CN117854732A - Chronic disease management method and system based on big data analysis - Google Patents
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- 208000017667 Chronic Disease Diseases 0.000 title claims abstract description 166
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- 201000010099 disease Diseases 0.000 claims abstract description 50
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Abstract
The invention provides a chronic disease management method and system based on big data analysis, which belongs to the technical field of disease management and specifically comprises the following steps: the patient severity is determined through the index data of the associated index and the variation condition of the index data, when the patient is determined not to belong to the patient concerned based on the patient severity, the disease development data of different similar chronic patients in a preset time period in the future are obtained, the correction severity of the patient is determined by combining the similarity of the different similar chronic patients and the patient severity, the patient is divided into the patient concerned and other patients according to the correction severity, the data of the patient concerned corresponding to the chronic diseases to be managed by the hospital and the data of the other patients are used for determining the chronic diseases of the hospital by a consultation management doctor, and the reliability of the hospital outside management of the chronic diseases and the recovery rate of the patient are improved.
Description
Technical Field
The invention belongs to the technical field of disease management, and particularly relates to a chronic disease management method and system based on big data analysis.
Background
Different from other diseases, the key treatment of chronic diseases is that the diet habit and the medication mode of patients are closely related to the development of chronic diseases, so how to manage the chronic diseases outside the hospital is important to slow down the disease development rate of the chronic diseases and improve the survival rate of the chronic diseases.
In order to solve the technical problems, in the prior art, in the invention patent CN202110813766.1 "a chronic disease risk index evaluation and intervention system based on mobile internet", the disease condition of a chronic disease patient is intelligently evaluated, and an intervention implementation module is intelligently pushed to a user according to the risk index evaluation result, so as to realize the intellectualization of intervention evaluation, but the following technical problems exist:
for different chronic disease patients, even if the disease indexes are the same, the development speeds of the different disease indexes of the chronic disease patients are different, so that the disease risks are different, and if the development speeds of the disease indexes of the chronic disease cannot be comprehensively considered, the accurate assessment of the disease risks of the chronic disease patients cannot be accurately realized.
Aiming at the technical problems, the invention provides a chronic disease management method and system based on big data analysis.
Disclosure of Invention
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
according to one aspect of the present invention, a method of chronic disease management based on big data analysis is provided.
The chronic disease management method based on big data analysis is characterized by comprising the following steps:
s1, acquiring an examination index which is related to the existence of chronic diseases in examination indexes of a patient, taking the examination index as a related index, determining the severity of the illness of the patient according to index data of the related index and variation conditions of the index data, and entering a next step when the patient is determined not to belong to the patient concerned based on the severity of the illness;
s2, determining index data of the associated indexes of the patient under different historical examination frequencies based on the examination records, and determining the similarity between the patient and a preset chronic patient in a patient library and similar chronic patients by combining the change condition of the associated indexes under different historical examination frequencies;
s3, acquiring disease development data of different similar chronic patients in a preset time period in the future, determining correction severity of the patients by combining the similarity between the different similar chronic patients and the disease severity, and dividing the patients into concerned patients and other patients according to the correction severity;
s4, determining the chronic disease back diagnosis management doctor of the hospital through data of the concerned patient corresponding to the chronic disease to be managed by the hospital and data of other patients.
The invention has the beneficial effects that:
1. the disease severity of the patient is determined through the index data of the associated index and the variation condition of the index data, so that the disease severity is estimated from the two angles of the abnormal condition of the index data of the associated index and the variation condition of the index data, the technical problem that the disease severity is estimated inaccurately due to the single abnormal condition of the index data is avoided, the accuracy of the disease severity estimation is improved, and a foundation is laid for the differential consultation management doctor to determine.
2. The similarity between the patient and the preset chronic disease patients in the patient library is determined through the index data of the associated indexes of the patient under different historical examination frequencies and the change condition of the associated indexes under different historical examination frequencies, so that the determination of the similar chronic disease patients of the patient from the two angles of the index data and the change condition under different historical examination frequencies is realized, further, the evaluation of the future development condition of the chronic disease of the patient can be accurately realized, and a foundation is laid for accurately realizing the evaluation of the severity degree of the chronic disease of the patient.
3. The data of the concerned patients corresponding to the chronic diseases to be managed in the hospital and the data of other patients are used for determining the back diagnosis management doctors of the chronic diseases in the hospital, so that the technical problem that the efficiency and timeliness of the back diagnosis management cannot meet the requirements due to the fact that the number of the original back diagnosis management doctors is fixed is avoided, the determination of the number of the back diagnosis management doctors from the aspects of the severity and the number of the diseases is realized, and the reliable back diagnosis management of the patients with the chronic diseases is realized.
A further technical solution is that the examination index of the patient is determined according to the self-test result of the patient or the examination result of the medical structure.
The further technical scheme is that the association index is determined according to the type of the chronic disease of the patient, and particularly is determined through a matching result of the type of the chronic disease of the patient.
A further technical solution is that when the patient is determined to be a patient of interest based on the patient severity, the patient is determined to be a patient of interest, and the patient severity is taken as the patient correction severity.
The further technical scheme is that the method for determining the association indexes of the patient in different serious intervals by the index data of the association indexes of the patient specifically comprises the following steps:
and determining a data range of the index data of the associated index according to the index data, and determining a severe section of the associated index based on the data range.
The further technical scheme is that the disease development data comprise the fluctuation amounts of different associated indexes and the quantity of the associated indexes which are developed to a preset serious interval.
A further technical solution is to divide the patient into a patient of interest and other patients according to the correction severity, specifically including:
judging whether the correction severity of the patient is in a preset severity range, if so, taking the patient as other patients, and if not, taking the patient as a patient concerned.
The further technical proposal is that the method for determining the chronic disease back diagnosis management doctor in the hospital comprises the following steps:
determining the comprehensive severity of the concerned patients corresponding to the chronic diseases according to the number of concerned patients corresponding to the chronic diseases which need to be managed by the hospital and the corrected severity of different concerned patients;
determining a combined severity of the other patients corresponding to the chronic disease based on the number of other patients and the corrected severity of the different other patients;
the management difficulty of the chronic diseases of the hospital is determined based on the comprehensive severity of other patients and the comprehensive severity of the concerned patients, and the number of the back-diagnosis management doctors of the chronic diseases of the hospital is determined through the management difficulty.
In a second aspect, the present invention provides a computer system comprising: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: the processor executes a chronic disease management method based on big data analysis as described above when running the computer program.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention as set forth hereinafter.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings;
FIG. 1 is a flow chart of a method of chronic disease management based on big data analysis;
FIG. 2 is a flow chart of a method of determining a patient's severity of a disease;
FIG. 3 is a flow chart of a method of determining the similarity of a patient to a preset chronically ill patient in a patient library;
FIG. 4 is a flow chart of another method of determining similarity of a patient to a preset chronically ill patient in a patient library;
FIG. 5 is a flow chart of a method of correcting a determination of a degree of illness;
FIG. 6 is a flow chart of a method of determining a physician for back-diagnosis of chronic diseases in a hospital;
FIG. 7 is a block diagram of a computer system.
Detailed Description
In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present disclosure.
The key point of chronic diseases is that the chronic diseases are managed and returned after hospital, and in the prior art, when the severity of the chronic diseases of the patient suffering from the chronic diseases is evaluated, the severity of the chronic diseases of the patient is often evaluated only from the point of view of index data of related indexes, but the severity of the chronic diseases of different patients caused by the speed of changing the index data is ignored, so that the severity of the chronic diseases cannot be accurately evaluated.
In order to solve the technical problems, the following technical scheme is adopted:
firstly, determining the disease severity of a patient through index data of an associated index related to the existence of chronic diseases and the change condition of the index data in the examination index of the patient, specifically, determining the weight of the duty ratio of the associated index with abnormal existence of the index data and the duty ratio of the associated index of the change condition of the index data and determining the disease severity of the patient, and when the disease severity of the patient is smaller, at the moment, the patient does not belong to the patient concerned, and entering the next step;
then determining the similarity between the patient and the preset chronic disease patient in the patient library and the similarity of the patient with the similar chronic disease patient based on index data of the associated indexes of the patient under different historical examination frequencies and the change condition of the associated indexes under different historical examination frequencies, specifically, firstly determining the similarity under different historical examination frequencies by the sum of the number of the associated indexes with the same deviation amount between the index data of the associated indexes being smaller than the preset deviation and the change rate of the associated indexes and the ratio of the associated indexes, and then determining the similarity between the patient and the preset chronic disease patient in the patient library according to the average value of the similarity under different historical examination frequencies, and taking the preset chronic disease patient with larger similarity as the similar chronic disease patient;
determining the correction severity of the patient according to the disease development data of different similar chronic patients in a future preset time period, the similarity with the patient and the disease severity, specifically determining the correction weight values of different similar chronic patients according to the product of the number ratio of the associated indexes of the similar chronic patients in the future preset time period to the preset severity interval and the similarity, and then correcting the disease severity according to the average value of the correction weight values of the different similar chronic patients to obtain the correction severity, wherein the patient is classified into a concerned patient and other patients based on the correction severity;
and finally, determining the comprehensive severity of the concerned patients corresponding to the chronic diseases according to the number of concerned patients corresponding to the chronic diseases to be managed by the hospital and the correction severity of different concerned patients, determining the comprehensive severity of other patients corresponding to the chronic diseases based on the number of other patients and the correction severity of different other patients, determining the management difficulty of the chronic diseases of the hospital based on the comprehensive severity of other patients and the comprehensive severity of concerned patients, and determining the number of the back-diagnosis management doctors of the chronic diseases of the hospital according to the management difficulty.
Further explanation will be made below from two perspectives of the method class embodiment and the system class embodiment.
In order to solve the above-mentioned problems, according to one aspect of the present invention, as shown in fig. 1, there is provided a chronic disease management method based on big data analysis, which is characterized by comprising:
s1, acquiring an examination index which is related to the existence of chronic diseases in examination indexes of a patient, taking the examination index as a related index, determining the severity of the illness of the patient according to index data of the related index and variation conditions of the index data, and entering a next step when the patient is determined not to belong to the patient concerned based on the severity of the illness;
further, the examination index of the patient is determined according to the self-test result of the patient or the examination result of the medical structure.
The association index is determined according to the type of the chronic disease of the patient, and particularly is determined according to a matching result of the type of the chronic disease of the patient.
It will be appreciated that when the patient is determined to be of interest based on the severity of the disease, then the patient is determined to be of interest and the severity of the disease of the patient is taken as the corrected severity of the patient.
Specifically, the variation of the index data is determined according to the deviation amount of the index data and the index data of the same associated index under the adjacent historical inspection frequency, and specifically includes the variation amount and the variation rate.
In one possible embodiment, as shown in fig. 2, the method for determining the severity of the disease of the patient in step S1 is as follows:
determining the associated index of the patient in different serious intervals according to the index data of the associated index of the patient, determining the index severity of different serious intervals by combining the index data of different associated indexes, and determining the index problem assessment of the patient according to the index severity of different serious intervals;
determining the fluctuation amount and the fluctuation rate of the index data of the different associated indexes of the patient based on the fluctuation situation of the index data of the associated indexes of the patient, and determining the index fluctuation assessment of the patient according to the fluctuation amount and the fluctuation rate of the index data of the different associated indexes of the patient;
the patient's severity of the illness is determined by an index change assessment and an index problem assessment of the patient.
In another possible embodiment, the method for determining the severity of the illness of the patient in the step S1 is as follows:
determining the associated indexes of the patient in different serious intervals according to the index data of the associated indexes of the patient, and determining the patient as a concerned patient when the number of the associated indexes in the preset serious intervals is larger than the preset index number;
determining the change rate of the index data of different associated indexes of the patient based on the change condition of the index data of the associated indexes of the patient when the number of the associated indexes in the preset serious interval is not larger than the preset index number, and determining the patient as a concerned patient when the change rate does not meet the required number of the associated indexes;
when the number of the associated indexes of which the fluctuation rate does not meet the requirements is not greater than the number of the preset indexes, determining the index severity of different severe intervals according to the number of the associated indexes of different severe intervals and the index data of different associated indexes, judging whether the preset severe intervals of which the index severity does not meet the requirements exist or not, if so, determining that the patient is a patient concerned, and if not, entering the next step;
determining an index problem assessment of the patient through index severity of different severe intervals, determining fluctuation amounts and fluctuation rates of index data of different associated indexes of the patient based on fluctuation conditions of the index data of the associated indexes of the patient, and determining an index fluctuation assessment of the patient according to the fluctuation amounts and the fluctuation rates of the index data of the different associated indexes of the patient;
the patient's severity of the illness is determined by an index change assessment and an index problem assessment of the patient.
Specifically, determining the associated index of the patient in different serious intervals according to the index data of the associated index of the patient specifically includes:
and determining a data range of the index data of the associated index according to the index data, and determining a severe section of the associated index based on the data range.
It should be noted that, the preset serious section is determined according to the type of the association index, specifically, the serious section to be concerned corresponding to the association index is determined according to the type of the association index, and the serious section to be associated is taken as the preset serious section.
In another possible embodiment, the method for determining the severity of the illness of the patient in the step S1 is as follows:
s11, determining the associated index of the patient in different serious intervals according to the index data of the associated index of the patient, determining the index severity of different serious intervals according to the number of the associated indexes of different serious intervals and the index data of different associated indexes, judging whether a preset serious interval with the index severity not meeting the requirement exists or not, if yes, determining that the patient is a patient concerned, and if not, entering the next step;
s12, determining the fluctuation rate and fluctuation amount of the index data of different associated indexes of the patient based on the fluctuation condition of the index data of the associated indexes of the patient, determining the fluctuation amount of the index of different associated indexes according to the fluctuation amount and the fluctuation rate of the index data of different associated indexes of the patient, judging whether the number of the associated indexes of which the fluctuation amount of the index does not meet the requirement meets the requirement, if yes, entering the next step, and if no, determining that the patient is a patient concerned;
s13, determining an index problem evaluation quantity of the patient according to index severity of different severe intervals and the number of preset severe intervals of which the index severity does not meet the requirement, judging whether the index problem evaluation quantity of the patient meets the requirement, if so, entering a next step, and if not, determining that the patient is a patient concerned;
s14, determining an index variation evaluation quantity of the patient according to the index variation quantities of different associated indexes of the patient and the number of associated indexes of which the index variation quantities do not meet the requirements, judging whether the index variation evaluation quantity of the patient meets the requirements, if so, entering a next step, and if not, determining that the patient is a patient concerned;
s15, determining the severity of the illness of the patient through the index change evaluation value and the index problem evaluation value of the patient.
S2, determining index data of the associated indexes of the patient under different historical examination frequencies based on the examination records, and determining the similarity between the patient and a preset chronic patient in a patient library and similar chronic patients by combining the change condition of the associated indexes under different historical examination frequencies;
further, the examination record is determined according to an uploaded record of the patient's historical examination data or a read result of the patient's historical examination data at a medical institution.
In one possible embodiment, as shown in fig. 3, the method for determining the similarity between the patient in the step S2 and the preset chronic disease patient in the patient library is as follows:
determining the index similarity of the patient and the preset chronic patient under different historical examination frequencies according to the index data of the associated indexes of the patient under different historical examination frequencies;
determining index variation similarity of the patient and the preset chronic patient under different historical examination frequencies according to variation conditions of associated indexes of the patient under different historical examination frequencies;
and determining the number of similar examination frequencies of the patient based on the index similarity and the index variation similarity of the patient under different historical examination frequencies, and determining the similarity of the patient and the preset chronic disease patient by combining the index similarity and the index variation similarity of the patient under different historical examination frequencies.
In another possible embodiment, the method for determining the similarity between the patient in the step S2 and the preset chronic patients in the patient library is as follows:
determining the index similarity of the patient and the preset chronic disease patient under different historical examination frequencies according to index data of the associated indexes of the patient under different historical examination frequencies, and determining the preset chronic disease patient as a similar chronic disease patient when the index similarity of the patient and the preset chronic disease patient under different historical examination frequencies is larger than a preset index similarity threshold value, and determining the similarity of the patient and the preset chronic disease patient in a patient library according to the average value of the index similarity under different historical examination frequencies;
determining index variation similarity of the patient and the preset chronic patient under different historical examination frequencies according to variation conditions of associated indexes of the patient under different historical examination frequencies, and determining comprehensive similarity of the patient and the preset chronic patient under different historical examination frequencies by combining the index similarity;
when the comprehensive similarity of the patient and the preset chronic disease patient under different historical examination frequencies is larger than a preset comprehensive similarity threshold value, determining that the preset chronic disease patient is a similar chronic disease patient, and determining the similarity of the patient and the preset chronic disease patient in a patient library through the average value of the comprehensive similarity under different historical examination frequencies;
when the comprehensive similarity between the patient and the preset chronic disease patient is not more than the historical examination frequency of the preset comprehensive similarity threshold, determining the number of the similar examination frequency of the patient based on the index similarity and the index variation similarity of the patient under different historical examination frequencies, and determining the similarity between the patient and the preset chronic disease patient by combining the comprehensive similarity of the patient under different historical examination frequencies.
In another possible embodiment, as shown in fig. 4, the method for determining the similarity between the patient in the step S2 and the preset chronic disease patient in the patient library is as follows:
s21, determining the index similarity of the patient and the preset chronic disease patient under different historical examination frequencies according to the index data of the associated indexes of the patient under different historical examination frequencies, judging whether the historical examination frequencies of the patient and the preset chronic disease patient with the index similarity larger than a preset index similarity threshold meet the requirements or not, if so, entering the next step, and if not, entering the step S23;
s22, determining index change similarity of the patient and the preset chronic disease patient under different historical examination frequencies according to change conditions of associated indexes of the patient under different historical examination frequencies, and determining comprehensive similarity of the patient and the preset chronic disease patient under different historical examination frequencies by combining the index similarity;
s23, judging whether the historical examination frequency of the patient with the comprehensive similarity greater than a preset index similarity threshold meets the requirement or not, if so, determining that the preset chronic patient is a similar chronic patient, determining the similarity of the patient with the preset chronic patient in a patient library through the average value of the comprehensive similarity under different historical examination frequencies, and if not, entering the next step;
s24, determining the number of similar examination frequencies of the patient based on the index similarity and the index variation similarity of the patient under different historical examination frequencies, and determining the similarity of the patient and the preset chronic disease patient by combining the comprehensive similarity of the patient under different historical examination frequencies.
Further, when the similarity of the preset chronic patients is greater than the preset similarity, determining that the preset chronic patients are similar chronic patients.
S3, acquiring disease development data of different similar chronic patients in a preset time period in the future, determining correction severity of the patients by combining the similarity between the different similar chronic patients and the disease severity, and dividing the patients into concerned patients and other patients according to the correction severity;
it should be noted that the preset time period is determined according to the severity of the illness of the patient, wherein the higher the severity of the illness of the patient is, the longer the preset time period is.
In one possible embodiment, as shown in fig. 5, the method for determining the correction of the disease degree in the step S3 is as follows:
determining the fluctuation amount of the associated indexes of different similar chronic patients in a future preset time period based on the disease development data of the different similar chronic patients in the future preset time period, and determining the index development abnormal values of the different similar chronic patients by combining the number of the associated indexes developed to the preset severe interval and the index data;
and determining the number of abnormally-varied patients in the similar chronic patients based on the index development abnormal values, determining the disease degree correction by combining the index development abnormal values of different similar chronic patients and the similarity of the similar chronic patients, and determining the corrected disease degree of the patients according to the disease degree correction and the disease severity.
Specifically, the disease development data includes the fluctuation amounts of different associated indexes and the number of associated indexes developed to a preset severe interval.
It will be appreciated that classifying patients into patients of interest and other patients according to correction severity includes:
judging whether the correction severity of the patient is in a preset severity range, if so, taking the patient as other patients, and if not, taking the patient as a patient concerned.
S4, determining the chronic disease back diagnosis management doctor of the hospital through data of the concerned patient corresponding to the chronic disease to be managed by the hospital and data of other patients.
In one possible embodiment, as shown in fig. 6, the method for determining the chronic disease review manager of the hospital in step S4 is as follows:
determining the comprehensive severity of the concerned patients corresponding to the chronic diseases according to the number of concerned patients corresponding to the chronic diseases which need to be managed by the hospital and the corrected severity of different concerned patients;
determining a combined severity of the other patients corresponding to the chronic disease based on the number of other patients and the corrected severity of the different other patients;
the management difficulty of the chronic diseases of the hospital is determined based on the comprehensive severity of other patients and the comprehensive severity of the concerned patients, and the number of the back-diagnosis management doctors of the chronic diseases of the hospital is determined through the management difficulty.
In another possible embodiment, the method for determining the chronic disease review manager of the hospital in the step S4 is as follows:
s41, determining the number of patients with the chronic diseases based on the number of concerned patients corresponding to the chronic diseases to be managed by the hospital and the number of other patients, and determining the basic number of the chronic disease back-diagnosis management doctors of the hospital according to the number of the patients;
s42, judging whether the number proportion of the concerned patients corresponding to the chronic diseases to be managed by the hospital is larger than a preset number proportion, if so, entering a step S44, and if not, entering a step S43;
s43, judging whether the average value of correction severity of the patient corresponding to the chronic disease to be managed by the hospital meets the requirement, if so, taking the basic quantity as the quantity of the back-diagnosis management doctors of the chronic disease of the hospital, and if not, entering the next step;
s44, determining the comprehensive severity of the concerned patients corresponding to the chronic diseases according to the number of concerned patients corresponding to the chronic diseases to be managed by the hospital and the corrected severity of different concerned patients, judging whether the comprehensive severity of the concerned patients corresponding to the chronic diseases meets the requirement, if so, taking the basic number as the number of the consultation management doctors of the chronic diseases of the hospital, and if not, entering the next step;
s45, determining the comprehensive severity of other patients corresponding to the chronic diseases based on the number of the other patients and the correction severity of different other patients, determining the management difficulty of the chronic diseases of the hospital based on the comprehensive severity of other patients and the comprehensive severity of concerned patients, and correcting the basic number through the management difficulty to obtain the number of the back-diagnosis management doctors of the chronic diseases of the hospital.
In a second aspect, as shown in FIG. 7, the present invention provides a computer system comprising: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: the processor executes a chronic disease management method based on big data analysis as described above when running the computer program.
The chronic disease management method based on big data analysis specifically comprises the following steps:
obtaining an examination index which is related to chronic diseases in examination indexes of a patient, taking the examination index as a related index, determining the severity of the patient according to index data of the related index and the change condition of the index data, and entering a next step when the patient is determined not to belong to a patient concerned based on the severity of the patient;
determining the index similarity of the patient and the preset chronic disease patient under different historical examination frequencies according to index data of the associated indexes of the patient under different historical examination frequencies, and determining the preset chronic disease patient as a similar chronic disease patient when the index similarity of the patient and the preset chronic disease patient under different historical examination frequencies is larger than a preset index similarity threshold value, and determining the similarity of the patient and the preset chronic disease patient in a patient library according to the average value of the index similarity under different historical examination frequencies;
determining index variation similarity of the patient and the preset chronic patient under different historical examination frequencies according to variation conditions of associated indexes of the patient under different historical examination frequencies, and determining comprehensive similarity of the patient and the preset chronic patient under different historical examination frequencies by combining the index similarity;
when the comprehensive similarity of the patient and the preset chronic disease patient under different historical examination frequencies is larger than a preset comprehensive similarity threshold value, determining that the preset chronic disease patient is a similar chronic disease patient, and determining the similarity of the patient and the preset chronic disease patient in a patient library through the average value of the comprehensive similarity under different historical examination frequencies;
when the comprehensive similarity between the patient and the preset chronic disease patient is not more than the historical examination frequency of the preset comprehensive similarity threshold value, determining the number of similar examination frequencies of the patient based on the index similarity and the index variation similarity of the patient under different historical examination frequencies, determining the similarity between the patient and the preset chronic disease patient by combining the comprehensive similarity of the patient under different historical examination frequencies, and determining the preset chronic disease patient as a similar chronic disease patient when the similarity between the preset chronic disease patient is more than the preset similarity;
acquiring disease development data of different similar chronic patients in a future preset time period, determining correction severity of the patients by combining the similarity between the different similar chronic patients and the disease severity, and dividing the patients into concerned patients and other patients according to the correction severity;
determining the comprehensive severity of the concerned patients corresponding to the chronic diseases according to the number of concerned patients corresponding to the chronic diseases which need to be managed by the hospital and the corrected severity of different concerned patients;
determining a combined severity of the other patients corresponding to the chronic disease based on the number of other patients and the corrected severity of the different other patients;
the management difficulty of the chronic diseases of the hospital is determined based on the comprehensive severity of other patients and the comprehensive severity of the concerned patients, and the number of the back-diagnosis management doctors of the chronic diseases of the hospital is determined through the management difficulty.
Through the above embodiments, the present invention has the following beneficial effects:
1. the disease severity of the patient is determined through the index data of the associated index and the variation condition of the index data, so that the disease severity is estimated from the two angles of the abnormal condition of the index data of the associated index and the variation condition of the index data, the technical problem that the disease severity is estimated inaccurately due to the single abnormal condition of the index data is avoided, the accuracy of the disease severity estimation is improved, and a foundation is laid for the differential consultation management doctor to determine.
2. The similarity between the patient and the preset chronic disease patients in the patient library is determined through the index data of the associated indexes of the patient under different historical examination frequencies and the change condition of the associated indexes under different historical examination frequencies, so that the determination of the similar chronic disease patients of the patient from the two angles of the index data and the change condition under different historical examination frequencies is realized, further, the evaluation of the future development condition of the chronic disease of the patient can be accurately realized, and a foundation is laid for accurately realizing the evaluation of the severity degree of the chronic disease of the patient.
3. The data of the concerned patients corresponding to the chronic diseases to be managed in the hospital and the data of other patients are used for determining the back diagnosis management doctors of the chronic diseases in the hospital, so that the technical problem that the efficiency and timeliness of the back diagnosis management cannot meet the requirements due to the fact that the number of the original back diagnosis management doctors is fixed is avoided, the determination of the number of the back diagnosis management doctors from the aspects of the severity and the number of the diseases is realized, and the reliable back diagnosis management of the patients with the chronic diseases is realized.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, devices, non-volatile computer storage medium embodiments, the description is relatively simple, as it is substantially similar to method embodiments, with reference to the section of the method embodiments being relevant.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing is merely one or more embodiments of the present description and is not intended to limit the present description. Various modifications and alterations to one or more embodiments of this description will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, or the like, which is within the spirit and principles of one or more embodiments of the present description, is intended to be included within the scope of the claims of the present description.
Claims (10)
1. The chronic disease management method based on big data analysis is characterized by comprising the following steps:
obtaining an examination index which is related to chronic diseases in examination indexes of a patient, taking the examination index as a related index, determining the severity of the patient according to index data of the related index and the change condition of the index data, and entering a next step when the patient is determined not to belong to a patient concerned based on the severity of the patient;
determining index data of the associated indexes of the patient under different historical examination frequencies based on the examination records, and determining the similarity between the patient and a preset chronic patient in a patient library and similar chronic patients by combining the change condition of the associated indexes under different historical examination frequencies;
acquiring disease development data of different similar chronic patients in a future preset time period, determining correction severity of the patients by combining the similarity between the different similar chronic patients and the disease severity, and dividing the patients into concerned patients and other patients according to the correction severity;
the determination of the doctor for the back diagnosis management of the chronic diseases of the hospital is performed through the data of the concerned patients corresponding to the chronic diseases which need to be managed by the hospital and the data of other patients.
2. The chronic disease management method based on big data analysis according to claim 1, wherein the examination index of the patient is determined based on the self-test result of the patient or the examination result of a medical structure.
3. The method for chronic disease management based on big data analysis according to claim 1, wherein the correlation index is determined according to the type of chronic disease of the patient, in particular by the matching result of the type of chronic disease of the patient.
4. The method for chronic disease management based on big data analysis according to claim 1, wherein when it is determined that the patient belongs to a patient of interest based on the severity of the disease, then the patient is determined to be a patient of interest, and the severity of the disease of the patient is taken as a corrected severity of the patient.
5. The method of claim 1, wherein the method of determining the severity of a disease in a patient is:
determining the associated index of the patient in different serious intervals according to the index data of the associated index of the patient, determining the index severity of different serious intervals by combining the index data of different associated indexes, and determining the index problem assessment of the patient according to the index severity of different serious intervals;
determining the fluctuation amount and the fluctuation rate of the index data of the different associated indexes of the patient based on the fluctuation situation of the index data of the associated indexes of the patient, and determining the index fluctuation assessment of the patient according to the fluctuation amount and the fluctuation rate of the index data of the different associated indexes of the patient;
the patient's severity of the illness is determined by an index change assessment and an index problem assessment of the patient.
6. The method for chronic disease management based on big data analysis according to claim 5, wherein determining the associated index of the patient in different severe intervals is performed by index data of the associated index of the patient, specifically comprising:
and determining a data range of the index data of the associated index according to the index data, and determining a severe section of the associated index based on the data range.
7. The method for chronic disease management based on big data analysis according to claim 1, wherein the examination record is determined based on an uploaded record of the patient's historical examination data or a read result of the patient's historical examination data at a medical facility.
8. The method for chronic disease management based on big data analysis according to claim 1, wherein the method for determining the similarity between the patient and the preset chronic disease patient in the patient library is:
determining the index similarity of the patient and the preset chronic patient under different historical examination frequencies according to the index data of the associated indexes of the patient under different historical examination frequencies;
determining index variation similarity of the patient and the preset chronic patient under different historical examination frequencies according to variation conditions of associated indexes of the patient under different historical examination frequencies;
and determining the number of similar examination frequencies of the patient based on the index similarity and the index variation similarity of the patient under different historical examination frequencies, and determining the similarity of the patient and the preset chronic disease patient by combining the index similarity and the index variation similarity of the patient under different historical examination frequencies.
9. The chronic disease management method based on big data analysis according to claim 1, wherein the method for determining the doctor for the diagnosis back of chronic disease in the hospital is as follows:
s41, determining the number of patients with the chronic diseases based on the number of concerned patients corresponding to the chronic diseases to be managed by the hospital and the number of other patients, and determining the basic number of the chronic disease back-diagnosis management doctors of the hospital according to the number of the patients;
s42, judging whether the number proportion of the concerned patients corresponding to the chronic diseases to be managed by the hospital is larger than a preset number proportion, if so, entering a step S44, and if not, entering a step S43;
s43, judging whether the average value of correction severity of the patient corresponding to the chronic disease to be managed by the hospital meets the requirement, if so, taking the basic quantity as the quantity of the back-diagnosis management doctors of the chronic disease of the hospital, and if not, entering the next step;
s44, determining the comprehensive severity of the concerned patients corresponding to the chronic diseases according to the number of concerned patients corresponding to the chronic diseases to be managed by the hospital and the corrected severity of different concerned patients, judging whether the comprehensive severity of the concerned patients corresponding to the chronic diseases meets the requirement, if so, taking the basic number as the number of the consultation management doctors of the chronic diseases of the hospital, and if not, entering the next step;
s45, determining the comprehensive severity of other patients corresponding to the chronic diseases based on the number of the other patients and the correction severity of different other patients, determining the management difficulty of the chronic diseases of the hospital based on the comprehensive severity of other patients and the comprehensive severity of concerned patients, and correcting the basic number through the management difficulty to obtain the number of the back-diagnosis management doctors of the chronic diseases of the hospital.
10. A computer system, comprising: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: the processor, when running the computer program, performs a chronic disease management method based on big data analysis as claimed in any of claims 1-9.
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