A nursing management system for chronic disease patients based on big data

Abstract

The present invention discloses a nursing management system for chronic disease patients based on big data, which relates to the field of medical nursing technology, including a disease course development analysis unit, a patient health analysis unit, an online diagnosis and treatment unit, a nursing management unit, a nursing recommendation unit, and a nursing correction unit; the present invention constructs a disease course development model of the chronic disease according to the disease course development data, and makes a graded judgment on the disease course development evaluation trend according to the patient's physiological data, and corrects the judgment result through the online diagnosis and treatment unit, obtains a matching nursing list according to the judgment result, and sends the nursing list to the nursing staff's telemedicine terminal and the intelligent mobile terminal of the chronic disease patient's family members, obtains the patient's physiological data after completing each nursing judgment cycle, judges the correction trend of the nursing influence coefficient, evaluates the effect of the nursing plan, and timely corrects the nursing list according to the patient's chronic disease development process, so as to better match the patient's disease development and inhibit the deterioration of the chronic disease.

Description

Technical Field

[0001] The present invention relates to the field of medical care technology, and in particular to a chronic disease patient care management system based on big data. Background Art

[0002] Today, the trend of population aging is difficult to reverse worldwide. The aging population is increasing rapidly. As they age, many elderly people will experience weakened physical functions and cognitive decline, and need help and care from others in their daily lives. Although the degree of aging is increasing, the spectrum of human diseases is shifting from acute diseases to chronic diseases, people's medical concepts are gradually shifting from treatment to prevention, and the mode of medical diagnosis and treatment is gradually shifting from acute diagnosis and treatment mode to chronic diagnosis and treatment mode.

[0003] With the continuous improvement of living standards and the arrival of an aging society, people's demand for prevention and health care is increasing. Effective disease prevention can reduce the waste of resources in the medical system and improve medical efficiency. People with chronic diseases now account for about one-tenth of the total population. Chronic diseases refer to the general term for diseases that are not contagious and have long-term accumulation to form disease morphological damage. Specifically, they include hypertension, diabetes, hyperlipidemia, and inflammatory bowel disease. Once prevention and treatment are not in time, it will cause harm to the economy, life, and other aspects. Through biomedical monitoring, medication, and supervision of patients' execution of medical advice for patients with chronic diseases. Chronic diseases can be effectively prevented and controlled. However, the traditional chronic disease management model has the problem of fragmented management, and cannot achieve continuous and full-process management of patients, resulting in poor medical experience and low quality of life for patients. Therefore, how to achieve standardized and standardized management of chronic disease patients has always been a problem that needs to be solved in the field of medical care;

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to obtain the patient's physiological data after completing each nursing judgment cycle, judge the correction trend of the nursing influence coefficient, evaluate the effect of the nursing plan, and timely correct the nursing list according to the patient's chronic disease development process to better match the patient's disease development and inhibit the deterioration of chronic diseases.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a nursing management system for chronic disease patients based on big data, comprising a disease course development analysis unit, a patient health analysis unit, an online diagnosis and treatment unit, a nursing management unit, a nursing recommendation unit, and a nursing correction unit;

[0007] The disease course analysis unit obtains the disease course data of chronic diseases suffered by chronic disease patients from the network platform and medical database through big data, and builds a disease course model of the chronic disease based on the disease course data;

[0008] The patient health analysis unit is used to obtain the historical case data and current physiological data of chronic disease patients, calculate the disease progression assessment coefficient according to the changes in the patient's physiological data, substitute the disease progression assessment coefficient into the disease progression model to obtain the patient's disease progression assessment trend, and grade the disease progression assessment trend according to the preset trend judgment interval, and output the judgment result to the online diagnosis and treatment unit;

[0009] The online diagnosis and treatment unit is used to provide several online doctor ports. After obtaining the judgment result, the corresponding online doctor port is matched according to the chronic disease suffered by the chronic disease patient, and the judgment result is displayed to the doctor. After the doctor's online diagnosis, the judgment result is corrected, and the corrected judgment result is obtained and sent to the nursing recommendation unit;

[0010] Specifically, the online diagnosis and treatment unit includes several online doctor ports and an online service terminal. The online service terminal is used to obtain the judgment result, and match the corresponding online doctor port according to the chronic disease suffered by the chronic disease patient, and send the judgment result to the corresponding online doctor port. The doctor obtains the judgment result through the online doctor port, and corrects the disease development assessment trend according to the specific content of the judgment result and his own diagnostic experience, and obtains the corrected judgment result;

[0011] The nursing management unit obtains the corresponding nursing items of chronic diseases suffered by chronic patients from the network platform and medical database through big data to customize periodic nursing lists, and classifies and stores the nursing lists according to the classification standards of the disease development assessment trend, and obtains routine nursing lists, nursing lists for the deterioration period, and nursing lists for the terminal period;

[0012] After obtaining the judgment result, the nursing recommendation unit searches for a matching nursing list from the nursing management unit and sends the nursing list to the nursing staff's telemedicine terminal and the smart mobile terminal of the chronic disease patient's family members;

[0013] The nursing correction unit is used to obtain timing from the starting time of the nursing cycle and preset the nursing judgment cycle. After completing each nursing judgment cycle, the patient's physiological data is obtained, the nursing impact coefficient is calculated based on the changes in the patient's physiological data, and the correction trend of the nursing impact coefficient is judged according to the preset correction interval. A nursing update signal or a nursing maintenance signal is generated and sent to the patient health analysis unit.

[0014] Furthermore, the specific method of constructing the course development model of the chronic disease is as follows:

[0015] S101, determining the disease classification of the chronic disease according to the diagnosis result of the chronic disease patient, and obtaining chronic disease keywords;

[0016] S102, setting a keyword search engine according to the chronic disease keyword, and obtaining the disease course development data of the chronic disease in the network platform and the medical database through the keyword search engine, and according to the disease course development data, it can be known that the development cycle T of the chronic disease is the incubation period T1, the initial symptom period T2, the clinical period T3, the chronic stage T4, the exacerbation stage T4 and the terminal stage T6;

[0017] The incubation period is T1: the disease has not yet manifested itself and some risk factors may exist;

[0018] Initial symptom stage T2: patients may experience mild symptoms, which are often easily ignored or misunderstood;

[0019] Clinical stage T3: The disease shows obvious symptoms and enters the diagnosis stage, and medical intervention may be required;

[0020] Chronic stage T4: The disease is stable but still requires long-term management and may be accompanied by complications;

[0021] Deterioration stage T5: If not controlled, the disease may worsen, affecting the quality of life, leading to serious complications or disability;

[0022] Terminal stage T6: The patient may be facing life-threatening situations and requires special medical and psychological support.

[0023] S103, obtaining the pathological characteristics corresponding to the nodes of the development cycle according to the disease course development data, wherein the pathological characteristics include the characteristics of the affected functional organs, the surface characteristics and the physiological state, and obtaining the pathological characteristic coefficient by comprehensively evaluating the pathological characteristics according to the doctor's medical experience. The pathological characteristic coefficient is to define the development degree of the chronic disease through the pathological characteristics reflected by the chronic disease patients. The larger the pathological characteristic coefficient is, the closer the chronic disease is to the terminal stage T6;

[0024] The comprehensive evaluation of the pathological characteristics can be used to obtain the pathological characteristic coefficient, which can be used to analyze the types of chronic diseases. Taking diabetes as an example, the affected functional organ characteristics include cardiovascular damage, nerve damage, and kidney damage.

[0025] Surface characteristics include eye complications, hand and foot ulcers, and the extent of skin infection;

[0026] Physiological traits include levels of psychological anxiety and depression;

[0027] Specifically: the affected functional organ traits are comprehensively evaluated by the examination results of the functional organs, and the evaluation value a of the affected functional organ traits is obtained by means of a scale. The surface traits are comprehensively evaluated by the doctors of the online diagnosis and treatment unit on the surface pathology of the chronic disease patients, and the evaluation value b of the surface traits is obtained by grading. The physiological state is comprehensively evaluated by the psychological anxiety and depression of the chronic disease patients, and the evaluation value c of the affected functional organ traits is also obtained by means of a scale. The pathological characteristic coefficient Hi is calculated according to the following normalization formula: , where α, β, and γ are all preset proportional coefficients, and the disease characteristic coefficient is used to reflect the degree of disease verification of patients with chronic diseases;

[0028] S104. A two-dimensional matrix is ​​established with the development cycle T as the X-axis coordinate and the pathological characteristic coefficient as the Y-axis coordinate, and the patient's physiological data is marked at the corresponding pathological characteristic coefficient to obtain a standard disease course development model. According to the standard disease course development model, the correlation between the patient's physiological data and the pathological characteristic coefficient can be known.

[0029] Furthermore, the specific process of calculating the disease progression assessment coefficient is as follows:

[0030] S201, obtaining historical case data of patients with chronic diseases, sorting and arranging the historical cases according to the time in the historical case data, and extracting the historical physiological data of the patients from the historical case data;

[0031] S202, similarly establishing a two-dimensional matrix with the development cycle T as the X-axis coordinate and the symptom characteristic coefficient as the Y-axis coordinate, marking the patient's historical physiological data in the corresponding symptom characteristic coefficient, and obtaining the patient's personal disease course development model;

[0032] S203, obtaining the patient's current physiological data, which includes the blood sugar value Gi, the blood lipid value Fi and the liver function value Hi, and calculating the patient's disease progression assessment coefficient Wi according to the following formula:

[0033] , where e1, e2 and e3 are preset proportional coefficients, and [Gmin, Gmax] is the preset normal blood sugar value range;

[0034] The disease progression assessment coefficient is used to evaluate the degree of progression of the patient's chronic disease based on the patient's current physiological data. The larger the disease progression assessment coefficient, the greater the malignant effect of the patient's chronic disease on the patient's health status. Conversely, the smaller the disease progression assessment coefficient, the smaller the malignant effect of the patient's chronic disease on the patient's health status.

[0035] Furthermore, the process of obtaining the graded judgment result according to the disease progression assessment trend is as follows:

[0036] S301, comparing the patient's personal disease course development model with the standard disease course development model to obtain the correlation between the patient's personal disease course development model and the standard disease course development model, and thus obtaining the correlation coefficient di between the patient's physiological data and the symptom characteristic coefficient in the personal disease course development model;

[0037] S302, the patient's disease progression assessment coefficient Wi is combined with the correlation coefficient di to obtain the patient's disease progression assessment trend;

[0038] S303, obtain the preset trend judgment interval [Dmin, Dmax], if If it is greater than or equal to Dmin, it means that the disease progression assessment trend is tending towards a benign development trend, and the result is generated to the superior;

[0039] like If it is greater than Dmin and less than Dmax, it means that the disease progression assessment trend tends to be normal, generating a level result;

[0040] like If it is greater than or equal to Dmax, it means that the disease progression assessment trend tends to be a malignant development trend, and a downward result is generated.

[0041] Furthermore, the nursing list includes medical care items, dietary care items, exercise care items and psychological treatment items:

[0042] Medical care matters include medication management matters and specific matters of routine examinations and the examination cycle;

[0043] Dietary care includes patient-specific recipes, dietary taboos, and dietary control cycles;

[0044] Sports care items include the appropriate exercise method, duration and frequency of exercise;

[0045] Psychological treatment items include family communication advice and art therapy advice.

[0046] Furthermore, the nursing judgment cycle is related to the inspection cycle in medical care matters. In the normal state of chronic diseases, the nursing judgment cycle is consistent with the inspection cycle. In the exacerbation stage of chronic diseases, the nursing judgment cycle is one third of the inspection cycle. In the terminal stage of chronic diseases, the nursing judgment cycle is one sixth of the inspection cycle.

[0047] Furthermore, the specific process of determining the correction trend of the nursing impact coefficient is as follows:

[0048] S401, acquiring the patient's physiological data after completing each nursing judgment cycle, and obtaining the patient's post-nursing physiological data, wherein the post-nursing physiological data includes a blood sugar value Gj, a blood lipid value Fj, and a liver function value Hj;

[0049] S402. Calculate the patient's nursing impact coefficient Ki according to the following formula: , e4, e5 and e6 are preset proportional coefficients, G0 is the blood glucose value before care, F0 is the blood lipid value before care and H0 is the liver function value before care;

[0050] The nursing impact coefficient reflects the degree of chronic disease development of chronic disease patients after nursing. The larger the nursing impact coefficient, the faster the deterioration trend of chronic disease after nursing. On the contrary, the smaller the disease progression assessment coefficient, the slower the deterioration trend of chronic disease after nursing.

[0051] S403, obtaining a preset correction interval [Kmim, Kmax], and if the nursing influence coefficient is greater than or equal to Kmim, generating a nursing maintenance signal;

[0052] If the nursing impact coefficient is greater than Kmim and less than Kmax, a nursing update signal is generated;

[0053] If the nursing impact coefficient is greater than or equal to Kmax, a nursing abnormality signal is generated and sent to the nursing staff's telemedicine terminal and the smart mobile terminal of the chronic disease patient's family members for abnormal reminder.

[0054] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0055] The present invention constructs a disease progression model for the chronic disease based on the disease progression data, and makes a graded judgment on the disease progression assessment trend based on the patient's physiological data, and corrects the judgment result through an online diagnosis and treatment unit, and obtains a matching nursing list based on the judgment result, and sends the nursing list to the nursing staff's telemedicine terminal and the smart mobile terminal of the chronic disease patient's family members, obtains the patient's physiological data after completing each nursing judgment cycle, judges the correction trend of the nursing influence coefficient, evaluates the effect of the nursing plan, and timely corrects the nursing list according to the patient's chronic disease development progress to better match the patient's disease progression and inhibit the deterioration of the chronic disease. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 The overall external structure schematic diagram of the present invention is shown. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Example:

[0059] like Figure 1 As shown, a nursing management system for chronic disease patients based on big data includes a disease course analysis unit, a patient health analysis unit, an online diagnosis and treatment unit, a nursing management unit, a nursing recommendation unit, and a nursing correction unit;

[0060] The disease course analysis unit obtains the disease course data of chronic diseases suffered by chronic disease patients from the network platform and medical database through big data, and builds a disease course model of the chronic disease based on the disease course data;

[0061] The specific method of constructing the disease progression model of this chronic disease is as follows:

[0062] S101, determining the disease classification of the chronic disease according to the diagnosis result of the chronic disease patient, and obtaining chronic disease keywords;

[0063] S102, setting a keyword search engine according to the chronic disease keyword, and obtaining the disease course development data of the chronic disease in the network platform and the medical database through the keyword search engine, and according to the disease course development data, it can be known that the development cycle T of the chronic disease is the incubation period T1, the initial symptom period T2, the clinical period T3, the chronic stage T4, the exacerbation stage T4 and the terminal stage T6;

[0064] The incubation period is T1: the disease has not yet manifested itself and some risk factors may exist;

[0065] Initial symptom stage T2: patients may experience mild symptoms, which are often easily ignored or misunderstood;

[0066] Clinical stage T3: The disease shows obvious symptoms and enters the diagnosis stage, and medical intervention may be required;

[0067] Chronic stage T4: The disease is stable but still requires long-term management and may be accompanied by complications;

[0068] Deterioration stage T5: If not controlled, the disease may worsen, affecting the quality of life, leading to serious complications or disability;

[0069] Terminal stage T6: The patient may be facing life-threatening situations and requires special medical and psychological support.

[0070] S103, obtaining the pathological characteristics corresponding to the nodes of the development cycle according to the disease course development data, wherein the pathological characteristics include the characteristics of the affected functional organs, the surface characteristics and the physiological state, and obtaining the pathological characteristic coefficient by comprehensively evaluating the pathological characteristics according to the doctor's medical experience. The pathological characteristic coefficient is to define the development degree of the chronic disease through the pathological characteristics reflected by the chronic disease patients. The larger the pathological characteristic coefficient is, the closer the chronic disease is to the terminal stage T6;

[0071] The comprehensive evaluation of the pathological characteristics can be used to obtain the pathological characteristic coefficient, which can be used to analyze the types of chronic diseases. Taking diabetes as an example, the affected functional organ characteristics include cardiovascular damage, nerve damage, and kidney damage.

[0072] Surface characteristics include eye complications, hand and foot ulcers, and the extent of skin infection;

[0073] Physiological traits include levels of psychological anxiety and depression;

[0074] Specifically: the affected functional organ traits are comprehensively evaluated by the examination results of the functional organs, and the evaluation value a of the affected functional organ traits is obtained by means of a scale. The surface traits are comprehensively evaluated by the doctors of the online diagnosis and treatment unit on the surface pathology of the chronic disease patients, and the evaluation value b of the surface traits is obtained by grading. The physiological state is comprehensively evaluated by the psychological anxiety and depression of the chronic disease patients, and the evaluation value c of the affected functional organ traits is also obtained by means of a scale. The pathological characteristic coefficient Hi is calculated according to the following normalization formula: , where α, β, and γ are all preset proportional coefficients, and the disease characteristic coefficient is used to reflect the degree of disease verification of patients with chronic diseases;

[0075] S104. A two-dimensional matrix is ​​established with the development cycle T as the X-axis coordinate and the pathological characteristic coefficient as the Y-axis coordinate, and the patient's physiological data is marked at the corresponding pathological characteristic coefficient to obtain a standard disease course development model. According to the standard disease course development model, the correlation between the patient's physiological data and the pathological characteristic coefficient can be known.

[0076] The patient health analysis unit is used to obtain the historical case data and current physiological data of chronic disease patients, calculate the disease progression assessment coefficient according to the changes in the patient's physiological data, substitute the disease progression assessment coefficient into the disease progression model to obtain the patient's disease progression assessment trend, and grade the disease progression assessment trend according to the preset trend judgment interval, and output the judgment result to the online diagnosis and treatment unit;

[0077] The specific process of calculating the disease progression assessment coefficient is as follows:

[0078] S201, obtaining historical case data of patients with chronic diseases, sorting and arranging the historical cases according to the time in the historical case data, and extracting the historical physiological data of the patients from the historical case data;

[0079] S202, similarly establishing a two-dimensional matrix with the development cycle T as the X-axis coordinate and the symptom characteristic coefficient as the Y-axis coordinate, marking the patient's historical physiological data in the corresponding symptom characteristic coefficient, and obtaining the patient's personal disease course development model;

[0080] S203, obtaining the patient's current physiological data, which includes the blood sugar value Gi, the blood lipid value Fi and the liver function value Hi, and calculating the patient's disease progression assessment coefficient Wi according to the following formula:

[0081] , where e1, e2 and e3 are preset proportional coefficients, and [Gmin, Gmax] is the preset normal blood sugar value range;

[0082] The disease progression assessment coefficient is used to evaluate the degree of progression of the patient's chronic disease based on the patient's current physiological data. The larger the disease progression assessment coefficient, the greater the malignant effect of the patient's chronic disease on the patient's health status. Conversely, the smaller the disease progression assessment coefficient, the smaller the malignant effect of the patient's chronic disease on the patient's health status.

[0083] The process of obtaining the graded judgment result based on the disease progression assessment trend is as follows:

[0084] S301, comparing the patient's personal disease course development model with the standard disease course development model to obtain the correlation between the patient's personal disease course development model and the standard disease course development model, and thus obtaining the correlation coefficient di between the patient's physiological data and the symptom characteristic coefficient in the personal disease course development model;

[0085] S302, the patient's disease progression assessment coefficient Wi is combined with the correlation coefficient di to obtain the patient's disease progression assessment trend;

[0086] S303, obtain the preset trend judgment interval [Dmin, Dmax], if If it is greater than or equal to Dmin, it means that the disease progression assessment trend is tending towards a benign development trend, and the result is generated to the superior;

[0087] like If it is greater than Dmin and less than Dmax, it means that the disease progression assessment trend tends to be normal, generating a level result;

[0088] like If it is greater than or equal to Dmax, it means that the disease progression assessment trend tends to be a malignant development trend, and a downward result is generated.

[0089] The online diagnosis and treatment unit is used to provide several online doctor ports. After obtaining the judgment result, the corresponding online doctor port is matched according to the chronic disease suffered by the chronic disease patient, and the judgment result is displayed to the doctor. After the doctor's online diagnosis, the judgment result is corrected, and the corrected judgment result is obtained and sent to the nursing recommendation unit;

[0090] Specifically, the online diagnosis and treatment unit includes several online doctor ports and an online service terminal. The online service terminal is used to obtain the judgment result, and match the corresponding online doctor port according to the chronic disease suffered by the chronic disease patient, and send the judgment result to the corresponding online doctor port. The doctor obtains the judgment result through the online doctor port, and corrects the disease development assessment trend according to the specific content of the judgment result and his own diagnostic experience, and obtains the corrected judgment result;

[0091] The nursing management unit obtains the corresponding nursing items of chronic diseases suffered by chronic patients from the network platform and medical database through big data to customize periodic nursing lists, and classifies and stores the nursing lists according to the classification standards of the disease development assessment trend, and obtains routine nursing lists, nursing lists for the deterioration period, and nursing lists for the terminal period;

[0092] The nursing list includes medical care items, dietary care items, exercise care items and psychological treatment items:

[0093] Medical care matters include medication management matters and specific matters of routine examinations and the examination cycle;

[0094] Dietary care includes patient-specific recipes, dietary taboos, and dietary control cycles;

[0095] Sports care items include the appropriate exercise method, duration and frequency of exercise;

[0096] Psychological treatment items include family communication advice and art therapy advice.

[0097] After obtaining the judgment result, the nursing recommendation unit searches for a matching nursing list from the nursing management unit and sends the nursing list to the nursing staff's telemedicine terminal and the smart mobile terminal of the chronic disease patient's family members;

[0098] The nursing correction unit is used to obtain timing from the starting time of the nursing cycle and preset the nursing judgment cycle. After completing each nursing judgment cycle, the patient's physiological data is obtained, the nursing impact coefficient is calculated based on the changes in the patient's physiological data, and the correction trend of the nursing impact coefficient is judged according to the preset correction interval. A nursing update signal or a nursing maintenance signal is generated and sent to the patient health analysis unit.

[0099] The nursing judgment cycle is related to the inspection cycle in medical care matters. In the normal state of chronic diseases, the nursing judgment cycle is consistent with the inspection cycle. In the exacerbation stage of chronic diseases, the nursing judgment cycle is one third of the inspection cycle. In the terminal stage of chronic diseases, the nursing judgment cycle is one sixth of the inspection cycle.

[0100] The specific process of determining the correction trend of the nursing influence coefficient is as follows:

[0101] S401, acquiring the patient's physiological data after completing each nursing judgment cycle, and obtaining the patient's post-nursing physiological data, wherein the post-nursing physiological data includes a blood sugar value Gj, a blood lipid value Fj, and a liver function value Hj;

[0102] S402. Calculate the patient's nursing impact coefficient Ki according to the following formula: , e4, e5 and e6 are preset proportional coefficients, G0 is the blood glucose value before care, F0 is the blood lipid value before care and H0 is the liver function value before care;

[0103] The nursing impact coefficient reflects the degree of chronic disease development of chronic disease patients after nursing. The larger the nursing impact coefficient, the faster the deterioration trend of chronic disease after nursing. On the contrary, the smaller the disease progression assessment coefficient, the slower the deterioration trend of chronic disease after nursing.

[0104] S403, obtaining a preset correction interval [Kmim, Kmax], and if the nursing influence coefficient is greater than or equal to Kmim, generating a nursing maintenance signal;

[0105] If the nursing impact coefficient is greater than Kmim and less than Kmax, a nursing update signal is generated;

[0106] If the nursing impact coefficient is greater than or equal to Kmax, a nursing abnormality signal is generated and sent to the nursing staff's telemedicine terminal and the smart mobile terminal of the chronic disease patient's family members for abnormal reminder.

[0107] The present invention constructs a disease progression model for the chronic disease based on the disease progression data, and makes a graded judgment on the disease progression assessment trend based on the patient's physiological data, and corrects the judgment result through an online diagnosis and treatment unit, and obtains a matching nursing list based on the judgment result, and sends the nursing list to the nursing staff's telemedicine terminal and the smart mobile terminal of the chronic disease patient's family members, obtains the patient's physiological data after completing each nursing judgment cycle, judges the correction trend of the nursing influence coefficient, evaluates the effect of the nursing plan, and timely corrects the nursing list according to the patient's chronic disease development progress to better match the patient's disease progression and inhibit the deterioration of the chronic disease.

[0108] The size of the interval is set to facilitate comparison. The size of the interval value depends on the amount of sample data and the number of bases set by technical personnel in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0109] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0110] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A chronic disease patient care management system based on big data, characterized in that: It includes disease progression analysis unit, patient health analysis unit, online diagnosis and treatment unit, nursing management unit, nursing recommendation unit, and nursing correction unit; The disease course analysis unit obtains the disease course data of chronic diseases suffered by chronic disease patients from the network platform and medical database through big data, and builds a disease course model of the chronic disease based on the disease course data; The specific method of constructing the disease progression model of this chronic disease is as follows: S101, determining the disease classification of the chronic disease according to the diagnosis result of the chronic disease patient, and obtaining chronic disease keywords; S102, setting a keyword search engine according to the chronic disease keyword, and obtaining the disease course development data of the chronic disease in the network platform and the medical database through the keyword search engine, and according to the disease course development data, it can be known that the development cycle T of the chronic disease is the incubation period T1, the initial symptom period T2, the clinical period T3, the chronic stage T4, the exacerbation stage T4 and the terminal stage T6; S103, obtaining the pathological characteristics corresponding to the nodes of the development cycle according to the disease course development data, wherein the pathological characteristics include the characteristics of the affected functional organs, surface characteristics and physiological state, and obtaining the pathological characteristic coefficient by comprehensively evaluating the pathological characteristics; Specifically: the affected functional organ traits are comprehensively evaluated by the examination results of the functional organs, and the evaluation value a of the affected functional organ traits is obtained by means of a scale. The surface traits are comprehensively evaluated by the doctors of the online diagnosis and treatment unit on the surface pathology of the chronic disease patients, and the evaluation value b of the surface traits is obtained by grading. The physiological state is comprehensively evaluated by the psychological anxiety and depression of the chronic disease patients, and the evaluation value c of the affected functional organ traits is also obtained by means of a scale. The pathological characteristic coefficient Hi is calculated according to the following normalization formula: , where α, β, and γ are all preset proportional coefficients, and the disease characteristic coefficient is used to reflect the degree of disease verification of patients with chronic diseases; S104, establishing a two-dimensional matrix with the development cycle T as the X-axis coordinate and the symptom characteristic coefficient as the Y-axis coordinate, and marking the patient's physiological data at the corresponding symptom characteristic coefficient to obtain a standard disease course development model, according to which the correlation between the patient's physiological data and the symptom characteristic coefficient can be known; The patient health analysis unit is used to obtain the historical case data and current physiological data of chronic disease patients, calculate the disease progression assessment coefficient according to the changes in the patient's physiological data, substitute the disease progression assessment coefficient into the disease progression model to obtain the patient's disease progression assessment trend, and grade the disease progression assessment trend according to the preset trend judgment interval, and output the judgment result to the online diagnosis and treatment unit; The specific process of calculating the disease progression assessment coefficient is as follows: S201, obtaining historical case data of patients with chronic diseases, sorting and arranging the historical cases according to the time in the historical case data, and extracting the historical physiological data of the patients from the historical case data; S202, similarly establishing a two-dimensional matrix with the development cycle T as the X-axis coordinate and the symptom characteristic coefficient as the Y-axis coordinate, marking the patient's historical physiological data in the corresponding symptom characteristic coefficient, and obtaining the patient's personal disease course development model; S203, obtaining the patient's current physiological data, which includes the blood sugar value Gi, the blood lipid value Fi and the liver function value Hi, and calculating the patient's disease progression assessment coefficient Wi according to the following formula: , where e1, e2 and e3 are preset proportional coefficients, and [Gmin, Gmax] is the preset normal blood sugar value range; The online diagnosis and treatment unit is used to provide several online doctor ports. After obtaining the judgment result, the corresponding online doctor port is matched according to the chronic disease suffered by the chronic disease patient, and the judgment result is displayed to the doctor. After the doctor's online diagnosis, the judgment result is corrected, and the corrected judgment result is obtained and sent to the nursing recommendation unit; The nursing management unit obtains the corresponding nursing items of chronic diseases suffered by chronic patients from the network platform and medical database through big data to customize periodic nursing lists, and classifies and stores the nursing lists according to the classification standards of the disease development assessment trend, and obtains routine nursing lists, nursing lists for the deterioration period, and nursing lists for the terminal period; After obtaining the judgment result, the nursing recommendation unit searches for a matching nursing list from the nursing management unit and sends the nursing list to the nursing staff's telemedicine terminal and the smart mobile terminal of the chronic disease patient's family members; The nursing correction unit is used to obtain the time from the start time of the nursing cycle and preset the nursing judgment cycle, obtain the patient's physiological data after completing each nursing judgment cycle, calculate the nursing impact coefficient based on the change of the patient's physiological data, judge the correction trend of the nursing impact coefficient according to the preset correction interval, and generate a nursing update signal or a nursing maintenance signal to send to the patient health analysis unit; The specific process of determining the correction trend of the nursing influence coefficient is as follows: S401, acquiring the patient's physiological data after completing each nursing judgment cycle, and obtaining the patient's post-nursing physiological data, wherein the post-nursing physiological data includes a blood sugar value Gj, a blood lipid value Fj, and a liver function value Hj; S402. Calculate the patient's nursing impact coefficient Ki according to the following formula: , e4, e5 and e6 are preset proportional coefficients, G0 is the blood glucose value before care, F0 is the blood lipid value before care and H0 is the liver function value before care; S403, obtaining a preset correction interval [Kmim, Kmax], and if the nursing influence coefficient is greater than or equal to Kmim, generating a nursing maintenance signal; If the nursing impact coefficient is greater than Kmim and less than Kmax, a nursing update signal is generated; If the nursing impact coefficient is greater than or equal to Kmax, a nursing abnormality signal is generated and sent to the nursing staff's telemedicine terminal and the smart mobile terminal of the chronic disease patient's family members for abnormal reminder.

2. The big data-based chronic disease patient care management system according to claim 1 is characterized in that: The process of obtaining the graded judgment result based on the disease progression assessment trend is as follows: S301, comparing the patient's personal disease course development model with the standard disease course development model to obtain the correlation between the patient's personal disease course development model and the standard disease course development model, and thus obtaining the correlation coefficient di between the patient's physiological data and the symptom characteristic coefficient in the personal disease course development model; S302, the patient's disease progression assessment coefficient Wi is combined with the correlation coefficient di to obtain the patient's disease progression assessment trend; S303, obtain the preset trend judgment interval [Dmin, Dmax], if If it is greater than or equal to Dmin, it means that the disease progression assessment trend is tending towards a benign development trend, and the result is generated to the superior; like If it is greater than Dmin and less than Dmax, it means that the disease progression assessment trend tends to be normal, generating a level result; like If it is greater than or equal to Dmax, it means that the disease progression assessment trend tends to be a malignant development trend, and a downward result is generated.

3. The big data-based chronic disease patient care management system according to claim 1 is characterized in that: The nursing list includes medical care items, dietary care items, exercise care items and psychological treatment items: Medical care matters include medication management matters and specific matters of routine examinations and the examination cycle; Dietary care includes patient-specific recipes, dietary taboos, and dietary control cycles; Sports care items include the appropriate exercise method, duration and frequency of exercise; Psychological treatment items include family communication advice and art therapy advice.

4. The big data-based chronic disease patient care management system according to claim 1 is characterized in that: The nursing judgment cycle is related to the inspection cycle in medical care matters. In the normal state of chronic diseases, the nursing judgment cycle is consistent with the inspection cycle. In the exacerbation stage of chronic diseases, the nursing judgment cycle is one third of the inspection cycle. In the terminal stage of chronic diseases, the nursing judgment cycle is one sixth of the inspection cycle.

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