistical analysis and mining on the information of the slow-release pulmonary patient, doctors and managers are helped to know the disease development trend of the slow-release pulmonary patient, and then the effect of the treatment scheme of the slow-release pulmonary patient can be evaluated, so that corresponding decision support is provided for the optimization of the treatment scheme of the slow-release pulmonary patient.
The existing information management method for the patients with chronic obstructive pulmonary disease has some defects: on the one hand, when the severity of the chronic obstructive pulmonary disease is evaluated, the existing method takes the inspection result of one or more items of the chronic obstructive pulmonary disease as the basis, lacks the integrity, does not comprehensively consider all inspection information of the chronic obstructive pulmonary disease, such as medical images, clinical symptoms, lung function inspection, blood qi inspection, blood routine inspection and the like, so that the reliability of the evaluation result of the severity of the chronic obstructive pulmonary disease is insufficient, the accuracy and the referenceability of all inspection information are different, and if the referenceability of the inspection information serving as the basis of the severity of the chronic obstructive pulmonary disease is insufficient, the diagnosis result of the chronic obstructive pulmonary disease is greatly error, and the treatment and the rehabilitation of the chronic obstructive pulmonary disease are easily misjudged, and can be influenced in serious cases.
On the one hand, when judging whether the various pieces of examination information of the patient with the slow lung resistance are abnormal or not, the conventional method compares the various pieces of examination information of the patient with the slow lung resistance with a set fixed value, and does not consider the differences among individuals of the patient with the slow lung resistance, for example, the reference standards of the examination information of the patient with the slow lung resistance in different age groups of different sexes are different, so that the analysis result of the examination information of the patient with the slow lung resistance is insufficient in accuracy.
On the other hand, when the existing method is used for analyzing the disease development trend of the patient with the slow-release lung, the influence of the external environment on the disease of the patient with the slow-release lung is not considered, for example, the probability of respiratory tract infection of the patient with the slow-release lung is increased due to the large change of the air temperature and the air humidity, so that the occurrence of the slow-release lung of the bronchus is stimulated, namely, when the disease development trend of the patient with the slow-release lung at a certain stage is inconsistent with the expected trend, the treatment scheme is not necessarily poor and the influence of the external environment is possibly caused, so that the reliability of the result of the existing method for analyzing the disease development trend of the patient with the slow-release lung is insufficient, and accurate and reliable reference comments cannot be provided for optimizing the treatment scheme of the patient with the slow-release lung.
Disclosure of Invention
Aiming at the problems, the invention provides a chronic disease information management system based on artificial intelligence, which realizes the function of chronic disease information management.
The technical scheme adopted for solving the technical problems is as follows: the invention provides a chronic disease information management system based on artificial intelligence, comprising: the patient medical image information analysis module: the method is used for acquiring medical image information of each patient with slow lung resistance in a recuperation zone of a target hospital in each sampling time period in the monitoring period, recording the medical image information as medical image information of each patient in each sampling time period in the monitoring period, and analyzing the disease severity coefficient of each patient based on medical images in each sampling time period in the monitoring period.
Patient clinical symptom information analysis module: the method is used for acquiring clinical symptom information of each patient in each sampling time period in the monitoring period and analyzing the disease severity coefficient of each patient based on clinical symptoms in each sampling time period in the monitoring period.
Patient lung function examination information analysis module: the system is used for acquiring the lung function examination information of each patient in each sampling time period in the monitoring period and analyzing the disease severity coefficient of each patient based on the lung function examination in each sampling time period in the monitoring period.
A blood gas examination information analysis module of the patient: the method is used for acquiring blood gas examination information of each patient in each sampling time period in the monitoring period and analyzing the disease severity coefficient of each patient based on blood gas examination in each sampling time period in the monitoring period.
Patient blood routine examination information analysis module: the method is used for acquiring the blood routine examination information of each patient in each sampling time period in the monitoring period and analyzing the disease severity coefficient of each patient based on the blood routine examination in each sampling time period in the monitoring period.
Patient condition development trend analysis module: the system is used for analyzing the disease development trend type of each patient according to the disease severity coefficient of each patient based on medical images, clinical symptoms, lung function examination, blood gas examination and blood routine examination in each sampling time period in the monitoring period and feeding back the disease development trend type of each patient.
Database: the system is used for storing medical record information of slow-resistance lung patients in a recuperation area of a target hospital.
On the basis of the above embodiment, the specific analysis process of the patient medical image information analysis module includes: and setting the duration of the monitoring period, and dividing the monitoring period according to a preset equal time length principle to obtain each sampling time period in the monitoring period.
Medical image information of each patient in each sampling time period in a monitoring period is obtained, the bronchus outline of each patient in each sampling time period in the monitoring period is obtained, medical record information of the target hospital recuperation area slow-resistance lung patient stored in a database is extracted, sex and age of each patient are obtained, the sex and age of each patient are compared with the preset reference bronchus outline corresponding to each age of each sex, and the reference bronchus outline of each patient is obtained through screening.
Comparing the bronchus profile of each patient in each sampling period with the corresponding reference bronchus profile to obtain the coincidence degree of the bronchus profile of each patient in each sampling period and the corresponding reference bronchus profile, and marking the coincidence degree as，/>Indicate->Number of the individual sampling periods, +.>，/>Indicate->The number of the individual patient is set,。
by analysis of formulasObtaining the bronchus deformation coefficient of each patient in each sampling period in the monitoring period>Wherein->Representing a preset bronchus contour coincidence threshold.
On the basis of the above embodiment, the specific analysis process of the patient medical image information analysis module further includes: according to the medical image information of each patient in each sampling time period in the monitoring period, the area of each mucus area of each bronchus of each patient in each sampling time period in the monitoring period is obtained and is recorded as，/>Indicate->The number of the mucus region is determined,。
dividing the bronchus region according to a preset principle to obtain each subregion of bronchus, acquiring the positions of mucus regions of each patient bronchus in each sampling time period in the monitoring period, comparing the positions with the influence factors of each subregion of bronchus in the preset period, screening to obtain the influence factors of the mucus regions of each patient bronchus in each sampling time period in the monitoring period, and marking the influence factors as。
By analysis of formulasObtaining the bronchus mucus accumulation coefficient of each patient in each sampling period in the monitoring period>Wherein->The corresponding influence factors of the preset bronchus unit mucus areas are represented.
And arranging each detection point on the bronchus of each patient according to a preset principle, and obtaining the thickness of the bronchus wall and the diameter of the bronchus inner cavity of each detection point on the bronchus of each patient in each sampling period according to the medical image information of each patient in each sampling period in the monitoring period.
Obtaining the reference ratio of the bronchial wall thickness to the bronchial inner cavity diameter of each patient, and recording the reference ratio as。
By analysis of formulasObtaining the bronchial wall thickening coefficient of each patient in each sampling period in the monitoring period>Wherein->Respectively represent the +.>Sample time period->The patient is at first->The bronchial wall thickness and bronchial lumen diameter of each detection point,。
on the basis of the above embodiment, the specific analysis process of the patient medical image information analysis module further includes: by analysis of formulasObtaining the disease severity coefficient of each patient based on the medical image in each sampling period in the monitoring period>Wherein->Respectively representing the preset threshold values of the bronchus deformation coefficient, the bronchus mucus accumulation coefficient and the bronchus wall thickening coefficient,respectively represent preset bronchiA deformation coefficient, a bronchial mucus accumulation coefficient, and a bronchial wall thickening coefficient.
Based on the above embodiment, the specific analysis process of the patient clinical symptom information analysis module is as follows: acquiring clinical symptom information of each patient in each sampling time period in the monitoring period, obtaining respiratory rate, pulse and sputum excretion of each patient in each sampling time period in the monitoring period, and respectively marking the respiratory rate, pulse and sputum excretion asThe sputum excretion color of each patient in each sampling time period in the monitoring period is obtained, compared with the influence factors corresponding to the preset sputum excretion color, and the influence factors corresponding to the sputum excretion color of each patient in each sampling time period in the monitoring period are obtained through screening and expressed as->。
The reference respiratory frequency and the reference pulse of each patient are acquired and respectively recorded as。
By analysis of formulasObtaining the disease severity coefficient of each patient based on clinical symptoms in each sampling period in the monitoring period>Wherein->Representing natural constant->Indicating the influence factor corresponding to the preset unit sputum excretion amount, +.>Weights respectively representing preset respiratory rate, pulse and sputum excretion.
Above mentioned real worldOn the basis of the embodiment, the specific analysis process of the lung function examination information analysis module of the patient is as follows: acquiring lung function examination information of each patient in each sampling time period in a monitoring period, obtaining one second forced expiration volume, forced vital capacity and expiration peak flow rate of each patient in each sampling time period in the monitoring period, and respectively recording the obtained information as。
Obtaining the reference ratio of one second forced expiration volume to forced vital capacity of each patient, and recording the reference ratio as。
Comparing the gender and age of each patient with the expected value of the expiratory peak flow velocity corresponding to each age of each preset gender, screening to obtain the expected value of the expiratory peak flow velocity of each patient, and recording the expected value as。
By analysis of formulasObtaining the disease severity coefficient of each patient based on the lung function examination in each sampling period>Wherein->Representing a correction factor of a preset disease severity coefficient based on a lung function examination.
Based on the above embodiment, the specific analysis process of the blood gas examination information analysis module of the patient is: obtaining blood gas examination information of each patient in each sampling time period in the monitoring period, obtaining alveolar oxygen partial pressure and arterial blood carbon dioxide partial pressure of each patient in each sampling time period in the monitoring period, and respectively marking the alveolar oxygen partial pressure and the arterial blood carbon dioxide partial pressure as。
Obtaining the reference alveolar oxygen partial pressure and the reference arterial blood carbon dioxide partial pressure of each patient, and respectively marking the partial pressures as。
By analysis of formulasObtaining the disease severity coefficient of each patient based on blood gas examination in each sampling period>Wherein->Respectively representing preset weights of alveolar oxygen partial pressure and arterial blood carbon dioxide partial pressure.
Based on the above embodiment, the specific analysis process of the patient blood routine examination information analysis module is as follows: obtaining blood routine examination information of each patient in each sampling time period in the monitoring period, obtaining eosinophil percentage and neutrophil percentage of each patient in each sampling time period in the monitoring period, and respectively marking the eosinophil percentage and the neutrophil percentage as。
Reference values of eosinophil percentage and neutrophil percentage of each patient were obtained and respectively recorded as。
By analysis of formulasObtaining the disease severity coefficient of each patient based on blood routine examination in each sampling period>。
Based on the above embodiment, the patient condition trend analysis module specifically analyzesThe process comprises the following steps: acquiring climate information of each sampling time period in the monitoring period, obtaining air temperature variation and air humidity variation of each sampling time period in the monitoring period, and respectively marking the air temperature variation and the air humidity variation asBy analysis of the formulaObtaining the climate influence factor +.>Wherein->The influence factors of the preset unit air temperature change amount and the preset unit air humidity change amount are respectively shown.
By analysis of formulasObtaining an evaluation index of the severity of the illness of each patient in each sampling period in the monitoring period>Wherein->And a correction amount indicating a preset disease severity evaluation index.
Based on the above embodiment, the specific analysis process of the patient disease development trend analysis module further includes: and drawing a disease development trend curve of each patient according to the disease severity evaluation index of each patient in each sampling time period in the monitoring period.
Comparing the disease development trend curve of each patient with the disease development trend curve corresponding to the preset various disease development trend types to obtain the similarity of the disease development trend curve of each patient and the disease development trend curve corresponding to the preset various disease development trend types, taking the disease development trend type corresponding to the maximum similarity as the disease development trend type of the patient, counting to obtain the disease development trend type of each patient, and feeding back to the medical team of the target hospital.
Compared with the prior art, the chronic disease information management system based on artificial intelligence has the following beneficial effects: 1. according to the invention, the medical image information, the clinical symptom information, the lung function examination information, the blood gas examination information and the blood routine examination information of the patient with the slow lung resistance are acquired, so that the disease severity of the patient with the slow lung resistance is comprehensively estimated, the reliability and the accuracy of an estimation result are further improved, misjudgment is avoided, and an effective diagnosis opinion of the disease condition of the patient with the slow lung resistance is provided, thereby being beneficial to the treatment and the rehabilitation of the patient with the slow lung resistance.
2. When judging whether the examination information of the patient with the slow lung resistance is abnormal or not, the invention selects the adaptive reference standard as an analysis basis according to the gender and age of the patient with the slow lung resistance, thereby improving the accuracy of the analysis result of the examination information of the patient with the slow lung resistance.
3. According to the invention, the analysis result of the disease severity evaluation index of the slow-blocking lung patient is corrected by acquiring the air temperature change and the air humidity change of the external environment, the disease development trend curve based on the disease severity evaluation index is further optimized, and the influence of the external environment on the disease of the slow-blocking lung patient is considered when the disease development trend of the slow-blocking lung patient is evaluated, so that the rigor and the credibility of the disease development trend evaluation result of the slow-blocking lung patient are improved, and an accurate and reliable reference opinion is provided for optimizing the treatment scheme of the slow-blocking lung patient.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a diagram illustrating a system module connection according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides an artificial intelligence-based chronic disease information management system, which comprises a patient medical image information analysis module, a patient clinical symptom information analysis module, a patient lung function examination information analysis module, a patient blood gas examination information analysis module, a patient blood routine examination information analysis module, a patient disease development trend analysis module and a database.
The patient condition development trend analysis module is respectively connected with the patient medical image information analysis module, the patient clinical symptom information analysis module, the patient lung function examination information analysis module, the patient blood gas examination information analysis module and the patient blood routine examination information analysis module, and the database is respectively connected with the patient medical image information analysis module, the patient clinical symptom information analysis module, the patient lung function examination information analysis module, the patient blood gas examination information analysis module and the patient blood routine examination information analysis module.
The patient medical image information analysis module is used for acquiring medical image information of each patient with slow lung resistance in a recuperation zone of a target hospital in each sampling time period in the monitoring period, recording the medical image information as medical image information of each patient in each sampling time period in the monitoring period, and analyzing the disease severity coefficient of each patient based on medical images in each sampling time period in the monitoring period.
Further, the specific analysis process of the patient medical image information analysis module comprises the following steps: and setting the duration of the monitoring period, and dividing the monitoring period according to a preset equal time length principle to obtain each sampling time period in the monitoring period.
Medical image information of each patient in each sampling time period in a monitoring period is obtained, the bronchus outline of each patient in each sampling time period in the monitoring period is obtained, medical record information of the target hospital recuperation area slow-resistance lung patient stored in a database is extracted, sex and age of each patient are obtained, the sex and age of each patient are compared with the preset reference bronchus outline corresponding to each age of each sex, and the reference bronchus outline of each patient is obtained through screening.
Comparing the bronchus profile of each patient in each sampling period with the corresponding reference bronchus profile to obtain the coincidence degree of the bronchus profile of each patient in each sampling period and the corresponding reference bronchus profile, and marking the coincidence degree as，/>Indicate->Number of the individual sampling periods, +.>，/>Indicate->The number of the individual patient is set,。
by analysis of formulasObtaining the bronchus deformation coefficient of each patient in each sampling period in the monitoring period>Wherein->Representing a preset bronchus contour coincidence threshold.
As a preferred option, the medical images of a patient with slow pulmonary resistance in the nursing area of the target hospital include chest X-rays, CT scans, magnetic resonance imaging, and pulmonary function tests.
When judging whether the examination information of the patient with the slow lung resistance is abnormal, the invention selects the adaptive reference standard as the analysis basis according to the gender and age of the patient with the slow lung resistance, thereby improving the accuracy of the analysis result of the examination information of the patient with the slow lung resistance.
Further, the specific analysis process of the patient medical image information analysis module further comprises: according to the medical image information of each patient in each sampling time period in the monitoring period, the area of each mucus area of each bronchus of each patient in each sampling time period in the monitoring period is obtained and is recorded as，/>Indicate->Numbering of the mucus areas>。
Dividing the bronchus region according to a preset principle to obtain each subregion of bronchus, acquiring the positions of mucus regions of each patient bronchus in each sampling time period in the monitoring period, comparing the positions with the influence factors of each subregion of bronchus in the preset period, screening to obtain the influence factors of the mucus regions of each patient bronchus in each sampling time period in the monitoring period, and marking the influence factors as。
By analysis of formulasObtaining the bronchus mucus accumulation coefficient of each patient in each sampling period in the monitoring period>Wherein->The corresponding influence factors of the preset bronchus unit mucus areas are represented.
And arranging each detection point on the bronchus of each patient according to a preset principle, and obtaining the thickness of the bronchus wall and the diameter of the bronchus inner cavity of each detection point on the bronchus of each patient in each sampling period according to the medical image information of each patient in each sampling period in the monitoring period.
Obtaining the reference ratio of the bronchial wall thickness to the bronchial inner cavity diameter of each patient, and recording the reference ratio as。
As a preferred scheme, a reference ratio of the bronchial wall thickness to the bronchial lumen diameter of each patient is obtained, and the specific method is as follows: and comparing the gender and age of each patient with the preset reference ratio of the bronchial wall thickness and the bronchial inner cavity diameter corresponding to each age of each gender, and screening to obtain the reference ratio of the bronchial wall thickness and the bronchial inner cavity diameter of each patient.
By analysis of formulasObtaining the bronchial wall thickening coefficient of each patient in each sampling period in the monitoring period>Wherein->Respectively represent the +.>Sample time period->The patient is at first->The bronchial wall thickness and bronchial lumen diameter of each detection point,。
further, the specific analysis process of the patient medical image information analysis module further comprises: by analysis of formulasObtaining the disease severity coefficient of each patient based on the medical image in each sampling period in the monitoring period>Wherein->Respectively representing the preset threshold values of the bronchus deformation coefficient, the bronchus mucus accumulation coefficient and the bronchus wall thickening coefficient,respectively representing the preset weight factors of the bronchus deformation coefficient, the bronchus mucus accumulation coefficient and the bronchus wall thickening coefficient.
The patient clinical symptom information analysis module is used for acquiring clinical symptom information of each patient in each sampling time period in the monitoring period and analyzing the disease severity coefficient of each patient based on clinical symptoms in each sampling time period in the monitoring period.
Further, the specific analysis process of the patient clinical symptom information analysis module is as follows: acquiring clinical symptom information of each patient in each sampling time period in the monitoring period, obtaining respiratory rate, pulse and sputum excretion of each patient in each sampling time period in the monitoring period, and respectively marking the respiratory rate, pulse and sputum excretion asThe sputum excretion color of each patient in each sampling time period in the monitoring period is obtained, compared with the influence factor corresponding to the preset sputum excretion color, and the sputum excretion color of each patient in each sampling time period in the monitoring period is obtained through screeningThe influence factor of the color correspondence and expressed as +.>。
The reference respiratory frequency and the reference pulse of each patient are acquired and respectively recorded as。
As a preferred scheme, the reference respiratory rate and the reference pulse of each patient are acquired by the following specific methods: the gender and age of each patient are respectively compared with the preset reference respiratory frequency and reference pulse corresponding to each age group of each gender, and the reference respiratory frequency and the reference pulse of each patient are obtained through screening.
By analysis of formulasObtaining the disease severity coefficient of each patient based on clinical symptoms in each sampling period in the monitoring period>Wherein->Representing natural constant->Indicating the influence factor corresponding to the preset unit sputum excretion amount, +.>Weights respectively representing preset respiratory rate, pulse and sputum excretion.
As a preferable scheme, the respiratory rate, pulse and sputum excretion of the patient in the sampling time period in the monitoring period are obtained, the respiratory rate, pulse and sputum excretion of the patient can be measured for a plurality of times in the sampling time period in the monitoring period, and the average value of the plurality of times of measurement is taken as the respiratory rate, pulse and sputum excretion of the patient in the sampling time period in the monitoring period.
The patient lung function examination information analysis module is used for acquiring lung function examination information of each patient in each sampling time period in the monitoring period and analyzing the disease severity coefficient of each patient based on lung function examination in each sampling time period in the monitoring period.
Further, the specific analysis process of the patient lung function examination information analysis module is as follows: acquiring lung function examination information of each patient in each sampling time period in a monitoring period, obtaining one second forced expiration volume, forced vital capacity and expiration peak flow rate of each patient in each sampling time period in the monitoring period, and respectively recording the obtained information as。
Obtaining the reference ratio of one second forced expiration volume to forced vital capacity of each patient, and recording the reference ratio as。
As a preferred scheme, a reference ratio of one second forced expiration volume to forced vital capacity of each patient is obtained by the following specific method: and comparing the sex and age of each patient with a preset reference ratio of one-second forced expiration volume to forced vital capacity corresponding to each age of each sex, and screening to obtain the reference ratio of one-second forced expiration volume to forced vital capacity of each patient.
Comparing the gender and age of each patient with the expected value of the expiratory peak flow velocity corresponding to each age of each preset gender, screening to obtain the expected value of the expiratory peak flow velocity of each patient, and recording the expected value as。
By analysis of formulasObtaining the disease severity coefficient of each patient based on the lung function examination in each sampling period>Wherein->Representing a correction factor of a preset disease severity coefficient based on a lung function examination.
The blood gas examination information analysis module of the patient is used for obtaining blood gas examination information of each patient in each sampling time period in the monitoring period and analyzing the disease severity coefficient of each patient based on blood gas examination in each sampling time period in the monitoring period.
Further, the specific analysis process of the blood gas examination information analysis module of the patient is as follows: obtaining blood gas examination information of each patient in each sampling time period in the monitoring period, obtaining alveolar oxygen partial pressure and arterial blood carbon dioxide partial pressure of each patient in each sampling time period in the monitoring period, and respectively marking the alveolar oxygen partial pressure and the arterial blood carbon dioxide partial pressure as。
Obtaining the reference alveolar oxygen partial pressure and the reference arterial blood carbon dioxide partial pressure of each patient, and respectively marking the partial pressures as。
As a preferred embodiment, the reference alveolar oxygen partial pressure and the reference arterial blood carbon dioxide partial pressure of each patient are obtained by the following steps: and respectively comparing the gender and age of each patient with the preset reference alveolar oxygen partial pressure and the reference arterial blood carbon dioxide partial pressure corresponding to each age range of each gender, and screening to obtain the reference alveolar oxygen partial pressure and the reference arterial blood carbon dioxide partial pressure of each patient.
By analysis of formulasObtaining the disease severity coefficient of each patient based on blood gas examination in each sampling period>Wherein->Respectively representing preset weights of alveolar oxygen partial pressure and arterial blood carbon dioxide partial pressure.
The patient blood routine examination information analysis module is used for acquiring blood routine examination information of each patient in each sampling time period in the monitoring period and analyzing the disease severity coefficient of each patient based on blood routine examination in each sampling time period in the monitoring period.
Further, the specific analysis process of the patient blood routine examination information analysis module is as follows: obtaining blood routine examination information of each patient in each sampling time period in the monitoring period, obtaining eosinophil percentage and neutrophil percentage of each patient in each sampling time period in the monitoring period, and respectively marking the eosinophil percentage and the neutrophil percentage as。
Reference values of eosinophil percentage and neutrophil percentage of each patient were obtained and respectively recorded as。
As a preferred embodiment, the reference values of eosinophil percentage and neutrophil percentage of each patient are obtained by: and (3) respectively comparing the gender and age of each patient with preset reference values of eosinophil percentage and neutrophil percentage corresponding to each age range of each gender, and screening to obtain the reference values of eosinophil percentage and neutrophil percentage of each patient.
By analysis of formulasObtaining the disease severity coefficient of each patient based on blood routine examination in each sampling period>。
The patient condition development trend analysis module is used for analyzing the condition development trend type of each patient according to the condition severity coefficient of each patient based on medical images, clinical symptoms, lung function examination, blood and qi examination and blood routine examination in each sampling time period in the monitoring period, and feeding back the condition development trend type of each patient.
Further, the specific analysis process of the patient disease development trend analysis module comprises the following steps: acquiring climate information of each sampling time period in the monitoring period, obtaining air temperature variation and air humidity variation of each sampling time period in the monitoring period, and respectively marking the air temperature variation and the air humidity variation asBy analysis formula->Obtaining the climate influence factor +.>Wherein->The influence factors of the preset unit air temperature change amount and the preset unit air humidity change amount are respectively shown.
By analysis of formulasObtaining an evaluation index of the severity of the illness of each patient in each sampling period in the monitoring period>Wherein->And a correction amount indicating a preset disease severity evaluation index.
Further, the specific analysis process of the patient disease development trend analysis module further comprises: and drawing a disease development trend curve of each patient according to the disease severity evaluation index of each patient in each sampling time period in the monitoring period.
As a preferable scheme, the disease development trend curve of each patient is drawn, and the specific method is as follows: establishing a coordinate system by taking the sampling time period as an independent variable and the disease severity evaluation index as a dependent variable, marking corresponding data points in the coordinate system according to the disease severity evaluation index of each patient in each sampling time period in a monitoring period, and drawing a disease development trend curve of each patient by using an establishing method of a mathematical model.
Comparing the disease development trend curve of each patient with the disease development trend curve corresponding to the preset various disease development trend types to obtain the similarity of the disease development trend curve of each patient and the disease development trend curve corresponding to the preset various disease development trend types, taking the disease development trend type corresponding to the maximum similarity as the disease development trend type of the patient, counting to obtain the disease development trend type of each patient, and feeding back to the medical team of the target hospital.
By acquiring the medical image information, the clinical symptom information, the lung function examination information, the blood and qi examination information and the blood routine examination information of the patient with the slow lung resistance, the invention comprehensively evaluates the severity of the patient with the slow lung resistance, further improves the reliability and the accuracy of the evaluation result, avoids misjudgment, and provides the effective diagnosis opinion of the patient with the slow lung resistance, thereby being beneficial to the treatment and the rehabilitation of the patient with the slow lung resistance.
It should be noted that, the invention corrects the analysis result of the disease severity evaluation index of the patient with chronic obstructive pulmonary disease by obtaining the air temperature change and the air humidity change of the external environment, further optimizes the disease development trend curve based on the disease severity evaluation index, considers the influence of the external environment on the disease of the patient with chronic obstructive pulmonary disease when evaluating the disease development trend of the patient with chronic obstructive pulmonary disease, and further improves the rigor and the credibility of the disease development trend evaluation result of the patient with chronic obstructive pulmonary disease, thereby providing accurate and reliable reference opinion for optimizing the treatment scheme of the patient with chronic obstructive pulmonary disease.
The database is used for storing medical record information of slow-blocking lung patients in a recuperation area of a target hospital.
The foregoing is merely illustrative and explanatory of the principles of this invention, as various modifications and additions may be made to the specific embodiments described, or similar arrangements may be substituted by those skilled in the art, without departing from the principles of this invention or beyond the scope of this invention as defined in the claims.
Claims (1)
1. A chronic disease information management system based on artificial intelligence, comprising:
the patient medical image information analysis module: the method comprises the steps of acquiring medical image information of each patient with slow lung resistance in a recuperation zone of a target hospital in each sampling time period in a monitoring period, recording the medical image information as medical image information of each patient in each sampling time period in the monitoring period, and analyzing the disease severity coefficient of each patient based on medical images in each sampling time period in the monitoring period;
patient clinical symptom information analysis module: the system is used for acquiring clinical symptom information of each patient in each sampling time period in the monitoring period and analyzing the disease severity coefficient of each patient based on clinical symptoms in each sampling time period in the monitoring period;
patient lung function examination information analysis module: the system is used for acquiring lung function examination information of each patient in each sampling time period in the monitoring period and analyzing the disease severity coefficient of each patient based on lung function examination in each sampling time period in the monitoring period;
a blood gas examination information analysis module of the patient: the system is used for acquiring blood gas examination information of each patient in each sampling time period in the monitoring period and analyzing the disease severity coefficient of each patient based on blood gas examination in each sampling time period in the monitoring period;
patient blood routine examination information analysis module: the system is used for acquiring the blood routine examination information of each patient in each sampling time period in the monitoring period and analyzing the disease severity coefficient of each patient based on the blood routine examination in each sampling time period in the monitoring period;
patient condition development trend analysis module: the system is used for analyzing the disease development trend type of each patient and feeding back the disease development trend type according to the disease severity coefficient of each patient based on medical images, clinical symptoms, lung function examination, blood gas examination and blood routine examination in each sampling time period in the monitoring period;
database: the medical record information of the target hospital recuperation area slow-resistance lung patient is stored;
the specific analysis process of the patient medical image information analysis module comprises the following steps:
setting the duration of a monitoring period, and dividing the monitoring period according to a preset equal time length principle to obtain each sampling time period in the monitoring period;
acquiring medical image information of each patient in each sampling time period in a monitoring period, obtaining the bronchus outline of each patient in each sampling time period in the monitoring period, extracting medical record information of the slow-resistance lung patient in the target hospital nursing area stored in a database, obtaining the gender and age of each patient, comparing the gender and age with the reference bronchus outline corresponding to each age range of each preset gender, and screening to obtain the reference bronchus outline of each patient;
comparing the bronchus profile of each patient in each sampling period with the corresponding reference bronchus profile to obtain the coincidence degree of the bronchus profile of each patient in each sampling period and the corresponding reference bronchus profile, and marking the coincidence degree as，/>Indicate->Number of the individual sampling periods, +.>，/>Indicate->The number of the individual patient is set,；
by analysis of formulasObtaining the bronchus deformation coefficient of each patient in each sampling period in the monitoring period>Wherein->Representing a preset bronchus contour coincidence degree threshold;
the specific analysis process of the patient medical image information analysis module further comprises the following steps:
according to the medical image information of each patient in each sampling time period in the monitoring period, the area of each mucus area of each bronchus of each patient in each sampling time period in the monitoring period is obtained and is recorded as，/>Indicate->The number of the mucus region is determined,；
dividing the bronchus region according to a preset principle to obtain each subregion of bronchus, acquiring the positions of mucus regions of each patient bronchus in each sampling time period in the monitoring period, comparing the positions with the influence factors of each subregion of bronchus in the preset period, screening to obtain the influence factors of the mucus regions of each patient bronchus in each sampling time period in the monitoring period, and marking the influence factors as；
By analysis of formulasObtaining each sampling time period in the monitoring periodBronchial mucus accumulation coefficient of each patient +.>Wherein->Representing an influence factor corresponding to a preset bronchus unit mucus area;
arranging each detection point on the bronchus of each patient according to a preset principle, and obtaining the thickness of the bronchus wall and the diameter of the bronchus inner cavity of each detection point on the bronchus of each patient in each sampling period according to the medical image information of each patient in each sampling period in the monitoring period;
obtaining the reference ratio of the bronchial wall thickness to the bronchial inner cavity diameter of each patient, and recording the reference ratio as；
By analysis of formulasObtaining the bronchial wall thickening coefficient of each patient in each sampling period in the monitoring period>Wherein->Respectively represent the +.>Sample time period->The patient is at first->Bronchial wall thickness and bronchial lumen diameter for each detection point, +.>；
The specific analysis process of the patient medical image information analysis module further comprises the following steps:
by analysis of formulasObtaining the disease severity coefficient of each patient based on the medical image in each sampling period in the monitoring period>WhereinThreshold values respectively representing preset bronchi deformation coefficient, bronchi mucus accumulation coefficient and bronchi wall thickening coefficient, +.>Respectively representing preset weight factors of bronchus deformation coefficient, bronchus mucus accumulation coefficient and bronchus wall thickening coefficient;
the specific analysis process of the patient clinical symptom information analysis module is as follows:
acquiring clinical symptom information of each patient in each sampling time period in the monitoring period, obtaining respiratory rate, pulse and sputum excretion of each patient in each sampling time period in the monitoring period, and respectively marking the respiratory rate, pulse and sputum excretion asThe sputum excretion color of each patient in each sampling time period in the monitoring period is obtained, compared with the influence factors corresponding to the preset sputum excretion color, and the influence factors corresponding to the sputum excretion color of each patient in each sampling time period in the monitoring period are obtained through screening and expressed as->；
Acquiring the reference respiratory rate and reference pulse of each patient, and dividing the reference respiratory rate and the reference pulseIs marked as；
By analysis of formulasObtaining the disease severity coefficient of each patient based on clinical symptoms in each sampling period in the monitoring period>Wherein->Representing natural constant->Indicating the influence factor corresponding to the preset unit sputum excretion amount, +.>Weights respectively representing preset respiratory rate, pulse and sputum excretion;
the specific analysis process of the patient lung function examination information analysis module is as follows:
acquiring lung function examination information of each patient in each sampling time period in a monitoring period, obtaining one second forced expiration volume, forced vital capacity and expiration peak flow rate of each patient in each sampling time period in the monitoring period, and respectively recording the obtained information as；
Obtaining the reference ratio of one second forced expiration volume to forced vital capacity of each patient, and recording the reference ratio as；
Comparing the gender and age of each patient with the expected value of the expiratory peak flow velocity corresponding to each age of each preset gender, screening to obtain the expected value of the expiratory peak flow velocity of each patient, and recording the expected valueIs that；
By analysis of formulasObtaining the disease severity coefficient of each patient based on the lung function examination in each sampling period>Wherein->A correction factor representing a preset disease severity coefficient based on a lung function examination;
the specific analysis process of the blood gas examination information analysis module of the patient is as follows:
obtaining blood gas examination information of each patient in each sampling time period in the monitoring period, obtaining alveolar oxygen partial pressure and arterial blood carbon dioxide partial pressure of each patient in each sampling time period in the monitoring period, and respectively marking the alveolar oxygen partial pressure and the arterial blood carbon dioxide partial pressure as；
Obtaining the reference alveolar oxygen partial pressure and the reference arterial blood carbon dioxide partial pressure of each patient, and respectively marking the partial pressures as；
By analysis of formulasObtaining the disease severity coefficient of each patient based on blood gas examination in each sampling period>Wherein->Respectively are provided withWeights representing preset alveolar oxygen partial pressure and arterial blood carbon dioxide partial pressure;
the specific analysis process of the patient blood routine examination information analysis module is as follows:
obtaining blood routine examination information of each patient in each sampling time period in the monitoring period, obtaining eosinophil percentage and neutrophil percentage of each patient in each sampling time period in the monitoring period, and respectively marking the eosinophil percentage and the neutrophil percentage as；
Reference values of eosinophil percentage and neutrophil percentage of each patient were obtained and respectively recorded as；
By analysis of formulasObtaining the disease severity coefficient of each patient based on blood routine examination in each sampling period>；
The specific analysis process of the patient disease development trend analysis module comprises the following steps:
acquiring climate information of each sampling time period in the monitoring period, obtaining air temperature variation and air humidity variation of each sampling time period in the monitoring period, and respectively marking the air temperature variation and the air humidity variation asBy analysis of the formulaObtaining the climate influence factor +.>Wherein->Respectively representing the influence factors of the preset unit air temperature variation and the unit air humidity variation;
by analysis of formulasObtaining an evaluation index of the severity of the illness of each patient in each sampling period in the monitoring period>Wherein->A correction amount indicating a preset disease severity assessment index;
the specific analysis process of the patient disease development trend analysis module further comprises the following steps:
according to the disease severity evaluation index of each patient in each sampling time period in the monitoring period, drawing a disease development trend curve of each patient;
comparing the disease development trend curve of each patient with the disease development trend curve corresponding to the preset various disease development trend types to obtain the similarity of the disease development trend curve of each patient and the disease development trend curve corresponding to the preset various disease development trend types, taking the disease development trend type corresponding to the maximum similarity as the disease development trend type of the patient, counting to obtain the disease development trend type of each patient, and feeding back to the medical team of the target hospital.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111081328A (en) * | 2019-12-20 | 2020-04-28 | 赛客（厦门）医疗器械有限公司 | Rehabilitation management system and method |
| CN114694844A (en) * | 2022-03-31 | 2022-07-01 | 橙意家人科技（天津）有限公司 | Chronic obstructive pulmonary disease assessment method based on oxygen saturation acquisition and analysis |
| CN116246778A (en) * | 2023-04-28 | 2023-06-09 | 北京智想创源科技有限公司 | Intelligent diagnosis platform for lung function detection |
| CN116612891A (en) * | 2023-07-14 | 2023-08-18 | 营动智能技术(山东)有限公司 | Chronic patient data processing system |
-
2023
- 2023-12-27 CN CN202311810700.2A patent/CN117497181B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111081328A (en) * | 2019-12-20 | 2020-04-28 | 赛客（厦门）医疗器械有限公司 | Rehabilitation management system and method |
| CN114694844A (en) * | 2022-03-31 | 2022-07-01 | 橙意家人科技（天津）有限公司 | Chronic obstructive pulmonary disease assessment method based on oxygen saturation acquisition and analysis |
| CN116246778A (en) * | 2023-04-28 | 2023-06-09 | 北京智想创源科技有限公司 | Intelligent diagnosis platform for lung function detection |
| CN116612891A (en) * | 2023-07-14 | 2023-08-18 | 营动智能技术(山东)有限公司 | Chronic patient data processing system |
Non-Patent Citations (7)
| Title |
|---|
| "Onefunctional variant in the 3!"untranslated region ofTLR4is associated with the elevated risk of ventilator"associated pneumonia in the patients with chronic obstructive pulmonary disease";Zhao Xiaoyun等;《Journal of Cellular Physiology》;20190219;全文 * |
| "The Relationship Between Dyspnea and Pulmonary Functions, Arterial Blood Gases and Exercise Capacity in Patients with COPD";Öznur AKKOCA等;《Tüberküloz ve Toraks Dergisi》;20011231;第49卷(第4期);全文 * |
| MSCT定量研究技术预测合并COPD肺癌患者的放疗效果;任会丽;方伟军;洪贤;;中国临床研究;20200720(07);全文 * |
| 任会丽 ; 方伟军 ; 洪贤 ; .MSCT定量研究技术预测合并COPD肺癌患者的放疗效果.中国临床研究.2020,(07),全文. * |
| 史雪 ; 左一俊 ; 俞小卫 ; .慢阻肺急性加重期肺功能与血气分析、炎症指标相关性评价.临床医药文献电子杂志.2018,(71),全文. * |
| 慢阻肺急性加重期肺功能与血气分析、炎症指标相关性评价;史雪;左一俊;俞小卫;;临床医药文献电子杂志;20180903(71);全文 * |
| 竺久东."匹多莫德联合低剂量阿奇霉素治疗支气管扩张症状效果观察".《中国乡村医药》.2023,第30卷(第12期),全文. * |
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