Postoperative rehabilitation nursing management system based on data analysis

Abstract

The invention relates to the technical field of postoperative rehabilitation nursing management systems, in particular to a postoperative rehabilitation nursing management system based on data analysis, which comprises a data acquisition module; the physiological health analysis module is used for generating a health state evaluation result based on the physiological data, and the physiological health analysis module comprises a dynamic weighted information entropy collaborative matrix, nonlinear smooth prediction, multi-level abnormal deviation detection, personalized dynamic threshold adaptation and progressive health state grading collaborative evaluation; the health evaluation module is used for obtaining a rehabilitation level based on the health state evaluation result; the system comprises a dynamic weighting information entropy collaborative matrix algorithm, a rehabilitation recommendation module, a nursing plan module, a health information module and an expert suggestion module, realizes real-time weighting of complex association among physiological data through the dynamic weighting information entropy collaborative matrix algorithm, overcomes the problem that a traditional fixed weight model is difficult to adapt to dynamic change of data, and improves the accuracy of data processing through dynamic adjustment of collaborative matrix weight. And the health state change of the patient is accurately captured.

Description

Technical Field

[0001] The present invention relates to the technical field of postoperative rehabilitation nursing management systems, and in particular to a postoperative rehabilitation nursing management system based on data analysis. Background Art

[0002] In recent years, with the advancement of medical big data and intelligent analysis technology, intelligent management methods and systems for medical test specimens have received widespread attention and application in the field of postoperative rehabilitation care. Existing postoperative rehabilitation management systems usually rely on static monitoring data and simple linear analysis models to collect and evaluate patient physiological data. However, most of these systems are limited to basic physiological parameter detection, lack sophisticated analysis and processing methods for dynamic changes in data, and cannot effectively adapt to real-time fluctuations in the patient's physiological state, resulting in limited rehabilitation effects. Especially in the face of fluctuations in multidimensional physiological data such as heart rate and body temperature, existing technologies usually use a single-dimensional weighted analysis method, which makes it difficult to accurately capture the complex nonlinear relationship between physiological data, resulting in insufficient accuracy and real-time performance of health assessment results, and it is difficult to achieve refined management and personalized care of patients' rehabilitation conditions.

[0003] The deficiencies of existing systems in data collection, analysis and generation of nursing plans are particularly obvious. In the data collection stage, existing technologies are usually unable to achieve real-time dynamic data collection, and the collection frequency and data quality of various physiological indicators are unbalanced, which can easily lead to data missing or lagging, affecting the effectiveness of subsequent analysis. More importantly, the existing rehabilitation management systems generally rely on fixed weight models, and the correlation between physiological data is not flexibly reflected in the weights. For example, for fluctuations in heart rate and respiratory rate, traditional methods are difficult to adjust the weights in real time to match the dynamic changes of patients, and thus cannot accurately reflect the patient's true health status. At the same time, due to the lack of multi-dimensional collaborative processing methods, the system has a high false alarm rate or missed alarm rate in abnormality detection, making it difficult to detect the patient's abnormal physiological state in a timely manner, and potential risks in the rehabilitation process are often not effectively monitored.

[0004] Another limitation of existing technologies is the lack of personalized care recommendations. The rehabilitation process of postoperative patients is affected by many factors, and individual differences are significant. Traditional postoperative rehabilitation systems often adopt "universal" care plans and lack the ability to adapt to the needs of different patients. This fixed plan is difficult to dynamically adjust according to the individual's physiological characteristics, and the level of personalization in rehabilitation training, diet management, and drug recommendations is low, which greatly reduces the patient's rehabilitation effect and comfort. Especially in the assessment of rehabilitation level, the existing system usually uses a single indicator evaluation method, which cannot accurately assess the patient's comprehensive health status, resulting in the recommended care plan is not accurate enough and the rehabilitation effect is not ideal.

[0005] Low data processing efficiency is also a major bottleneck of existing technologies. With the continuous accumulation of patients' physiological data and the increasing demand for real-time collection, the system processing burden increases, the response time of data analysis becomes longer, and high-frequency data collection and real-time processing cannot be achieved. The existing system architecture often lacks a collaborative mechanism between modules, and the flow of data between modules is not smooth enough, which affects the operating efficiency of the overall system. In addition, each module in the system operates independently, and it is difficult to achieve effective collaborative optimization of data input and output, which greatly limits the overall effect of rehabilitation nursing management. Summary of the invention

[0006] The present invention proposes a postoperative rehabilitation care management system based on data analysis, which aims to significantly improve the system's health assessment accuracy, anomaly detection sensitivity, personalized care level and data processing efficiency by introducing innovative algorithms such as dynamic weighted information entropy collaborative matrix, multidimensional nonlinear Taylor smoothing prediction, multi-level abnormal deviation detection, personalized dynamic threshold adaptation and progressive health status grading collaborative evaluation.

[0007] The present invention proposes a postoperative rehabilitation nursing management system based on data analysis, comprising:

[0008] A data acquisition module is used to collect multi-dimensional physiological data of the patient after surgery, including heart rate, body temperature, respiratory rate, movement status and sleep status;

[0009] A physiological health analysis module, used to generate a health status assessment result based on the physiological data, the physiological health analysis module including a dynamic weighted information entropy collaborative matrix, nonlinear smooth prediction, multi-level abnormal deviation detection, personalized dynamic threshold adaptation and progressive health status hierarchical collaborative assessment;

[0010] A health assessment module, used to obtain a rehabilitation level based on the health status assessment result;

[0011] A rehabilitation recommendation module, used to obtain a personalized rehabilitation training program based on the rehabilitation level;

[0012] A nursing plan module, for generating a nursing plan including a diet plan, drug management and a rehabilitation training sequence based on the outputs of the physiological health analysis module and the rehabilitation recommendation module;

[0013] Health information module, used for transmission and communication of health information between patients and doctors;

[0014] The expert advice module is used to obtain expert advice from the knowledge base based on the patient's health status assessment results and rehabilitation level.

[0015] Preferably, the physiological health analysis module includes a dynamic weighted information entropy synergy matrix, which is used to calculate the synergy relationship between the physiological data and generate a synergy matrix, and the synergy matrix is ​​used to determine the dynamic weighted information entropy synergy weight of each physiological data to provide input for subsequent nonlinear smooth prediction; wherein the dynamic weighted information entropy synergy matrix is ​​calculated by the following formula:

[0016]

[0017] Among them, W ij Physiological data D i and D j The dynamic weighted information entropy collaborative weight, p i,j (x k ) represents physiological data D i and D j In state x k The joint probability distribution under ij Physiological data D i and D j The synergy coefficient represents the synergy strength between physiological data.

[0018] Preferably, the physiological health analysis module includes a multidimensional nonlinear Taylor smoothing prediction unit, which is used to perform smooth prediction on each physiological data based on the weight result of the synergy matrix to obtain a smooth trend value; the prediction result of the multidimensional nonlinear Taylor smoothing prediction unit is determined according to the following formula:

[0019]

[0020] Among them, T i Physiological data D i The smooth trend prediction value of f(x0) represents the physiological data D i The value at the reference point x0, f (m) (x0) represents the mth order derivative, M is the maximum order of the nonlinear expansion, W ij is the weight coefficient generated by the dynamic weighted information entropy synergy matrix.

[0021] Preferably, the physiological health analysis module further comprises a multi-level abnormal deviation detection unit for determining an abnormal deviation index based on the smooth trend prediction value and the current state of each physiological data; the abnormal deviation index is calculated by the following formula:

[0022]

[0023] Among them, Δ i Physiological data D i The multi-level abnormal deviation index, D i is the real-time value of physiological data, Ti is the smoothed trend prediction value of the physiological data, N is the total dimension of the physiological data, and the denominator is the mean square deviation of each physiological data.

[0024] Preferably, the physiological health analysis module further comprises a personalized dynamic threshold adaptation unit, which is used to generate a personalized dynamic health threshold based on the abnormal deviation index and the patient's physiological characteristics for use in progressive health status hierarchical collaborative assessment; the personalized dynamic threshold is determined by the following formula:

[0025]

[0026] Among them, Θ i Physiological data D i The personalized dynamic threshold, α i Represents physiological data D i The basic threshold of , β is the weighting coefficient of abnormal deviation, Δ k represents the abnormal deviation index of the kth data, W ij represents the weight generated by the dynamic weighted information entropy cooperative matrix, and γ is the cooperative weighting coefficient.

[0027] Preferably, the physiological health analysis module further includes a progressive health status grading collaborative evaluation unit, which is used to grade each physiological indicator based on the personalized dynamic threshold and generate a comprehensive health level of the patient; the comprehensive health level is determined by the following formula:

[0028]

[0029] Among them, G is the patient's comprehensive health level, S k is the score of the kth physiological index, Θ k is the personalized dynamic threshold, C k is the synergistic impact index of the kth physiological indicator.

[0030] Preferably, the health assessment module generates a rehabilitation level of the patient based on the comprehensive health level, and the rehabilitation level is used to guide the generation of a rehabilitation training program in the rehabilitation recommendation module.

[0031] Preferably, the rehabilitation recommendation module generates a personalized rehabilitation training program based on the rehabilitation level, and the rehabilitation training program includes training frequency, exercise volume and action suggestions.

[0032] Preferably, the care plan module generates a comprehensive care plan including a diet plan, drug management and a rehabilitation training sequence based on the rehabilitation training program and the comprehensive health level.

[0033] Preferably, the expert advice module obtains expert advice from an expert knowledge base based on the patient's rehabilitation level and the output result of the health assessment module, so as to provide targeted rehabilitation guidance for the patient.

[0034] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0035] The physiological health analysis module of the present invention realizes real-time weighting of complex associations between physiological data through a dynamic weighted information entropy collaborative matrix algorithm, overcoming the problem that the traditional fixed weight model is difficult to adapt to dynamic changes in data. In terms of algorithmic mechanism, through the dynamic adjustment of the collaborative matrix weight, the weight of each physiological data can be adaptively adjusted according to real-time fluctuations, accurately capturing changes in the patient's health status. This algorithm design effectively solves the contradiction between data fluctuations and nonlinear relationships, making the system more accurate and real-time in health assessment.

[0036] In addition, multi-dimensional nonlinear Taylor smoothing prediction achieves dynamic smoothing of data trends through multi-order Taylor expansion, effectively solving the impact of abnormal data fluctuations and noise, making subsequent anomaly detection and evaluation processes more accurate. Especially in terms of anomaly detection, multi-level abnormal deviation detection identifies abnormal states in a hierarchical and progressive manner, which can accurately determine the degree of data deviation, reduce false positives and false negatives, and ensure that the patient's health status can be intervened in a timely manner. Personalized dynamic threshold adaptation combines the individual differences of patients to generate adaptive dynamic thresholds, which is different from the rigid model of traditional fixed thresholds, making the rehabilitation plan more targeted.

[0037] From a macro level, the present invention realizes a complete closed-loop system from data collection, analysis, feedback to the generation of rehabilitation suggestions. Data flows between the data collection module, physiological health analysis module, health assessment module, rehabilitation recommendation module and nursing plan module. Each module forms an efficient rehabilitation management system through a close collaborative working mechanism. Through effective collaboration and information exchange between modules, the system can provide real-time response and dynamic adaptation, solving the technical contradiction of lack of complementarity and difficulty in connection between modules in traditional systems. At the micro level, the progressive health status hierarchical collaborative assessment makes the rehabilitation plan more personalized and precise through multi-level detailed grading of health status, thereby improving the overall effect of rehabilitation management.

[0038] In summary, the present invention has significant beneficial effects in terms of accuracy, real-time, personalization and coordination of postoperative rehabilitation nursing management. Through the collaborative work of various modules and the mutual cooperation of innovative algorithms, the system of the present invention can better meet the personalized and dynamic rehabilitation needs of patients, and provide an efficient and intelligent solution for the application of intelligent management system for medical specimens in postoperative care. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is the overall logic block diagram of the system of the present invention.

[0040] Figure 2 The following is a detailed process of data collection and physiological health analysis of the present invention.

[0041] Figure 3 This is the detailed flow of the nursing plan module of the present invention. DETAILED DESCRIPTION

[0042] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects are described in detail below in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0043] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0044] Example 1

[0045] See also Figure 1-3 The present invention relates to a postoperative rehabilitation nursing management system based on data analysis, which is particularly suitable for postoperative nursing scenarios in intelligent management systems for medical specimens. The system consists of a data acquisition module 1, a physiological health analysis module 2, a health evaluation module 3, a rehabilitation recommendation module 4, and a nursing plan module 5, etc. It can collect, dynamically analyze, and evaluate the multi-dimensional physiological data of postoperative patients in real time, thereby realizing personalized rehabilitation training recommendations and nursing plan generation.

[0046] First, a postoperative rehabilitation nursing management system based on data analysis of the present invention includes a data acquisition module 1, a physiological health analysis module 2, a health evaluation module 3, a rehabilitation recommendation module 4, a nursing plan module 5, a health information module 6 and an expert advice module 7. Specifically, the data acquisition module 1 is used to collect multidimensional physiological data of postoperative patients, which usually include heart rate, body temperature, respiratory rate, movement state and sleep state. The physiological health analysis module 2 generates a health status assessment result based on the physiological data. In particular, the physiological health analysis module 2 includes innovative algorithms such as dynamic weighted information entropy collaborative matrix, multidimensional nonlinear Taylor smoothing prediction, multi-level abnormal deviation detection, personalized dynamic threshold adaptation and progressive health status graded collaborative assessment, which can ensure the comprehensiveness and accuracy of data processing. The health evaluation module 3 further generates a rehabilitation level based on the health status assessment result, and the rehabilitation recommendation module 4 formulates a personalized rehabilitation training plan based on the rehabilitation level. The nursing plan module 5 generates a nursing plan including a diet plan, drug management and rehabilitation training sequence based on the output of the physiological health analysis module 2 and the rehabilitation recommendation module 4. In addition, the health information module 6 realizes the transmission of health information between the patient and the doctor, and the expert advice module 7 obtains personalized advice from the expert knowledge base based on the patient's health status assessment results and rehabilitation level.

[0047] In a preferred embodiment of the present invention, the data acquisition module 1 uses a high-precision sensor, the heart rate acquisition frequency is set to once per second, the body temperature and respiratory rate are set to once per minute, and the sleep state can be recorded once per hour. The reason for setting different acquisition frequencies is that the heart rate fluctuates faster, while the body temperature and respiratory rate change more slowly. Different acquisition frequencies can reduce the burden of data acquisition while ensuring accuracy.

[0048] Preferably, in one embodiment of the present invention, the dynamic weighted information entropy synergy matrix included in the physiological health analysis module 2 is used to calculate the synergy relationship between physiological data, thereby generating a synergy matrix to provide weight input for subsequent nonlinear smooth prediction. The calculation formula of the dynamic weighted information entropy synergy matrix is ​​as follows:

[0049]

[0050] Among them, W ij Physiological data D i and D j Dynamic weighted information entropy collaborative weight, p i,j (x k ) represents physiological data D i and D j In state x k The joint probability distribution under ij Physiological data Di and D j The synergy coefficient C is used to indicate the strength of association between data. ij It can be dynamically adjusted between 0 and 1, and different weights are assigned to different physiological data according to their correlation strength. The generation of the synergy matrix can reflect the complex interactive relationship between physiological data, making the subsequent smooth prediction more accurate, especially in the correlation between heart rate and respiratory rate, where subtle changes can be captured through high synergy weights.

[0051] Preferably, in one embodiment of the present invention, after obtaining the weights of the dynamic weighted information entropy synergy matrix, the physiological health analysis module 2 further uses a multidimensional nonlinear Taylor smoothing prediction algorithm to perform smooth prediction on the physiological data to obtain a trend smoothing value. This algorithm implements nonlinear smoothing processing of physiological data through high-order Taylor expansion, and the specific formula is as follows:

[0052]

[0053] Among them, T i Physiological data D i The smooth trend prediction value of f(x0) represents the physiological data D i The value at the reference point x0, f (m) (x0) is the mth order derivative, M is the maximum order of the nonlinear expansion, usually selected to be 3 to 5 to take into account both smoothing effect and computational efficiency. ij Represents the weight coefficient generated by the dynamic weighted information entropy collaborative matrix. After smoothing, the fluctuation of physiological data such as heart rate will be smoothed out, thereby reducing the impact of noise and making subsequent abnormality detection more accurate. In a preferred embodiment of the present invention, the Taylor expansion order is set to 4, so as to take into account the rapid fluctuation of heart rate and the stable change characteristics of body temperature.

[0054] Preferably, in one embodiment of the present invention, based on the smooth prediction, the physiological health analysis module 2 further includes a multi-level abnormal deviation detection unit for determining the degree of deviation of the current physiological data. Specifically, the multi-level abnormal deviation detection is calculated by the following formula:

[0055]

[0056] Among them, Δ i Represents physiological data D i The multi-level abnormal deviation index, D i Represents the physiological data currently collected in real time, T iis the smoothed trend prediction value, and the denominator is the mean square deviation of all physiological data and the trend prediction value. In general, to ensure the sensitivity of abnormal detection, the deviation threshold can be preferably set to 1.5 times the standard deviation value. i When the threshold is exceeded, the system determines that the data is abnormal. In one embodiment of the present invention, the abnormal threshold of heart rate is set to 1.2 times the standard deviation to capture the abnormal situation of rapid fluctuation in time. The threshold of body temperature and sleep state is set to 2 times the standard deviation to reduce the possibility of false alarm.

[0057] Preferably, in one embodiment of the present invention, based on the above abnormal deviation detection results, the physiological health analysis module 2 further includes a personalized dynamic threshold adaptation unit for generating a personalized dynamic health threshold to adapt to the individual differences of different patients. The calculation formula of the personalized dynamic threshold is:

[0058]

[0059] Among them, Θ i Represents physiological data D i The personalized dynamic threshold, α i It represents the basic threshold of physiological data. For example, the normal threshold of heart rate is set to 60-100 beats / minute with a floating range of 10%. β is the abnormal deviation feedback weight, which is preferably set between 0.8 and 1.2. γ is the synergistic weighting coefficient, which is usually 1.0. Δ k represents the abnormal deviation index of the kth physiological data, W ij is the weight of the dynamic weighted information entropy synergy matrix. Preferably, the personalized threshold enables the system to generate the most suitable rehabilitation care standards for patients of different ages and physiques, thereby providing a more accurate health assessment. In one embodiment of the present invention, by setting the deviation feedback weight β to 1.1, the system can respond to the deviation of physiological data more quickly, thereby detecting abnormal trends earlier and intervening in rehabilitation plans in advance.

[0060] In summary, the postoperative rehabilitation care management system based on data analysis of the present invention forms a complete closed-loop structure of data collection, analysis, evaluation and rehabilitation advice through the collaborative work of various modules.

[0061] Preferably, in one embodiment of the present invention, the health evaluation module 3 further generates the patient's rehabilitation level based on the output of the physiological health analysis module 2. Specifically, the health evaluation module 3 weighs and quantifies various health indicators by analyzing the health status assessment results output by the physiological health analysis module 2, thereby deriving the patient's rehabilitation level. In one embodiment of the present invention, the health evaluation module 3 first normalizes the patient's various physiological data so that different indicators can be evaluated under the same standard. For example, the heart rate can be normalized to between 0 and 1 within the standard range, and the respiratory rate and body temperature are also standardized according to their respective normal ranges.

[0062] Furthermore, the health evaluation module 3 can set different score intervals according to different rehabilitation levels. For example, the rehabilitation level can be divided into four levels: "excellent", "good", "average" and "poor". Preferably, when the patient's health status assessment results show that all key indicators are within the normal range, the system sets the rehabilitation level to "excellent"; if one or two secondary indicators are slightly beyond the normal range, it is "good"; when most physiological indicators fluctuate around the critical point, it is set to "average"; when more than two main indicators deviate significantly from the normal range, the system sets the rehabilitation level to "poor". This grading method ensures that the judgment results of the health evaluation module 3 are more comprehensive and objective, which helps to make accurate decisions in the subsequent rehabilitation recommendation module 4.

[0063] Preferably, in one embodiment of the present invention, the rehabilitation recommendation module 4 generates a personalized rehabilitation training program based on the rehabilitation level. The main function of the rehabilitation recommendation module 4 is to recommend a rehabilitation training program that meets the patient's current health status, including training frequency, amount of exercise and specific action suggestions, based on the rehabilitation level generated by the health assessment module 3. In a preferred embodiment, when the rehabilitation level is "excellent", the rehabilitation recommendation module 4 may recommend that the patient undergo moderate-intensity rehabilitation training, including three to five times a week, 30 to 45 minutes each time; when the rehabilitation level is "good", the recommended training frequency may be adjusted to two to four times a week to moderately reduce the amount of exercise; for patients with "average" or "poor" rehabilitation levels, the system may recommend low-intensity activities, such as stretching and walking training, to avoid further burden on the body.

[0064] Furthermore, the rehabilitation recommendation module 4 can make personalized action recommendations for patients. For example, for patients with large heart rate fluctuations, the rehabilitation recommendation module 4 may recommend low-intensity stretching and breathing training, while for patients with higher muscle recovery needs, the system will recommend appropriate strength training and aerobic exercise. Such personalized rehabilitation training programs help optimize the patient's recovery process and improve the quality of rehabilitation.

[0065] Preferably, in one embodiment of the present invention, the nursing plan module 5 formulates a comprehensive nursing plan including a diet plan, drug management, and a rehabilitation training sequence based on the output of the rehabilitation recommendation module 4 and the comprehensive health level generated by the health assessment module 3. The nursing plan module 5 forms a comprehensive nursing plan by analyzing the rehabilitation level, health assessment results, and personalized rehabilitation training plans to support the patient's rehabilitation process from multiple dimensions.

[0066] In one embodiment, the nursing plan module 5 may first generate a diet plan for the patient to ensure balanced nutrition and promote postoperative recovery. For example, when the rehabilitation level is "average" or "poor", the nursing plan module 5 will recommend that the patient increase the intake of protein and vitamins to speed up tissue healing, while for patients with a rehabilitation level of "good" or "excellent", a balanced diet combination may be recommended to maintain physical fitness and immunity. In addition, in terms of drug management, the nursing plan module 5 recommends appropriate drug management plans for patients based on the health status assessment results of the health assessment module 3. For example, for patients with high inflammatory indicators, the nursing plan module 5 may recommend the use of anti-inflammatory drugs and add the medication time and dosage to the nursing plan. Finally, the nursing plan module 5 sorts each action in the rehabilitation training and arranges the training order according to the patient's physical condition to ensure that the patient can gradually recover at a reasonable rhythm.

[0067] Through such meticulous nursing planning, Nursing Plan Module 5 ensures that patients receive comprehensive and meticulous care after surgery, avoiding secondary injuries caused by excessive exercise, while meeting nutritional needs, assisting drug treatment, and optimizing the overall recovery process.

[0068] Preferably, in one embodiment of the present invention, the health information module 6 realizes the exchange of health information between the patient and the doctor. The main function of the health information module 6 is to provide a real-time data exchange platform, so that patients can upload their own health data to the doctor, and the doctor can remotely view the patient's rehabilitation progress and give professional rehabilitation guidance. Preferably, the health information module 6 allows the patient to input data, record feelings and upload problems encountered during the rehabilitation process. In this way, the doctor can obtain the patient's latest health information in real time, so as to make corresponding adjustments to the rehabilitation plan based on the patient's feedback.

[0069] Furthermore, the health information module 6 also supports the doctor's feedback mechanism. For example, the doctor can push rehabilitation training adjustment suggestions, drug use reminders, etc. to the patient through this module. In order to facilitate the patient's understanding, the health information module 6 can display the rehabilitation level information of the health evaluation module 3 to the patient in the form of charts and text, so that the patient has an intuitive understanding of his or her own health status. At the same time, the patient can send a consultation request to the doctor through the health information module 6, so that the doctor can provide targeted suggestions in time in the background. In a preferred embodiment of the present invention, the health information module 6 can integrate voice input and push notification functions to improve the interaction efficiency between patients and doctors.

[0070] Preferably, in one embodiment of the present invention, the expert advice module 7 obtains expert advice from the knowledge base based on the patient's health status assessment results and rehabilitation level to provide more comprehensive personalized rehabilitation guidance. The expert advice module 7 includes a medical knowledge base and an algorithm recommendation engine, which can provide personalized professional advice to patients based on the patient's specific rehabilitation level and health status. These suggestions not only include training and diet, but may also involve psychological counseling, recovery progress analysis, etc., so as to help patients better adapt to the rehabilitation process.

[0071] In a preferred embodiment of the present invention, the expert advice module 7 can select the most suitable recovery plan from the system's knowledge base based on big data analysis and machine learning technology. Specifically, when the output of the health evaluation module 3 shows that the patient's health status fluctuates, the expert advice module 7 will automatically generate adaptive suggestions based on the patient's abnormal deviation index, dynamic threshold and other information. For example, when the patient's sleep quality decreases significantly, the expert advice module 7 may recommend soothing meditation exercises, or adjust the intensity of rehabilitation training at night to improve sleep quality. Through such personalized suggestions, the system can effectively help patients complete the rehabilitation process smoothly, while alleviating the psychological pressure of patients during rehabilitation.

[0072] In summary, a postoperative rehabilitation care management system based on data analysis of the present invention realizes multi-dimensional data collection, refined health analysis, personalized rehabilitation training recommendations and comprehensive nursing program formulation in the postoperative rehabilitation process through the collaborative cooperation between various modules. In particular, through innovative physiological health analysis algorithms and real-time data feedback mechanisms, the system can provide patients with accurate and comprehensive rehabilitation guidance, and improve the rehabilitation effect with the assistance of expert advice modules, so that it has broad application prospects in intelligent management of medical test specimens and related scenarios. The superiority of the present invention has been verified through comparative experiments, and an embodiment and a comparative example are specially selected for comparison to further illustrate the outstanding performance of the present invention in postoperative rehabilitation care management. The various indicators and detection methods set up in the experiment are based on professional medical data set standards to ensure the objectivity and repeatability of the experimental results.

[0073] Embodiment: The postoperative rehabilitation care management system based on data analysis of the present invention has core algorithms such as dynamic weighted information entropy collaborative matrix, multidimensional nonlinear Taylor smoothing prediction, multi-level abnormal deviation detection, personalized dynamic threshold adaptation and progressive health status grading collaborative evaluation, which are used to comprehensively and meticulously analyze the patient's multidimensional physiological data and generate personalized rehabilitation levels and care plans.

[0074] Comparative example: The existing postoperative rehabilitation management system does not have innovative collaborative weighting algorithms and personalized dynamic threshold adaptation functions. It only performs simple linear processing on physiological data through a static weight model, lacks adaptability to data fluctuations and nonlinear relationships, and the rehabilitation training program does not have the ability to dynamically adjust.

[0075] In order to scientifically evaluate the performance differences between the examples and the comparative examples, the following key indicators were selected for testing in this experiment:

[0076] 1. Health status assessment accuracy (%): The health status assessment results of the system are verified using professional medical data sets. The detection method calculates the accuracy by comparing the recovery level output by the system with the actual diagnosis of the doctor.

[0077] 2. Anomaly detection sensitivity (times / week): The number of times the system detects abnormal physiological data in a week. The detection method is based on the deviation from the standard deviation, and the detection system recognizes the frequency of abnormal situations in a week. The sensitivity is used to measure the response speed of data anomaly detection.

[0078] 3. Effectiveness of personalized nursing advice (%): The effectiveness of personalized nursing advice is measured through follow-up feedback from doctors comparing rehabilitation effects. The detection method is based on the patient's rehabilitation progress (such as physical recovery speed and sleep quality improvement rate) to evaluate the actual effect of the nursing advice provided by the system on the patient's rehabilitation progress.

[0079] 4. Data processing efficiency (s / item): The time required for the measurement system to process a single physiological data. The detection method is to input 1000 random physiological data into the system, record the data processing time, and calculate the average value.

[0080] The above indicators were tested in two sample systems, and the results are shown in the following table:

[0081] index Example Comparative Example Health status assessment accuracy (%) 95.4 78.6 Abnormal detection sensitivity (times / week) 15 8 Effectiveness of personalized care recommendations (%) 92.3 70.4 Data processing efficiency (s / item) 0.32 0.45

[0082] It can be seen from the test results that the embodiments of the present invention are significantly better than the control examples in various key indicators, demonstrating the superiority of the system in postoperative rehabilitation nursing management.

[0083] Health status assessment accuracy: The system of the present invention has an accuracy of 95.4% in health status assessment, which is a significant improvement over the 78.6% of the control group. This advantage is mainly due to the introduction of the dynamic weighted information entropy collaborative matrix. Through multi-dimensional collaborative analysis of physiological data, the system can more accurately capture the nonlinear relationship between physiological data, thereby generating an assessment result that is more in line with the actual health status.

[0084] Abnormality detection sensitivity: The system of the present invention has significantly improved the abnormality detection sensitivity, with the number of detections per week reaching 15 times, which is more sensitive than the control ratio of 8 times. This is due to the multi-level abnormal deviation detection algorithm having higher sensitivity in deviation judgment, especially faster response to subtle changes in heart rate and body temperature, which significantly improves the abnormality detection capability and helps to intervene in potential health risks in a timely manner.

[0085] Effectiveness of personalized nursing advice: The effectiveness of personalized nursing advice provided by the system of the present invention is 92.3%, which is significantly better than the 70.4% of the control group. Through the application of personalized dynamic threshold adaptation algorithm, the system can adjust the health threshold according to the individual differences of patients, ensuring that the generated nursing advice is more in line with the actual needs of patients, thereby improving the effect of rehabilitation training.

[0086] Data processing efficiency: In terms of data processing efficiency, the average processing time of a single piece of data in the system of the present invention is also significantly better than that of the control group (0.32 seconds vs. 0.45 seconds). The optimization of the dynamic weighted information entropy synergy matrix and the multi-dimensional nonlinear Taylor smoothing prediction algorithm significantly improves the system processing efficiency, ensures the real-time response capability when large-scale physiological data is input, and provides a guarantee for the real-time monitoring of postoperative rehabilitation care.

[0087] The experimental results of this embodiment fully demonstrate the superiority of the system of the present invention in health status assessment, abnormality detection sensitivity, effectiveness of personalized nursing recommendations and data processing efficiency. Through the above innovative algorithm, the system of the present invention can analyze the multi-dimensional physiological data of patients more accurately and in real time, and provide more personalized rehabilitation nursing solutions, making it have wide application potential in the intelligent management system of specimens for medical examinations and other postoperative rehabilitation nursing scenarios.

[0088] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A postoperative rehabilitation nursing management system based on data analysis, characterized in that: include: A data acquisition module is used to collect multi-dimensional physiological data of the patient after surgery, including heart rate, body temperature, respiratory rate, movement status and sleep status; A physiological health analysis module, used to generate a health status assessment result based on the physiological data, the physiological health analysis module including a dynamic weighted information entropy collaborative matrix, nonlinear smooth prediction, multi-level abnormal deviation detection, personalized dynamic threshold adaptation and progressive health status hierarchical collaborative assessment; A health assessment module, used to obtain a rehabilitation level based on the health status assessment result; A rehabilitation recommendation module, used to obtain a personalized rehabilitation training program based on the rehabilitation level; A nursing plan module, for generating a nursing plan including a diet plan, drug management and a rehabilitation training sequence based on the outputs of the physiological health analysis module and the rehabilitation recommendation module; Health information module, used for transmission and communication of health information between patients and doctors; The expert advice module is used to obtain expert advice from the knowledge base based on the patient's health status assessment results and rehabilitation level.

2. The postoperative rehabilitation nursing management system based on data analysis according to claim 1 is characterized in that: The physiological health analysis module includes a dynamic weighted information entropy synergy matrix, which is used to calculate the synergy relationship between the physiological data and generate a synergy matrix, and the synergy matrix is ​​used to determine the dynamic weighted information entropy synergy weight of each physiological data to provide input for subsequent nonlinear smooth prediction; wherein the dynamic weighted information entropy synergy matrix is ​​calculated by the following formula: Among them, W ij Physiological data D i and D j Dynamic weighted information entropy collaborative weight, p i,j (x k ) represents physiological data D i and D j In state x k The joint probability distribution under ij Physiological data D i and D j The synergy coefficient represents the synergy strength between physiological data.

3. The postoperative rehabilitation nursing management system based on data analysis according to claim 2 is characterized in that: The physiological health analysis module further includes a multidimensional nonlinear Taylor smoothing prediction unit, which is used to perform smoothing prediction on each physiological data based on the weight result of the synergy matrix to obtain a smoothing trend value; the prediction result of the multidimensional nonlinear Taylor smoothing prediction unit is determined according to the following formula: Among them, T i Physiological data D i The smooth trend prediction value of f(x0) represents the physiological data D i The value at the reference point x0, f (m) (x0) represents the mth order derivative, M is the maximum order of the nonlinear expansion, W ij is the weight coefficient generated by the dynamic weighted information entropy synergy matrix.

4. The postoperative rehabilitation nursing management system based on data analysis according to claim 3 is characterized in that: The physiological health analysis module further includes a multi-level abnormal deviation detection unit for determining an abnormal deviation index based on the smooth trend prediction value and the current state of each physiological data; the abnormal deviation index is calculated by the following formula: Among them, Δ i Physiological data D i The multi-level abnormal deviation index, D i is the real-time value of physiological data, T i is the smoothed trend prediction value of the physiological data, N is the total dimension of the physiological data, and the denominator is the mean square deviation of each physiological data.

5. The postoperative rehabilitation nursing management system based on data analysis according to claim 4 is characterized in that: The physiological health analysis module further includes a personalized dynamic threshold adaptation unit, which is used to generate a personalized dynamic health threshold based on the abnormal deviation index and the patient's physiological characteristics for progressive health status hierarchical collaborative assessment; the personalized dynamic threshold is determined by the following formula: Among them, Θ i Physiological data D i The personalized dynamic threshold, α i Represents physiological data D i The basic threshold of , β is the weighting coefficient of abnormal deviation, Δ k represents the abnormal deviation index of the kth data, W ij represents the weight generated by the dynamic weighted information entropy cooperative matrix, and γ is the cooperative weighting coefficient.

6. The postoperative rehabilitation nursing management system based on data analysis according to claim 5 is characterized in that: The physiological health analysis module also includes a progressive health status grading collaborative evaluation unit, which is used to grade each physiological indicator based on the personalized dynamic threshold and generate a comprehensive health level of the patient; the comprehensive health level is determined by the following formula: Among them, G is the patient's comprehensive health level, S k is the score of the kth physiological index, Θ k is the personalized dynamic threshold, C k is the synergistic impact index of the kth physiological indicator.

7. The postoperative rehabilitation nursing management system based on data analysis according to any one of claims 1 to 6, characterized in that: The health assessment module generates a rehabilitation level of the patient based on the comprehensive health level, and the rehabilitation level is used to guide the generation of a rehabilitation training program in the rehabilitation recommendation module.

8. The postoperative rehabilitation nursing management system based on data analysis according to claim 7 is characterized in that: The rehabilitation recommendation module generates a personalized rehabilitation training program based on the rehabilitation level, and the rehabilitation training program includes training frequency, exercise volume and action suggestions.

9. The postoperative rehabilitation nursing management system based on data analysis according to claim 8 is characterized in that: The care plan module generates a comprehensive care plan including a diet plan, drug management, and a rehabilitation training sequence based on the rehabilitation training program and the comprehensive health level.

10. The postoperative rehabilitation nursing management system based on data analysis according to claim 9 is characterized in that: The expert advice module obtains expert advice from an expert knowledge base based on the patient's rehabilitation level and the output result of the health assessment module, so as to provide targeted rehabilitation guidance for the patient.

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