Remote medical health monitoring data submission sorting method and system
By calculating the comprehensive evaluation coefficient of the patient and establishing the sorting priority, the problem of inflexible data sorting in telemedicine is solved, and the flexibility of data display and the work efficiency of doctors are improved.
Patent Information
- Application Number
- CN202510585337.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-08
- Publication Date
- 2025-09-26
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
In the existing technology, doctors cannot flexibly sort patient health monitoring data according to its importance in telemedicine, resulting in low work efficiency.
By collecting patients' vital signs data, symptom description data and treatment feedback data, the comprehensive evaluation coefficient is calculated using the evaluation coefficient formula, and then the sorting priority is constructed, and the data is integrated and displayed according to the priority.
It realizes flexible sorting according to the importance of patient health monitoring data, improves the flexibility of data display and the work efficiency of doctors.
Smart Images
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Figure FT_2 
Figure FDA0005391542660000011
Abstract
Description
Technical Field
[0001] The present application relates to the field of data analysis technology, and in particular to a method and system for sorting submission of remote medical health monitoring data. Background Art
[0002] With the development and application of computer and network technology in various fields, modern hospitals have established their own local area networks. Doctors can collect and display various health signs data and the latest medical information of remote patients through the network, keep abreast of patients' health information, and provide patients with good medical and health services.
[0003] In related technologies, doctors can only submit data to the server in the order of the patient's submission time. As a result, when doctors review the patient's health monitoring data, the data is also displayed in the order of submission time. This makes it impossible to display the patient's health monitoring data according to its importance, and queries under conditional constraints are still required. This not only reduces the flexibility of sorting, but also reduces work efficiency, and there is room for improvement. Summary of the Invention
[0004] In response to the deficiencies of the existing technology, the present application provides a method and system for sorting submission of remote medical health monitoring data.
[0005] In the first aspect, the present application provides a remote medical health monitoring data submission and sorting system, which adopts the following technical solutions:
[0006] A remote medical health monitoring data submission and sorting system, comprising:
[0007] Data collection module, used to collect and submit health monitoring data corresponding to target patients;
[0008] A preliminary analysis module is used to perform preliminary analysis on the health monitoring data corresponding to the target patient, and then confirm the evaluation coefficient corresponding to the health monitoring data of the target patient;
[0009] The priority division module is used to analyze and process the evaluation coefficients corresponding to the health monitoring data of the target patients, and to establish a sorting priority for the health monitoring data corresponding to the target patients based on the results of the analysis and processing;
[0010] The data integration module is used to integrate the health monitoring data corresponding to the target patients according to the sorting priority, and generate a target data list according to the integration result.
[0011] Preferably, the health monitoring data corresponding to the target patient includes vital sign data, symptom description data and treatment feedback data.
[0012] Preferably, the process of performing a preliminary analysis on the vital sign data corresponding to the target patient and confirming the vital sign evaluation coefficient corresponding to the target patient specifically includes:
[0013] The target patient's corresponding vital sign data is collected remotely in real time through the monitoring device worn by the target patient, and the target patient's corresponding heart rate data Hr, systolic blood pressure data Bpsys, diastolic blood pressure data Bpdia and respiratory rate data Br are extracted from the target patient's corresponding vital sign data, and the target patient's corresponding body temperature data T is confirmed;
[0014] By formula Confirm the physical sign assessment coefficient Vsac corresponding to the target patient, where Hr′, Bp′sys, Bp′dia, Br′, and T′ represent the preset standard heart rate data, standard systolic blood pressure data, standard diastolic blood pressure data, standard respiratory rate data, and standard body temperature data, respectively; ω1, ω2, ω3, ω4, and ω5 represent the preset weight coefficients, respectively; and e represents a natural constant.
[0015] Preferably, the process of performing a preliminary analysis on the symptom description data corresponding to the target patient and confirming the symptom assessment coefficient corresponding to the target patient specifically includes:
[0016] Acquire the symptom description data corresponding to the target patient, and confirm the symptom severity data, symptom frequency data and symptom duration data corresponding to the target patient from the symptom description data corresponding to the target patient, and confirm the symptom severity index Isev, symptom frequency index Ffre and symptom duration index Ddur corresponding to the target patient according to the symptom severity data, symptom frequency data and symptom duration data corresponding to the target patient respectively;
[0017] By formula The symptom assessment coefficient Sdac corresponding to the target patient is confirmed, where N is represented as a normalization coefficient and is obtained by fitting historical data.
[0018] Preferably, the process of performing a preliminary analysis on the treatment feedback data corresponding to the target patient and then confirming the treatment evaluation coefficient corresponding to the target patient specifically includes:
[0019] Confirm the treatment feedback data corresponding to the target patient, and extract the symptom score data Safter, quality of life score data Qafter and side effect score data Esev corresponding to the target patient after treatment from the treatment feedback data corresponding to the target patient, and confirm the treatment duration data Mdur corresponding to the target patient;
[0020] Confirm the corresponding symptom score data Sbefore and quality of life score data Qbefore of the target patient before treatment from the cloud database;
[0021] By formula Identify the treatment assessment coefficient Tfac corresponding to the target patient.
[0022] Preferably, the evaluation coefficients corresponding to the target patient's health monitoring data are analyzed and processed, and a ranking priority is established for the health monitoring data corresponding to the target patient based on the results of the analysis and processing, specifically including:
[0023] Substitute the target patient's corresponding physical sign assessment coefficient Vsac, the target patient's corresponding symptom assessment coefficient Sdac, and the target patient's corresponding treatment assessment coefficient Tfac into the formula β = Vsac * ψ1 + Sdac * ψ2 + Tfac * ψ3 to calculate the target patient's corresponding comprehensive assessment coefficient β, where ψ1, ψ2, and ψ3 represent the weight coefficients corresponding to the physical sign assessment coefficient, the symptom assessment coefficient, and the treatment assessment coefficient, respectively;
[0024] Compare the comprehensive evaluation coefficient β corresponding to the target patient with the preset comprehensive evaluation threshold interval [β1, β2];
[0025] If the comprehensive evaluation coefficient β corresponding to the target patient is less than β1, the health monitoring data corresponding to the target patient is determined to be of low ranking priority;
[0026] If the comprehensive evaluation coefficient β corresponding to the target patient is between [β1, β2], the health monitoring data corresponding to the target patient is determined to be of medium ranking priority;
[0027] If the comprehensive evaluation coefficient β corresponding to the target patient is greater than β2, the health monitoring data corresponding to the target patient is determined to be of high ranking priority.
[0028] Preferably, the health monitoring data determined to be of medium priority is analyzed, specifically including:
[0029] Confirm the prioritized health monitoring data and obtain the corresponding physical sign assessment coefficient, symptom assessment coefficient and treatment assessment coefficient for the target patients;
[0030] Confirm the secondary sorting priority according to the sign assessment coefficient, symptom assessment coefficient and treatment assessment coefficient corresponding to the target patient;
[0031] If the secondary sorting priority is set to the symptom assessment coefficient, the symptom assessment coefficient corresponding to the target patient is compared with the preset symptom assessment threshold. If the symptom assessment coefficient corresponding to the target patient is greater than the preset symptom assessment threshold, the health monitoring data is submitted and sorted according to the symptom assessment coefficient corresponding to the target patient. Otherwise, the health monitoring data is submitted and sorted according to the comprehensive assessment coefficient corresponding to the target patient.
[0032] If the secondary sorting priority is set to the physical sign assessment coefficient, the physical sign assessment coefficient corresponding to the target patient is compared with the preset physical sign assessment threshold. If the physical sign assessment coefficient corresponding to the target patient is greater than the preset physical sign assessment threshold, the health monitoring data is submitted and sorted according to the physical sign assessment coefficient corresponding to the target patient. Otherwise, the health monitoring data is submitted and sorted according to the comprehensive assessment coefficient corresponding to the target patient.
[0033] If the secondary sorting priority is set to the treatment evaluation coefficient, the treatment evaluation coefficient corresponding to the target patient is compared with the preset treatment evaluation threshold. If the treatment evaluation coefficient corresponding to the target patient is greater than the preset treatment evaluation threshold, the health monitoring data is submitted and sorted according to the treatment evaluation coefficient corresponding to the target patient. Otherwise, the health monitoring data is submitted and sorted according to the comprehensive evaluation coefficient corresponding to the target patient.
[0034] In a second aspect, the present application provides a method for sorting submission of remote medical health monitoring data, which adopts the following technical solution:
[0035] A method for sorting remote medical health monitoring data submission includes the following steps:
[0036] Collect and submit health monitoring data corresponding to target patients;
[0037] Conduct a preliminary analysis of the health monitoring data corresponding to the target patient, and then confirm the evaluation coefficient corresponding to the health monitoring data of the target patient;
[0038] Analyze and process the evaluation coefficients corresponding to the target patient's health monitoring data, and establish a ranking priority for the target patient's health monitoring data based on the analysis and processing results;
[0039] The health monitoring data corresponding to the target patients are integrated according to the sorting priorities, and a target data list is generated according to the integration results.
[0040] In a third aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute any one of the above-mentioned remote medical health monitoring data submission and sorting systems.
[0041] In summary, this application includes at least one of the following beneficial technical effects:
[0042] 1. The present invention provides a remote medical health monitoring data submission and sorting system. The system collects and submits vital sign data, symptom description data, and treatment feedback data corresponding to target patients, performs preliminary analysis on the vital sign data, symptom description data, and treatment feedback data corresponding to the target patients, and then determines the comprehensive evaluation coefficient corresponding to the target patient's health monitoring data. The comprehensive evaluation coefficient corresponding to the target patient is compared with a preset comprehensive evaluation threshold interval to determine the sorting priority corresponding to the target patient's health monitoring data. The health monitoring data corresponding to the target patient is integrated according to the sorting priority to generate a target data list. The data list can then be displayed according to the importance of the patient's health monitoring data, thereby effectively improving the flexibility of data sorting and thus effectively improving the work efficiency of doctors.
[0043] 2. By setting the secondary sorting priority, the health monitoring data with medium sorting priority can be sorted again, so that the data list can be displayed according to the importance of the patient's health monitoring data, thereby effectively improving the flexibility of data sorting. BRIEF DESCRIPTION OF THE DRAWINGS
[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0045] Figure 1 This is a system diagram for sorting submission of remote medical health monitoring data in an embodiment of the present application.
[0046] Figure 2 This is a flow chart of the method for submitting and sorting remote medical health monitoring data in an embodiment of the present application. DETAILED DESCRIPTION
[0047] The following is combined with Figure 1-2 This application is described in further detail.
[0048] Example 1
[0049] The present application discloses a remote medical health monitoring data submission and sorting system.
[0050] Reference Figure 1 , a remote medical health monitoring data submission and sorting system, comprising:
[0051] Data collection module, used to collect and submit health monitoring data corresponding to target patients;
[0052] A preliminary analysis module is used to perform preliminary analysis on the health monitoring data corresponding to the target patient, and then confirm the evaluation coefficient corresponding to the health monitoring data of the target patient;
[0053] The priority division module is used to analyze and process the evaluation coefficients corresponding to the health monitoring data of the target patients, and to establish a sorting priority for the health monitoring data corresponding to the target patients based on the results of the analysis and processing;
[0054] The data integration module is used to integrate the health monitoring data corresponding to the target patients according to the sorting priority, and generate a target data list according to the integration result.
[0055] Through the above technical solution, by collecting and submitting the health monitoring data corresponding to the target patient, and conducting a preliminary analysis on the health monitoring data corresponding to the target patient, the evaluation coefficient corresponding to the health monitoring data of the target patient is confirmed, and the evaluation coefficient corresponding to the health monitoring data of the target patient is analyzed and processed, and a sorting priority is constructed for the health monitoring data corresponding to the target patient according to the results of the analysis and processing, and the health monitoring data corresponding to the target patient is integrated according to the sorting priority, and then a target data list is generated, so that the data list can be displayed according to the importance of the patient's health monitoring data, thereby effectively improving the flexibility of data sorting, thereby effectively improving the work efficiency of doctors.
[0056] Furthermore, the health monitoring data corresponding to the target patient includes vital sign data, symptom description data and treatment feedback data.
[0057] Specifically, in the embodiment of the present application, the vital sign data corresponding to the target patient include but are not limited to heart rate data, systolic blood pressure data, diastolic blood pressure data, respiratory rate data and body temperature data;
[0058] Symptom description data corresponding to the target patient include but are not limited to symptom severity data, symptom frequency data, and symptom duration data;
[0059] The treatment feedback data corresponding to the target patient includes but is not limited to symptom score data Safter, quality of life score data, side effect score data and treatment duration data.
[0060] It should be noted that the process of conducting a preliminary analysis of the vital sign data corresponding to the target patient and confirming the vital sign assessment coefficient corresponding to the target patient specifically includes:
[0061] The target patient's corresponding vital sign data is collected remotely in real time through the monitoring device worn by the target patient, and the target patient's corresponding heart rate data Hr, systolic blood pressure data Bpsys, diastolic blood pressure data Bpdia and respiratory rate data Br are extracted from the target patient's corresponding vital sign data, and the target patient's corresponding body temperature data T is confirmed;
[0062] By formula Confirm the physical sign assessment coefficient Vsac corresponding to the target patient, where Hr′, Bp′sys, Bp′dia, Br′, and T′ represent the preset standard heart rate data, standard systolic blood pressure data, standard diastolic blood pressure data, standard respiratory rate data, and standard body temperature data, respectively; ω1, ω2, ω3, ω4, and ω5 represent the preset weight coefficients, respectively; and e represents a natural constant.
[0063] It should be noted that the process of conducting a preliminary analysis of the symptom description data corresponding to the target patient and confirming the symptom assessment coefficient corresponding to the target patient specifically includes:
[0064] Acquire the symptom description data corresponding to the target patient, and confirm the symptom severity data, symptom frequency data and symptom duration data corresponding to the target patient from the symptom description data corresponding to the target patient, and confirm the symptom severity index Isev, symptom frequency index Ffre and symptom duration index Ddur corresponding to the target patient according to the symptom severity data, symptom frequency data and symptom duration data corresponding to the target patient respectively;
[0065] By formula The symptom assessment coefficient Sdac corresponding to the target patient is confirmed, where N is represented by a normalization coefficient, which can be obtained by fitting historical data. N is used to adjust the symptom assessment coefficient corresponding to the target patient to a specific range for subsequent analysis and processing.
[0066] Specifically, the symptom severity is graded according to the symptom severity data corresponding to the target patient. For example, if the symptom severity is mild, the symptom severity index corresponding to the target patient is set to 1; if the symptom severity is moderate, the symptom severity index corresponding to the target patient is set to 2; if the symptom severity is severe, the symptom severity index corresponding to the target patient is set to 3;
[0067] Specifically, the symptom frequency is graded according to the symptom frequency data corresponding to the target patient. For example, if the symptom frequency is occasional, the symptom frequency index corresponding to the target patient is set to 1; if the symptom frequency is frequent, the symptom frequency index corresponding to the target patient is set to 2; if the symptom frequency is continuous, the symptom frequency index corresponding to the target patient is set to 3;
[0068] Specifically, the symptom duration is graded according to the symptom duration data corresponding to the target patient. For example, if the symptom duration is short, the symptom duration index corresponding to the target patient is set to 1; if the symptom frequency is medium duration, the symptom duration index corresponding to the target patient is set to 2; if the symptom frequency is long-term duration, the symptom duration index corresponding to the target patient is set to 3.
[0069] It should be noted that the process of conducting a preliminary analysis of the treatment feedback data corresponding to the target patient and then confirming the treatment evaluation coefficient corresponding to the target patient specifically includes:
[0070] Confirm the treatment feedback data corresponding to the target patient, and extract the symptom score data Safter, quality of life score data Qafter and side effect score data Esev corresponding to the target patient after treatment from the treatment feedback data corresponding to the target patient, and confirm the treatment duration data Mdur corresponding to the target patient;
[0071] Confirm the corresponding symptom score data Sbefore and quality of life score data Qbefore of the target patient before treatment from the cloud database;
[0072] By formula Identify the treatment assessment coefficient Tfac corresponding to the target patient.
[0073] Specifically, through the formula Identify the treatment evaluation coefficient Tfac corresponding to the target patient, where (Sbefore-Safter) represents the degree of symptom improvement, and a larger value indicates a more significant symptom improvement; (Qbefore-Qafter) represents the degree of improvement in quality of life, and a larger value indicates a more significant improvement in quality of life; Esev represents the severity of side effects, and a larger value indicates a more severe side effect.
[0074] It should be noted that the evaluation coefficients corresponding to the target patient's health monitoring data are analyzed and processed, and a ranking priority is established for the health monitoring data corresponding to the target patient based on the results of the analysis and processing, specifically including:
[0075] Substitute the target patient's corresponding physical sign assessment coefficient Vsac, the target patient's corresponding symptom assessment coefficient Sdac, and the target patient's corresponding treatment assessment coefficient Tfac into the formula β = Vsac * ψ1 + Sdac * ψ2 + Tfac * ψ3 to calculate the target patient's corresponding comprehensive assessment coefficient β, where ψ1, ψ2, and ψ3 represent the weight coefficients corresponding to the physical sign assessment coefficient, the symptom assessment coefficient, and the treatment assessment coefficient, respectively;
[0076] Compare the comprehensive evaluation coefficient β corresponding to the target patient with the preset comprehensive evaluation threshold interval [β1, β2];
[0077] If the comprehensive evaluation coefficient β corresponding to the target patient is less than β1, the health monitoring data corresponding to the target patient is determined to be of low ranking priority;
[0078] If the comprehensive evaluation coefficient β corresponding to the target patient is between [β1, β2], the health monitoring data corresponding to the target patient is determined to be of medium ranking priority;
[0079] If the comprehensive evaluation coefficient β corresponding to the target patient is greater than β2, the health monitoring data corresponding to the target patient is determined to be of high ranking priority.
[0080] Specifically, health monitoring data with low sorting priority is stored in the first data list, health monitoring data with medium sorting priority is stored in the second data list, and health monitoring data with high sorting priority is stored in the third data list. The sorting order of the above data lists is based on the comprehensive evaluation coefficient corresponding to the target patient, from high to low.
[0081] It should be noted that the analysis of health monitoring data determined to be of medium priority includes:
[0082] Confirm the prioritized health monitoring data and obtain the corresponding physical sign assessment coefficient, symptom assessment coefficient and treatment assessment coefficient for the target patients;
[0083] Confirm the secondary sorting priority according to the sign assessment coefficient, symptom assessment coefficient and treatment assessment coefficient corresponding to the target patient;
[0084] If the secondary sorting priority is set to the symptom assessment coefficient, the symptom assessment coefficient corresponding to the target patient is compared with the preset symptom assessment threshold. If the symptom assessment coefficient corresponding to the target patient is greater than the preset symptom assessment threshold, the health monitoring data is submitted and sorted according to the symptom assessment coefficient corresponding to the target patient. Otherwise, the health monitoring data is submitted and sorted according to the comprehensive assessment coefficient corresponding to the target patient.
[0085] For example, if the symptom assessment coefficients corresponding to target patients No. 1, No. 2, No. 3, and No. 4 are 11, 13, 17, and 19 respectively; the comprehensive assessment coefficients corresponding to target patients No. 1, No. 2, No. 3, and No. 4 are 20, 23, 28, and 26, and the preset symptom assessment threshold is 15, then the symptom assessment coefficients corresponding to target patients No. 3 and No. 4 are greater than the preset symptom assessment threshold, and the symptom assessment coefficient corresponding to target patient No. 4 is greater than the symptom assessment coefficient corresponding to target patient No. 3, then the health monitoring data of target patient No. 4 is submitted for sorting first, and since the comprehensive assessment coefficient corresponding to target patient No. 2 is greater than the comprehensive assessment coefficient corresponding to target patient No. 1, the health monitoring data of target patient No. 2 is submitted for sorting.
[0086] If the secondary sorting priority is set to the physical sign assessment coefficient, the physical sign assessment coefficient corresponding to the target patient is compared with the preset physical sign assessment threshold. If the physical sign assessment coefficient corresponding to the target patient is greater than the preset physical sign assessment threshold, the health monitoring data is submitted and sorted according to the physical sign assessment coefficient corresponding to the target patient. Otherwise, the health monitoring data is submitted and sorted according to the comprehensive assessment coefficient corresponding to the target patient.
[0087] If the secondary sorting priority is set to the treatment evaluation coefficient, the treatment evaluation coefficient corresponding to the target patient is compared with the preset treatment evaluation threshold. If the treatment evaluation coefficient corresponding to the target patient is greater than the preset treatment evaluation threshold, the health monitoring data is submitted and sorted according to the treatment evaluation coefficient corresponding to the target patient. Otherwise, the health monitoring data is submitted and sorted according to the comprehensive evaluation coefficient corresponding to the target patient.
[0088] Example 2
[0089] The embodiment of the present application also discloses a method for sorting submission of remote medical health monitoring data.
[0090] Reference Figure 2 , a remote medical health monitoring data submission sorting method, comprising the following steps:
[0091] Collect and submit health monitoring data corresponding to target patients;
[0092] Conduct a preliminary analysis of the health monitoring data corresponding to the target patient, and then confirm the evaluation coefficient corresponding to the health monitoring data of the target patient;
[0093] Analyze and process the evaluation coefficients corresponding to the target patient's health monitoring data, and establish a ranking priority for the target patient's health monitoring data based on the analysis and processing results;
[0094] The health monitoring data corresponding to the target patients are integrated according to the sorting priorities, and a target data list is generated according to the integration results.
[0095] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.
[0096] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
[0097] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.
Claims
1. A remote medical health monitoring data submission and sorting system, characterized in that: include: Data collection module, used to collect and submit health monitoring data corresponding to target patients; A preliminary analysis module is used to perform preliminary analysis on the health monitoring data corresponding to the target patient, and then confirm the evaluation coefficient corresponding to the health monitoring data of the target patient; The priority division module is used to analyze and process the evaluation coefficients corresponding to the health monitoring data of the target patients, and to establish a sorting priority for the health monitoring data corresponding to the target patients based on the results of the analysis and processing; The data integration module is used to integrate the health monitoring data corresponding to the target patients according to the sorting priority, and generate a target data list according to the integration result.
2. A remote medical health monitoring data submission and sorting system according to claim 1, characterized in that: The health monitoring data corresponding to the target patient includes vital sign data, symptom description data and treatment feedback data.
3. A remote medical health monitoring data submission and sorting system according to claim 2, characterized in that: The process of conducting a preliminary analysis of the vital sign data corresponding to the target patient and confirming the vital sign assessment coefficient corresponding to the target patient includes: The target patient's corresponding vital sign data is collected remotely in real time through the monitoring device worn by the target patient, and the target patient's corresponding heart rate data Hr, systolic blood pressure data Bpsys, diastolic blood pressure data Bpdia and respiratory rate data Br are extracted from the target patient's corresponding vital sign data, and the target patient's corresponding body temperature data T is confirmed; By formula Confirm the physical sign assessment coefficient Vsac corresponding to the target patient, where Hr′, Bp′sys, Bp′dia, Br′, and T′ represent the preset standard heart rate data, standard systolic blood pressure data, standard diastolic blood pressure data, standard respiratory rate data, and standard body temperature data, respectively; ω1, ω2, ω3, ω4, and ω5 represent the preset weight coefficients, respectively; and e represents a natural constant.
4. A remote medical health monitoring data submission and sorting system according to claim 2, characterized in that: The process of conducting a preliminary analysis of the symptom description data corresponding to the target patient and confirming the symptom assessment coefficient corresponding to the target patient specifically includes: Acquire the symptom description data corresponding to the target patient, and confirm the symptom severity data, symptom frequency data and symptom duration data corresponding to the target patient from the symptom description data corresponding to the target patient, and confirm the symptom severity index Isev, symptom frequency index Ffre and symptom duration index Ddur corresponding to the target patient according to the symptom severity data, symptom frequency data and symptom duration data corresponding to the target patient respectively; By formula The symptom assessment coefficient Sdac corresponding to the target patient is confirmed, where N is represented as a normalization coefficient and is obtained by fitting historical data.
5. A remote medical health monitoring data submission and sorting system according to claim 2, characterized in that: The process of conducting a preliminary analysis of the treatment feedback data corresponding to the target patient and then confirming the treatment evaluation coefficient corresponding to the target patient includes: Confirm the treatment feedback data corresponding to the target patient, and extract the symptom score data Safter, quality of life score data Qafter and side effect score data Esev corresponding to the target patient after treatment from the treatment feedback data corresponding to the target patient, and confirm the treatment duration data Mdur corresponding to the target patient; Confirm the corresponding symptom score data Sbefore and quality of life score data Qbefore of the target patient before treatment from the cloud database; By formula Identify the treatment assessment coefficient Tfac corresponding to the target patient.
6. A remote medical health monitoring data submission and sorting system according to claim 5, characterized in that: Analyze and process the evaluation coefficients corresponding to the target patient's health monitoring data, and establish a ranking priority for the target patient's health monitoring data based on the analysis and processing results, specifically including: Substitute the target patient's corresponding physical sign assessment coefficient Vsac, the target patient's corresponding symptom assessment coefficient Sdac, and the target patient's corresponding treatment assessment coefficient Tfac into the formula β = Vsac * ψ1 + Sdac * ψ2 + Tfac * ψ3 to calculate the target patient's corresponding comprehensive assessment coefficient β, where ψ1, ψ2, and ψ3 represent the weight coefficients corresponding to the physical sign assessment coefficient, the symptom assessment coefficient, and the treatment assessment coefficient, respectively; Compare the comprehensive evaluation coefficient β corresponding to the target patient with the preset comprehensive evaluation threshold interval [β1, β2]; If the comprehensive evaluation coefficient β corresponding to the target patient is less than β1, the health monitoring data corresponding to the target patient is determined to be of low ranking priority; If the comprehensive evaluation coefficient β corresponding to the target patient is between [β1, β2], the health monitoring data corresponding to the target patient is determined to be of medium ranking priority; If the comprehensive evaluation coefficient β corresponding to the target patient is greater than β2, the health monitoring data corresponding to the target patient is determined to be of high ranking priority.
7. A remote medical health monitoring data submission and sorting system according to claim 6, characterized in that: Analyze the health monitoring data that are determined to be of medium priority, including: Confirm the prioritized health monitoring data and obtain the corresponding physical sign assessment coefficient, symptom assessment coefficient and treatment assessment coefficient for the target patients; Confirm the secondary sorting priority according to the sign assessment coefficient, symptom assessment coefficient and treatment assessment coefficient corresponding to the target patient; If the secondary sorting priority is set to the symptom assessment coefficient, the symptom assessment coefficient corresponding to the target patient is compared with the preset symptom assessment threshold. If the symptom assessment coefficient corresponding to the target patient is greater than the preset symptom assessment threshold, the health monitoring data is submitted and sorted according to the symptom assessment coefficient corresponding to the target patient. Otherwise, the health monitoring data is submitted and sorted according to the comprehensive assessment coefficient corresponding to the target patient. If the secondary sorting priority is set to the physical sign assessment coefficient, the physical sign assessment coefficient corresponding to the target patient is compared with the preset physical sign assessment threshold. If the physical sign assessment coefficient corresponding to the target patient is greater than the preset physical sign assessment threshold, the health monitoring data is submitted and sorted according to the physical sign assessment coefficient corresponding to the target patient. Otherwise, the health monitoring data is submitted and sorted according to the comprehensive assessment coefficient corresponding to the target patient. If the secondary sorting priority is set to the treatment evaluation coefficient, the treatment evaluation coefficient corresponding to the target patient is compared with the preset treatment evaluation threshold. If the treatment evaluation coefficient corresponding to the target patient is greater than the preset treatment evaluation threshold, the health monitoring data is submitted and sorted according to the treatment evaluation coefficient corresponding to the target patient. Otherwise, the health monitoring data is submitted and sorted according to the comprehensive evaluation coefficient corresponding to the target patient.
8. A method for sorting remote medical health monitoring data submission, applied to a system for sorting remote medical health monitoring data submission according to claims 1-7, characterized in that: The following steps are involved: Collect and submit health monitoring data corresponding to target patients; Conduct a preliminary analysis of the health monitoring data corresponding to the target patient, and then confirm the evaluation coefficient corresponding to the health monitoring data of the target patient; Analyze and process the evaluation coefficients corresponding to the target patient's health monitoring data, and establish a ranking priority for the target patient's health monitoring data based on the analysis and processing results; The health monitoring data corresponding to the target patients are integrated according to the sorting priorities, and a target data list is generated according to the integration results.
9. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are run on a computer, the computer is caused to execute a remote medical health monitoring data submission and sorting system as claimed in any one of claims 1 to 7.