A risk assessment and early warning method and system for rehabilitation nursing

Abstract

The present invention discloses a risk assessment and early warning method and system for rehabilitation nursing, which relates to the field of medical data analysis technology, and includes the following steps: collecting various rehabilitation index data of patients, including physiological indexes, psychological indexes and rehabilitation progress data, and pre-processing the collected rehabilitation index data to obtain a rehabilitation index sequence table; extracting the correlation characteristics of the patient's physiological indexes, psychological indexes and rehabilitation progress according to the rehabilitation index sequence table. The risk assessment and early warning method and system for rehabilitation nursing can timely discover the risk factors that may appear in the patient's rehabilitation process by real-time monitoring of the patient's physiological and psychological state. The system dynamically calculates the rehabilitation risk assessment coefficient by analyzing the physiological indicators of heart rate, blood pressure, and body temperature, as well as the patient's psychological state and rehabilitation progress. When the assessment coefficient exceeds the preset early warning threshold, an early warning is automatically triggered to remind medical staff to intervene in time.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data analysis, and in particular to a risk assessment and early warning method and system for rehabilitation nursing. Background Art

[0002] With the continuous development of medical technology and the increase in patients' rehabilitation needs, the safety issues of rehabilitation nursing have become increasingly prominent. The core goal of rehabilitation nursing is to help patients restore or improve their functional status and improve their quality of life. The application of risk assessment and early warning aims to systematically and scientifically evaluate the various risks that patients may face during the rehabilitation process and formulate corresponding preventive measures, thereby effectively reducing the incidence of accidents and complications. It can not only improve the pertinence and effectiveness of rehabilitation treatment, but also shorten the patient's rehabilitation cycle, promote their faster and better recovery of health, and thus improve the overall safety of rehabilitation nursing.

[0003] In the existing technology, the risk assessment of patient rehabilitation often ignores the differences between individuals, which affects the intervention effect on the patient's nursing process, and cannot reflect the changes in the patient's condition in real time, resulting in delayed warning. Therefore, how to comprehensively analyze the patient's various rehabilitation indicators, dynamically adjust the monitoring of the patient's rehabilitation risk and formulate personalized intervention measures is the problem we need to solve. To this end, a risk assessment and early warning method and system for rehabilitation nursing are proposed. Summary of the invention

[0004] To achieve the above objectives, the present invention is implemented through the following technical solutions: In the first aspect, a risk assessment and early warning method for rehabilitation nursing comprises the following steps:

[0005] Step 1: Collect various rehabilitation index data of patients, including physiological indexes, psychological indexes and rehabilitation progress data, and pre-process the collected rehabilitation index data to obtain a rehabilitation index sequence table;

[0006] Step 2: Extract the correlation characteristics of the patient's physiological indicators, psychological indicators and rehabilitation progress according to the rehabilitation indicator sequence table, and evaluate the patient's physiological state, psychological state and rehabilitation progress state one by one;

[0007] Step 3: Based on the patient's personalized information and the associated data of the rehabilitation indicator sequence table, a personalized risk assessment model is established in combination with a machine learning algorithm to comprehensively analyze the patient's various rehabilitation indicators and assess the patient's current rehabilitation risk level;

[0008] Step 4: Combine the risk assessment model and the severity of the risk, preset the patient's risk level, match the corresponding warning threshold, deploy a real-time data monitoring system, continuously monitor and analyze the patient's rehabilitation indicators, and use the risk assessment model to output the patient's risk level;

[0009] Step 5. Based on the real-time data analysis and risk assessment results, dynamically adjust the patient's rehabilitation risk monitoring strategy, and formulate personalized intervention measures based on the patient's rehabilitation condition.

[0010] Preferably, in step 1, the process of obtaining the rehabilitation index sequence table includes:

[0011] The rehabilitation index data of patients that need to be collected are clearly defined, namely physiological indexes, psychological indexes and rehabilitation progress data. Among them, physiological index data are obtained through the hospital's electronic medical record system, medical equipment (electrocardiogram monitor, sphygmomanometer) and laboratory test results, including heart rate, blood pressure and body temperature. Psychological index data are obtained through psychological assessment scales, patient self-reports and family feedback assessments, including patient anxiety scores and depression scale scores. Patient anxiety scores are obtained through the anxiety self-rating scale, and depression scale scores are obtained through the depression self-rating scale. Rehabilitation progress data are obtained through rehabilitation treatment plan records, rehabilitation training logs, patient functional assessment results and doctor's assessment reports, including patients' rehabilitation goal scores, rehabilitation plan implementation scores, limb function assessment scores, daily living activities scores and treatment compliance scores;

[0012] Perform pre-processing operations such as data cleaning, data conversion and data standardization on the collected data of various rehabilitation indicators of patients;

[0013] The processed rehabilitation indicator data shall be classified and sorted according to their respective categories, and each category shall be subdivided according to specific indicators to form a rehabilitation indicator sequence table, which shall clearly and comprehensively reflect the patient's physiological, psychological and rehabilitation progress. The rehabilitation indicator sequence table shall include timestamp, patient personalized information, physiological indicator data, psychological indicator data, rehabilitation progress data, and the relevant data of the rehabilitation indicator sequence table shall be updated regularly to reflect the patient's latest rehabilitation status. The rehabilitation indicator sequence table and related data shall be stored in a secure and accessible data warehouse to ensure the privacy and security of the data. The patient's personalized information shall include basic information such as name, number, gender, age, and treatment history.

[0014] Preferably, in step 2, the process of evaluating the patient's physiological state, psychological state, and rehabilitation progress includes:

[0015] Traverse the rehabilitation indicator data in the rehabilitation indicator sequence table, and extract the patient's physiological indicators, psychological indicators and related features of rehabilitation progress, among which the physiological indicator features include heart rate, blood pressure and body temperature features, the psychological indicator features include anxiety score and depression scale score features, and the rehabilitation progress features include rehabilitation goal score, rehabilitation plan execution score, limb function assessment score, daily living activities ability score and treatment compliance score features;

[0016] Based on the extracted physiological indicators of heart rate, blood pressure and body temperature characteristics and their associated data, the heart rate stability function, blood pressure fluctuation function and body temperature abnormality function are calculated respectively, and the physiological status assessment index is obtained through comprehensive analysis. It is compared with the baseline data to assess the patient's physiological health status and physiological status trend, and identify potential health risks;

[0017] Based on the extracted anxiety and depression scale score characteristics of the psychological indicators and their associated data, the anxiety and depression scale scores were used as the main inputs. Combined with the emotional stability assessment, the anxiety and depression score functions and the emotional stability assessment function were calculated. A comprehensive analysis was performed to obtain the psychological state assessment index, which was compared with the baseline data to assess the patient's mental health status and psychological state trend, with special attention paid to the extreme values ​​and change trends of the anxiety and depression scores, so as to identify psychological problems in a timely manner and take intervention measures.

[0018] Based on the extracted rehabilitation goal score, rehabilitation plan implementation score, limb function assessment score, daily living activities ability score and treatment compliance score characteristics of rehabilitation progress, the rehabilitation progress assessment index was calculated and compared with the rehabilitation goal and plan to evaluate the patient's rehabilitation progress.

[0019] Preferably, the physiological state assessment index is obtained based on a comprehensive analysis of the heart rate stability function, the blood pressure fluctuation function and the body temperature abnormality function, and its calculation expression is:

[0020] ;

[0021] ;

[0022] ;

[0023] ;

[0024] in, It is the physiological status assessment index. is the heart rate stability function, is the blood pressure fluctuation function, is the abnormal body temperature function, For the The heart rate value measured is the baseline value of heart rate, is the standard deviation of heart rate, is the maximum value of blood pressure, is the minimum value of blood pressure, is the baseline value of blood pressure, is the standard deviation of blood pressure, is the maximum value of body temperature, is the baseline value of body temperature, is the standard deviation of body temperature, is the number of measurements, The smaller the value, the closer the patient's physiological state is to the baseline state, and the better the physiological health condition is. The larger the value, the farther it deviates from the baseline, and the health risk exists;

[0025] The mental state assessment index is obtained based on a comprehensive analysis of the anxiety and depression scoring function and the emotional stability assessment function, and its calculation expression is:

[0026] ;

[0027] ;

[0028] ;

[0029] in, is the mental status assessment index, is the anxiety and depression scoring function, is the emotional stability evaluation function, For the The anxiety scores of the second assessment were Number of psychological assessments for anxiety ratings and depression scale scores, For the Depression scale scores at the second assessment, is the baseline value of anxiety score, is the baseline value of the depression scale score, For the The weight of the anxiety score, For the The weight of the sub-depression scale score, For the The emotional stability index of the psychological assessment indicates the ratio of positive emotions to negative emotions. is the number of assessments of the emotional stability index, The value range is between 0 and 1. The closer the value is to 0, the closer the patient's mental health status is to the baseline state, and the better the mental health status is. The closer the value is to 1, the worse the mental health status is;

[0030] The calculation expression of the rehabilitation progress assessment index is:

[0031] ;

[0032] ;

[0033] in, The rehabilitation progress assessment index. For the The actual scores of the rehabilitation indicators, For the The weight of each rehabilitation indicator, The number of rehabilitation progress assessment indicators. is the rehabilitation index scoring function, which represents the standardized rehabilitation index score. For the The target scores of the rehabilitation indicators are The maximum value among all rehabilitation indicator target scores. is the total number of target scores for the rehabilitation indicator, is the midpoint of the rehabilitation index score, used to adjust the symmetry of the function. is a parameter that controls the steepness of the function curve. The value range is between 0 and 1. The closer the value is to 1, the closer the patient's rehabilitation progress is to or exceeds the goal, and the better the rehabilitation effect is.

[0034] Preferably, in step 3, the process of establishing the risk assessment model includes:

[0035] Extract the patient's personalized information (name, number, gender, age, treatment history) and the rehabilitation indicator data in the rehabilitation indicator sequence table, and match the associated features of the rehabilitation indicator data with the patient's personalized information to obtain a feature data set;

[0036] The feature data set is divided into a training set and a test set. The training set data is used to train the neural network model, and a risk assessment model that matches the patient's personalized information is constructed. The test set data is used to test the model performance of the risk assessment model. The patient data is analyzed through the risk assessment model to obtain preliminary results of rehabilitation risk assessment.

[0037] Based on the preliminary results of rehabilitation risk assessment, combined with the physiological status assessment index, psychological status assessment index and rehabilitation progress assessment index, the rehabilitation risk assessment coefficient is calculated to analyze the patient's current rehabilitation risk;

[0038] The trained risk assessment model is deployed to the production environment, and the patient's data is predicted to obtain preliminary results of the rehabilitation risk assessment. Combined with the output value of the rehabilitation risk assessment coefficient, the patient's current rehabilitation risk level is comprehensively assessed.

[0039] Preferably, the calculation expression of the rehabilitation risk assessment coefficient is:

[0040] ;

[0041] in, is the recovery risk assessment coefficient, which is used to measure the patient's overall recovery risk. It is the physiological status assessment index. is the mental status assessment index, The rehabilitation progress assessment index. is the baseline value of the rehabilitation progress assessment index. is the slope parameter of the logistic function, which controls the steepness of the curve. and It is the weight coefficient, which is used to adjust the proportion of each index in the final evaluation coefficient.

[0042] Preferably, in step 4, the process of outputting the patient risk level includes:

[0043] According to the clinical risk requirements and the severity of the risk, different risk levels are defined, namely low risk, medium risk and high risk. In combination with the rehabilitation risk assessment coefficient, a corresponding warning threshold is matched for each rehabilitation risk assessment coefficient. When the rehabilitation risk assessment coefficient exceeds the warning threshold, the corresponding level of warning is triggered;

[0044] Deploy a real-time data monitoring system, integrate various rehabilitation monitoring devices, electronic medical record systems, and patient report data sources into the real-time data monitoring system, collect various rehabilitation indicator data of patients in real time, and immediately clean and pre-process the data after it enters the real-time data monitoring system;

[0045] Use the risk assessment model to process real-time data, output the patient's rehabilitation risk assessment coefficient, compare the assessment result with the preset warning threshold, and output the patient's current risk level;

[0046] When the patient's risk level reaches the preset warning threshold, the warning mechanism is triggered and a warning message is issued. The warning information is notified to medical staff in real time via SMS, email, and application notification to keep an eye on the patient's condition.

[0047] Preferably, the plurality of risk levels correspond to the plurality of warning thresholds, the warning thresholds include an upper threshold and a lower threshold, and the correspondence between the plurality of risk levels and the plurality of warning thresholds is:

[0048] Low risk level The patient is currently in a good and stable recovery state with no significant risk. He should be monitored and no immediate warning is required.

[0049] Medium risk level ; The patient has a certain recovery risk, triggering a medium-risk warning, notifying medical staff to conduct assessment, further monitoring and intervention;

[0050] High risk level The patient is at significant risk of recovery and is in an emergency state, triggering a high-risk warning, and medical staff need to take immediate action;

[0051] in, is the rehabilitation risk assessment coefficient, is the lower threshold corresponding to the medium risk level and the upper threshold corresponding to the low risk level, is the lower threshold corresponding to the high risk level and the upper threshold corresponding to the medium risk level, , .

[0052] Preferably, in step 5, the process of formulating personalized intervention measures includes:

[0053] Determine the patient's current risk level based on real-time data analysis and risk assessment results, and adjust the monitoring frequency based on the risk level. High-risk patients may require more frequent monitoring;

[0054] Based on the risk assessment results, medical staff further adjust the monitored physiological and psychological parameters in combination with the patient's condition, and dynamically update the warning thresholds in accordance with the patient's recovery progress and risk assessment results;

[0055] Analyze the patient's rehabilitation goals and progress, identify areas that need improvement, and develop personalized intervention measures based on risk assessment results and rehabilitation progress, allocate medical resources and coordinate medical staff to intervene and monitor rehabilitation care;

[0056] Regularly evaluate the effectiveness of intervention measures, including the patient's rehabilitation progress and changes in risk assessment coefficients, collect feedback from patients and medical staff, optimize monitoring strategies and intervention measures, and regularly generate reports on rehabilitation risk assessment and intervention effectiveness for reference by medical staff, patients and their families.

[0057] In a second aspect, a risk assessment and early warning system for rehabilitation nursing is used to implement a risk assessment and early warning method for rehabilitation nursing, comprising: a risk assessment platform, wherein the risk assessment platform is communicatively connected with a data collection module, a data analysis module, a risk assessment model module, an early warning notification module, a monitoring and adjustment module, and a personalized intervention measure module, wherein the modules are electrically connected;

[0058] The data collection module is used to integrate various medical monitoring equipment and electronic medical record systems to collect patients' physiological indicators, psychological indicators and rehabilitation progress data, ensure the comprehensiveness and real-time nature of the data, and perform preprocessing operations on the collected data to obtain a rehabilitation indicator sequence table to improve data quality and analysis accuracy;

[0059] The data analysis module is used to extract features from the data in the rehabilitation index sequence table and analyze the patient's physiological state, psychological state and rehabilitation progress state one by one;

[0060] The risk assessment model module is used to combine the patient's personalized information and the associated data of the rehabilitation indicator sequence table to construct a risk assessment model, calculate the patient's rehabilitation risk assessment coefficient, and assess the patient's current rehabilitation risk level;

[0061] The warning notification module is used to process real-time data, preset the patient's risk level in combination with the risk assessment model and the severity of the risk, compare the warning threshold that matches the corresponding risk level, determine the patient's risk level, and automatically trigger the warning mechanism to generate warning information when the patient's rehabilitation risk assessment coefficient reaches the preset warning threshold;

[0062] The monitoring adjustment module dynamically adjusts the monitoring strategy and early warning threshold according to the patient's risk level and rehabilitation progress to ensure the effectiveness and pertinence of the monitoring strategy, and optimizes the monitoring frequency and parameters according to the patient's rehabilitation progress and feedback;

[0063] The personalized intervention measures module formulates personalized intervention measures according to the risk assessment results and the patient's rehabilitation status to adjust the rehabilitation plan.

[0064] The present invention provides a risk assessment and early warning method and system for rehabilitation nursing, which has the following beneficial effects:

[0065] 1. The risk assessment and early warning method and system for rehabilitation care can timely discover the risk factors that may occur in the patient's rehabilitation process by real-time monitoring of the patient's physiological and psychological state. The system dynamically calculates the rehabilitation risk assessment coefficient by analyzing the physiological indicators of heart rate, blood pressure, and body temperature, as well as the patient's psychological state and rehabilitation progress. When the assessment coefficient exceeds the preset early warning threshold, an early warning is automatically triggered to remind medical staff to intervene in time. The active monitoring and early warning mechanism significantly improves the safety of the patient's rehabilitation process. In addition, it can dynamically adjust the rehabilitation plan and provide personalized intervention measures based on the patient's real-time data and risk assessment results, thereby improving rehabilitation efficiency.

[0066] 2. The risk assessment and early warning method and system for rehabilitation care. Through risk assessment and early warning, medical staff can give priority to high-risk patients according to the patient's risk level, thereby allocating medical resources more reasonably, and by providing personalized rehabilitation plans and timely intervention measures, it can significantly improve the patient's rehabilitation experience. Through continuous monitoring and analysis of patient rehabilitation data, medical staff can promptly discover and solve potential problems and improve the efficiency of rehabilitation care. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A method flow chart of a risk assessment and early warning method and system for rehabilitation nursing of the present invention;

[0068] Figure 2 A flow chart for evaluating the patient's physiological state, psychological state, and rehabilitation progress state for the present invention;

[0069] Figure 3 A flow chart for establishing a risk assessment model of the present invention;

[0070] Figure 4 A flow chart for outputting patient risk levels for the present invention;

[0071] Figure 5 This is a diagram of the module structure of the present invention. DETAILED DESCRIPTION

[0072] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. The embodiments of the present invention are provided for the purpose of illustration and description, and are not intended to be exhaustive or to limit the present invention to the disclosed forms. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present invention, and to enable those of ordinary skill in the art to understand the present invention and thereby design various embodiments with various modifications suitable for specific uses.

[0073] The first embodiment, as Figure 1 , Figure 2 As shown, the present invention provides a technical solution: a risk assessment and early warning method for rehabilitation nursing, comprising the following steps:

[0074] Step 1: Collect various rehabilitation indicator data of patients, including physiological indicators, psychological indicators and rehabilitation progress data, and pre-process the collected rehabilitation indicator data to obtain a rehabilitation indicator sequence table, clarify the various rehabilitation indicator data of patients to be collected, namely physiological indicators, psychological indicators and rehabilitation progress data. Among them, physiological indicator data are obtained through the hospital's electronic medical record system, medical equipment (electrocardiogram monitor, sphygmomanometer) and laboratory test results, including heart rate, blood pressure, and body temperature. Psychological indicator data are obtained through psychological assessment scales, patient self-reports, and family feedback assessments, including patient anxiety scores and depression scale scores. Patient anxiety scores are obtained through anxiety self-rating scales, and depression scale scores are obtained through depression self-rating scales. Rehabilitation progress data are obtained through rehabilitation treatment plan records, rehabilitation training logs, patient functional assessment results, and doctor assessment reports, including patients' rehabilitation goal scores, rehabilitation plan implementation scores, limb function assessment scores, daily living activities scores, and treatment compliance scores. Data cleaning, data conversion, and data standardization are pre-processed for the collected patient rehabilitation indicator data. Processing operations, wherein data cleaning is used to identify and correct errors or inconsistencies in the data, duplicate data is deleted, and the uniqueness and accuracy of the data is ensured; data is converted from the original format or representation to a form suitable for analysis, text data is converted to numerical data, and date and time data is converted to a unified format; indicator data of different dimensions or ranges are standardized for comparison in subsequent analysis; the processed rehabilitation indicator data are classified and sorted according to their respective categories, and each category is subdivided according to specific indicators to form a rehabilitation indicator sequence table, which should clearly and comprehensively reflect the patient's physiological, psychological and rehabilitation progress status, wherein the rehabilitation indicator sequence table includes timestamps, patient personalized information, physiological indicator data, psychological indicator data, and rehabilitation progress data; the relevant data of the rehabilitation indicator sequence table is regularly updated to reflect the patient's latest rehabilitation status, and the rehabilitation indicator sequence table and related data are stored in a secure and accessible data warehouse to ensure data privacy and security; the patient's personalized information includes basic information such as name, number, gender, age, and treatment history;

[0075] Step 2: Extract the correlation characteristics of the patient's physiological indicators, psychological indicators and rehabilitation progress according to the rehabilitation indicator sequence table, evaluate the patient's physiological state, psychological state and rehabilitation progress state one by one, traverse the rehabilitation indicator data in the rehabilitation indicator sequence table, and extract the correlation characteristics of the patient's physiological indicators, psychological indicators and rehabilitation progress. Among them, the physiological indicator characteristics include heart rate, blood pressure and body temperature characteristics, the psychological indicator characteristics include anxiety score and depression scale score characteristics, and the rehabilitation progress characteristics include rehabilitation goal score, rehabilitation plan execution score, limb function assessment score, daily living activity ability score and treatment compliance score characteristics. The stability of the heart rate is analyzed through the heart rate characteristics. and variability, extract the average heart rate and heart rate variability features, extract the average, maximum, minimum and blood pressure fluctuations of systolic and diastolic blood pressure through blood pressure features, extract the average body temperature, fluctuation range and abnormal body temperature events through body temperature features, extract the score trend of the anxiety self-rating scale through anxiety score features, including recent score changes and historical highest / lowest scores, extract the score trend of the depression self-rating scale through depression scale score features, including recent score changes and historical highest / lowest scores, extract the degree and time of patients achieving rehabilitation goals through rehabilitation goal score features, extract the consistency and frequency of patients implementing rehabilitation plans through rehabilitation plan execution score features, and extract the score trend of patients implementing rehabilitation plans through limb The physical function assessment scoring features are used to extract the changing trends and current levels of limb function. The daily living activities scoring features are used to extract the changing trends and current levels of patients' daily self-care abilities. The treatment compliance scoring features are used to extract the consistency and frequency of patients' compliance with doctor's orders and treatment plans. Based on the extracted physiological indicators' heart rate, blood pressure, and body temperature features and their associated data, the heart rate stability function, blood pressure fluctuation function, and body temperature abnormality function are calculated respectively. A comprehensive analysis is performed to obtain the physiological status assessment index, which is compared with the baseline data to assess the patient's physiological health status and physiological status trends, identify potential health risks, and based on the extracted psychological indicators' anxiety score and depression scale score features and their associated data. Linked data, taking anxiety scores and depression scale scores as the main inputs, combined with emotional stability assessment, calculate anxiety and depression score functions and emotional stability assessment functions, and comprehensively analyze to obtain the psychological state assessment index, which is compared with the baseline data to assess the patient's mental health status and psychological state trend, paying special attention to the extreme values ​​and changing trends of anxiety and depression scores, identifying psychological problems in a timely manner and taking intervention measures, and calculating the rehabilitation progress assessment index based on the extracted rehabilitation goal score, rehabilitation plan implementation score, limb function assessment score, daily living activities ability score, and treatment compliance score characteristics, and comparing it with the rehabilitation goal and plan to assess the patient's rehabilitation progress;

[0076] Furthermore, the physiological state evaluation index is obtained based on the comprehensive analysis of the heart rate stability function, the blood pressure fluctuation function and the body temperature abnormality function, and its calculation expression is:

[0077] ;

[0078] ;

[0079] ;

[0080] ;

[0081] in, It is the physiological status assessment index. is the heart rate stability function, is the blood pressure fluctuation function, is the abnormal body temperature function, For the The heart rate value measured is the baseline value of heart rate, is the standard deviation of heart rate, is the maximum value of blood pressure, is the minimum value of blood pressure, is the baseline value of blood pressure, is the standard deviation of blood pressure, is the maximum value of body temperature, is the baseline value of body temperature, is the standard deviation of body temperature, is the number of measurements, The smaller the value, the closer the patient's physiological state is to the baseline state, and the better the physiological health condition is. The larger the value, the farther it deviates from the baseline, and the health risk exists. The heart rate stability function evaluates the stability and variability of the heart rate by calculating the sum of the squares of the deviations between the heart rate and its baseline value. The larger the deviation, the more unstable the heart rate. The blood pressure fluctuation function evaluates the fluctuation of blood pressure by calculating the deviations of the maximum and minimum blood pressure values ​​from the baseline value and combining the standard deviation of blood pressure. The body temperature abnormality function evaluates whether the body temperature is within the normal range by calculating the deviation of the maximum body temperature value from the baseline value and using an exponential function to amplify the impact of abnormal body temperature.

[0082] The psychological state assessment index is obtained based on a comprehensive analysis of the anxiety and depression scoring function and the emotional stability assessment function, and its calculation expression is:

[0083] ;

[0084] ;

[0085] ;

[0086] in, is the mental status assessment index, is the anxiety and depression scoring function, is the emotional stability evaluation function, For the The anxiety scores of the second assessment were Number of psychological assessments for anxiety ratings and depression scale scores, For the Depression scale scores at the second assessment, is the baseline value of anxiety score, is the baseline value of the depression scale score, For the The weight of the anxiety score, For the The weight of the sub-depression scale score, For the The emotional stability index of the psychological assessment indicates the ratio of positive emotions to negative emotions. is the number of assessments of the emotional stability index, The value range is between 0 and 1. The closer the value is to 0, the closer the patient's mental health status is to the baseline state, and the better the mental health status is. The closer the value is to 1, the worse the mental health is. The patient's anxiety and depression levels are assessed by calculating the deviations of the anxiety score and depression scale score from their baseline values ​​and assigning weights to each assessment. The patient's mood swings are assessed by calculating the square root of the sum of the squares of the mood stability index. The greater the mood swings, the greater the negative impact on mental health.

[0087] The calculation expression of rehabilitation progress assessment index is:

[0088] ;

[0089] ;

[0090] in, The rehabilitation progress assessment index. For the The actual scores of the rehabilitation indicators, For the The weight of each rehabilitation indicator, The number of rehabilitation progress assessment indicators. is the rehabilitation index scoring function, which represents the standardized rehabilitation index score. For the The target scores of the rehabilitation indicators are The maximum value among all rehabilitation indicator target scores. is the total number of target scores for the rehabilitation indicator, is the midpoint of the rehabilitation index score, used to adjust the symmetry of the function. is a parameter that controls the steepness of the function curve. The value range is between 0 and 1. The closer the value is to 1, the closer the patient's rehabilitation progress is to or exceeds the goal, and the better the rehabilitation effect is. The closer the value is to 0, the greater the gap between the rehabilitation progress and the goal, and the worse the rehabilitation effect. The value range is between 0 and 1. When approaching the minimum score, Close to 0, when Near the midpoint of the rating hour, Close to 0.5, when When approaching the maximum value of the score, Close to 1;

[0091] Step 3: Based on the patient's personalized information and the associated data of the rehabilitation indicator sequence table, a personalized risk assessment model is established in combination with a machine learning algorithm to comprehensively analyze the patient's various rehabilitation indicators and assess the patient's current rehabilitation risk level;

[0092] Step 4: Combine the risk assessment model and the severity of the risk, preset the patient's risk level, match the corresponding warning threshold, deploy a real-time data monitoring system, continuously monitor and analyze the patient's rehabilitation indicators, and use the risk assessment model to output the patient's risk level;

[0093] Step 5. Based on the real-time data analysis and risk assessment results, dynamically adjust the patient's rehabilitation risk monitoring strategy, and formulate personalized intervention measures based on the patient's rehabilitation condition.

[0094] The second embodiment is based on the first embodiment. Figure 3 , Figure 4 As shown in step 3, the process of establishing the risk assessment model includes:

[0095] Extract the patient's personalized information (name, number, gender, age, treatment history) and the rehabilitation indicator data in the rehabilitation indicator sequence table, and match the associated features of the rehabilitation indicator data with the patient's personalized information to obtain a feature data set, divide the feature data set into a training set and a test set, use the training set data to train the neural network model, build a risk assessment model that matches the patient's personalized information, and use the test set data to test the model performance of the risk assessment model. Analyze the patient data through the risk assessment model to obtain preliminary results of rehabilitation risk assessment. Based on the preliminary results of rehabilitation risk assessment, combined with the physiological state assessment index, psychological state assessment index and rehabilitation progress assessment index, calculate the rehabilitation risk assessment coefficient, analyze the patient's current rehabilitation risk, deploy the trained risk assessment model to the production environment, predict the patient's data, obtain preliminary results of rehabilitation risk assessment, and use the output value of the rehabilitation risk assessment coefficient to comprehensively assess the patient's current rehabilitation risk level;

[0096] Furthermore, the calculation expression of the rehabilitation risk assessment coefficient is:

[0097] ;

[0098] in, is the recovery risk assessment coefficient, which is used to measure the patient's overall recovery risk. It is the physiological status assessment index. is the mental status assessment index, The rehabilitation progress assessment index. is the baseline value of the rehabilitation progress assessment index. is the slope parameter of the logistic function, which controls the steepness of the curve. and is the weight coefficient, which is used to adjust the proportion of each index in the final evaluation coefficient. and Close to normal values, and Also close to or better than the benchmark value hour, The value will be smaller, indicating a lower risk of recovery. or An exception occurs, or Significantly lower than hour, The value will be larger, indicating a higher risk of recovery;

[0099] In step 4, the process of outputting the patient risk level includes:

[0100] According to clinical risk requirements and the severity of risks, different risk levels are defined, namely low risk, medium risk and high risk. In combination with the rehabilitation risk assessment coefficient, a corresponding warning threshold is matched for each rehabilitation risk assessment coefficient. When the rehabilitation risk assessment coefficient exceeds the warning threshold, the corresponding level of warning is triggered, and a real-time data monitoring system is deployed. Various rehabilitation monitoring equipment, electronic medical record systems, and patient report data sources are integrated into the real-time data monitoring system. The patient's various rehabilitation indicator data are collected in real time. After the data enters the real-time data monitoring system, it is immediately cleaned and preprocessed. The real-time data is processed using a risk assessment model, and the patient's rehabilitation risk assessment coefficient is output. The assessment result is compared with the preset warning threshold, and the patient's current risk level is output. When the patient's risk level reaches the preset warning threshold, the warning mechanism is triggered, and a warning message is issued. The warning message is notified to medical staff in real time through SMS, email, and application notification to pay attention to the patient's status;

[0101] Furthermore, multiple risk levels correspond to multiple warning thresholds, and the warning thresholds include an upper threshold and a lower threshold. The corresponding relationship between the multiple risk levels and the multiple warning thresholds is:

[0102] Low risk level The patient is currently in a good and stable recovery state with no significant risk. He should be monitored and no immediate warning is required.

[0103] Medium risk level ; The patient has a certain recovery risk, triggering a medium-risk warning, notifying medical staff to conduct assessment, further monitoring and intervention;

[0104] High risk level The patient is at significant risk of recovery and is in an emergency state, triggering a high-risk warning, and medical staff need to take immediate action;

[0105] in, is the rehabilitation risk assessment coefficient, is the lower threshold corresponding to the medium risk level and the upper threshold corresponding to the low risk level, is the lower threshold corresponding to the high risk level and the upper threshold corresponding to the medium risk level, , ;

[0106] In step five, the process of developing personalized interventions includes:

[0107] Based on the results of real-time data analysis and risk assessment, the patient's current risk level is determined, and the monitoring frequency is adjusted according to the risk level. High-risk patients may require more frequent monitoring. Based on the risk assessment results, medical staff further adjust the monitored physiological and psychological parameters in combination with the patient's condition, dynamically update the warning threshold in accordance with the patient's rehabilitation progress and risk assessment results, analyze the patient's rehabilitation goals and progress, identify areas that need improvement, and formulate personalized intervention measures based on risk assessment results and rehabilitation progress. Medical resources are allocated to coordinate medical staff to intervene and monitor rehabilitation care, and the effectiveness of intervention measures is regularly evaluated, including the patient's rehabilitation progress and changes in risk assessment coefficients. Feedback from patients and medical staff is collected, monitoring strategies and intervention measures are optimized, and regular reports on rehabilitation risk assessment and intervention effectiveness are generated for reference by medical staff, patients and their families.

[0108] The third embodiment is based on the first and second embodiments. Figure 5 As shown, the present invention also provides a risk assessment and early warning system for rehabilitation nursing, which is used to implement a risk assessment and early warning method for rehabilitation nursing, including: a risk assessment platform, the risk assessment platform is communicatively connected with a data collection module, a data analysis module, a risk assessment model module, an early warning notification module, a monitoring and adjustment module, and a personalized intervention measure module, wherein the modules are connected by electrical signals;

[0109] The data collection module is used to integrate various medical monitoring devices and electronic medical record systems to collect patients' physiological indicators, psychological indicators and rehabilitation progress data to ensure the comprehensiveness and real-time nature of the data, and to pre-process the collected data to obtain a rehabilitation indicator sequence table to improve data quality and analysis accuracy;

[0110] The data analysis module is used to extract features from the data in the rehabilitation index sequence table and analyze the patient's physiological state, psychological state, and rehabilitation progress state one by one;

[0111] The risk assessment model module is used to combine the patient's personalized information and the associated data of the rehabilitation indicator sequence table to build a risk assessment model, calculate the patient's rehabilitation risk assessment coefficient, and assess the patient's current rehabilitation risk level;

[0112] The early warning notification module is used to process real-time data, combine the risk assessment model and the severity of the risk, preset the patient's risk level, compare the early warning threshold that matches the corresponding risk level, and determine the patient's risk level. When the patient's rehabilitation risk assessment coefficient reaches the preset early warning threshold, the early warning mechanism is automatically triggered to generate early warning information;

[0113] The monitoring adjustment module dynamically adjusts the monitoring strategy and early warning threshold according to the patient's risk level and rehabilitation progress to ensure the effectiveness and pertinence of the monitoring strategy, and optimizes the monitoring frequency and parameters according to the patient's rehabilitation progress and feedback;

[0114] The personalized intervention module develops personalized intervention measures based on the risk assessment results and the patient's recovery status to adjust the rehabilitation plan.

[0115] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field and related fields without creative work should fall within the scope of protection of the present invention. The structures, devices and operating methods not specifically described and explained in the present invention are implemented according to the conventional means in the field unless otherwise specified and limited.

Claims

1. A risk assessment and early warning method for rehabilitation nursing, characterized in that: The following steps are involved: Step 1: Collect various rehabilitation index data of patients, including physiological indexes, psychological indexes and rehabilitation progress data, and pre-process the collected rehabilitation index data to obtain a rehabilitation index sequence table; Step 2: extracting the correlation features of the patient's physiological indicators, psychological indicators and rehabilitation progress respectively according to the rehabilitation indicator sequence table, and evaluating the patient's physiological state, psychological state and rehabilitation progress state one by one. In step 2, the process of evaluating the patient's physiological state, psychological state and rehabilitation progress state includes: Traverse the rehabilitation indicator data in the rehabilitation indicator sequence table, and extract the patient's physiological indicators, psychological indicators and related features of rehabilitation progress, among which the physiological indicator features include heart rate, blood pressure and body temperature features, the psychological indicator features include anxiety score and depression scale score features, and the rehabilitation progress features include rehabilitation goal score, rehabilitation plan execution score, limb function assessment score, daily living activities ability score and treatment compliance score features; Based on the extracted physiological indicators of heart rate, blood pressure and body temperature characteristics and their associated data, the heart rate stability function, blood pressure fluctuation function and body temperature abnormality function are calculated respectively, and the physiological status assessment index is obtained through comprehensive analysis. It is compared with the baseline data to assess the patient's physiological health status and physiological status trend, and identify potential health risks; Based on the extracted anxiety score and depression scale score characteristics of psychological indicators and their associated data, the anxiety score and depression scale score were used as the main input, combined with the emotional stability assessment, to calculate the anxiety and depression score function and the emotional stability assessment function, and the psychological state assessment index was obtained through comprehensive analysis, which was compared with the baseline data to assess the patient's mental health status and psychological state trend; Based on the extracted rehabilitation goal score, rehabilitation plan implementation score, limb function assessment score, daily living activities ability score and treatment compliance score characteristics of rehabilitation progress, the rehabilitation progress assessment index is calculated and compared with the rehabilitation goal and plan to evaluate the patient's rehabilitation progress; The physiological state evaluation index is obtained based on comprehensive analysis of the heart rate stability function, the blood pressure fluctuation function and the body temperature abnormality function, and its calculation expression is: ； ； ； ； in, is the physiological status assessment index, is the heart rate stability function, is the blood pressure fluctuation function, is the abnormal body temperature function, For the The heart rate value measured is the baseline value of heart rate, is the standard deviation of heart rate, is the maximum value of blood pressure, is the minimum value of blood pressure, is the baseline value of blood pressure, is the standard deviation of blood pressure, is the maximum value of body temperature, is the baseline value of body temperature, is the standard deviation of body temperature, is the number of measurements, The smaller the value, the closer the patient's physiological state is to the baseline state, and the better the physiological health condition is. The larger the value, the farther it deviates from the baseline, and the health risk exists; The mental state assessment index is obtained based on a comprehensive analysis of the anxiety and depression scoring function and the emotional stability assessment function, and its calculation expression is: ； ； ； in, is the mental status assessment index, is the anxiety and depression scoring function, is the emotional stability evaluation function, For the The anxiety scores of the second assessment were Number of psychological assessments for anxiety ratings and depression scale scores, For the Depression scale scores at the second assessment, is the baseline value of anxiety score, is the baseline value of the depression scale score, For the The weight of the anxiety score, For the The weight of the sub-depression scale score, For the The emotional stability index of the psychological assessment indicates the ratio of positive emotions to negative emotions. is the number of assessments of the emotional stability index, The value range is between 0 and 1. The closer the value is to 0, the closer the patient's mental health status is to the baseline state, and the better the mental health status is; The calculation expression of the rehabilitation progress assessment index is: ； ； in, The rehabilitation progress assessment index. For the The actual scores of the rehabilitation indicators, For the The weight of each rehabilitation indicator, The number of rehabilitation progress assessment indicators. is the rehabilitation index scoring function, For the The target scores of the rehabilitation indicators are The maximum value among all rehabilitation indicator target scores. is the total number of target scores for the rehabilitation indicator, The midpoint of the rehabilitation index score, is a parameter that controls the steepness of the function curve. The value range is between 0 and 1. The closer the value is to 1, the closer the patient's rehabilitation progress is to the goal and the better the rehabilitation effect is; Step 3: Based on the patient's personalized information and the associated data of the rehabilitation index sequence table, an individualized risk assessment model is established in combination with a machine learning algorithm to comprehensively analyze the patient's various rehabilitation indicators and assess the patient's current rehabilitation risk level. In step 3, the process of establishing the risk assessment model includes: Extracting the patient's personalized information and various rehabilitation indicator data in the rehabilitation indicator sequence table, and matching the associated features of the various rehabilitation indicator data with the patient's personalized information to obtain a feature data set; The feature data set is divided into a training set and a test set. The training set data is used to train the neural network model, and a risk assessment model that matches the patient's personalized information is constructed. The test set data is used to test the model performance of the risk assessment model. The patient data is analyzed through the risk assessment model to obtain preliminary results of rehabilitation risk assessment. Based on the preliminary results of rehabilitation risk assessment, combined with the physiological status assessment index, psychological status assessment index and rehabilitation progress assessment index, the rehabilitation risk assessment coefficient is calculated to analyze the patient's current rehabilitation risk; Deploy the trained risk assessment model to the production environment, predict the patient's data, obtain the preliminary results of the rehabilitation risk assessment, and comprehensively assess the patient's current rehabilitation risk level in conjunction with the output value of the rehabilitation risk assessment coefficient; The calculation expression of the rehabilitation risk assessment coefficient is: ； in, is the rehabilitation risk assessment coefficient, is the physiological status assessment index, is the mental status assessment index, The rehabilitation progress assessment index. is the baseline value of the rehabilitation progress assessment index. is the slope parameter of the logistic function, which controls the steepness of the curve. and is the weight coefficient; Step 4: Combine the risk assessment model and the severity of the risk, preset the patient's risk level, match the corresponding warning threshold, deploy a real-time data monitoring system, continuously monitor and analyze the patient's rehabilitation indicators, and use the risk assessment model to output the patient's risk level; Step 5. Based on the real-time data analysis and risk assessment results, dynamically adjust the patient's rehabilitation risk monitoring strategy, and formulate personalized intervention measures based on the patient's rehabilitation condition.

2. A risk assessment and early warning method for rehabilitation nursing according to claim 1, characterized in that: In the step 1, the process of obtaining the rehabilitation index sequence table includes: The rehabilitation index data of patients that need to be collected are clearly defined, namely physiological indexes, psychological indexes and rehabilitation progress data. Among them, physiological index data include heart rate, blood pressure and body temperature; psychological index data include patient anxiety score and depression scale score. Patient anxiety score is obtained through anxiety self-rating scale, and depression scale score is obtained through depression self-rating scale. Rehabilitation progress data include patient rehabilitation goal score, rehabilitation plan implementation score, limb function assessment score, daily living activities ability score and treatment compliance score; Perform pre-processing operations such as data cleaning, data conversion and data standardization on the collected data of various rehabilitation indicators of patients; The processed rehabilitation indicator data are classified and sorted according to their respective categories, and each category is subdivided according to specific indicators to form a rehabilitation indicator sequence table, where the rehabilitation indicator sequence table includes timestamp, patient personalized information, physiological indicator data, psychological indicator data, rehabilitation progress data, and the relevant data of the rehabilitation indicator sequence table is updated regularly, and the rehabilitation indicator sequence table and related data are stored in the data warehouse. The patient's personalized information includes basic information such as name, number, gender, age, and treatment history.

3. A risk assessment and early warning method for rehabilitation nursing according to claim 2, characterized in that: In step 4, the process of outputting the patient risk level includes: According to the clinical risk requirements and the severity of the risk, different risk levels are defined, namely low risk, medium risk and high risk. In combination with the rehabilitation risk assessment coefficient, a corresponding warning threshold is matched for each rehabilitation risk assessment coefficient. When the rehabilitation risk assessment coefficient exceeds the warning threshold, the corresponding level of warning is triggered; Deploy a real-time data monitoring system, integrate various rehabilitation monitoring devices, electronic medical record systems, and patient report data sources into the real-time data monitoring system, collect various rehabilitation indicator data of patients in real time, and immediately clean and pre-process the data after it enters the real-time data monitoring system; Use the risk assessment model to process real-time data, output the patient's rehabilitation risk assessment coefficient, compare the assessment result with the preset warning threshold, and output the patient's current risk level; When the patient's risk level reaches the preset warning threshold, the warning mechanism is triggered and a warning message is issued. The warning information is notified to medical staff in real time via SMS, email, and application notification to keep an eye on the patient's condition.

4. A risk assessment and early warning method for rehabilitation nursing according to claim 3, characterized in that: The multiple risk levels correspond to the multiple warning thresholds, the warning thresholds include an upper threshold and a lower threshold, and the corresponding relationship between the multiple risk levels and the multiple warning thresholds is: Low risk level ; Medium risk level ; High risk level ; in, is the rehabilitation risk assessment coefficient, is the lower threshold corresponding to the medium risk level and the upper threshold corresponding to the low risk level, The lower threshold corresponding to the high risk level and the upper threshold corresponding to the medium risk level.

5. A risk assessment and early warning method for rehabilitation nursing according to claim 4, characterized in that: In step 5, the process of developing personalized intervention measures includes: Determine the patient's current risk level based on real-time data analysis and risk assessment results, and adjust the monitoring frequency based on the risk level; Based on the risk assessment results, medical staff further adjust the monitored physiological and psychological parameters in combination with the patient's condition, and dynamically update the warning thresholds in accordance with the patient's recovery progress and risk assessment results; Analyze the patient's rehabilitation goals and progress, identify areas that need improvement, and develop personalized intervention measures based on risk assessment results and rehabilitation progress, allocate medical resources and coordinate medical staff to intervene and monitor rehabilitation care; Regularly evaluate the effectiveness of intervention measures, including patients' rehabilitation progress and changes in risk assessment coefficients, collect feedback from patients and medical staff, optimize monitoring strategies and intervention measures, and regularly generate reports on rehabilitation risk assessment and intervention effectiveness.

6. A risk assessment and early warning system for rehabilitation nursing, used to implement the risk assessment and early warning method for rehabilitation nursing according to any one of claims 1 to 5, comprising: The risk assessment platform is characterized in that: the risk assessment platform is communicatively connected with a data collection module, a data analysis module, a risk assessment model module, an early warning notification module, a monitoring and adjustment module, and a personalized intervention measure module, wherein the modules are electrically connected with each other; The data collection module is used to integrate various medical monitoring devices and electronic medical record systems, collect the patient's physiological indicators, psychological indicators and rehabilitation progress data, and perform preprocessing operations on the collected data to obtain a rehabilitation indicator sequence table; The data analysis module is used to extract features from the data in the rehabilitation index sequence table and analyze the patient's physiological state, psychological state and rehabilitation progress state one by one; The risk assessment model module is used to combine the patient's personalized information and the associated data of the rehabilitation indicator sequence table to construct a risk assessment model, calculate the patient's rehabilitation risk assessment coefficient, and assess the patient's current rehabilitation risk level; The warning notification module is used to process real-time data, preset the patient's risk level in combination with the risk assessment model and the severity of the risk, compare the warning threshold that matches the corresponding risk level, determine the patient's risk level, and automatically trigger the warning mechanism to generate warning information when the patient's rehabilitation risk assessment coefficient reaches the preset warning threshold; The monitoring adjustment module dynamically adjusts the monitoring strategy and warning threshold according to the patient's risk level and rehabilitation progress, and optimizes the monitoring frequency and parameters according to the patient's rehabilitation progress and feedback; The personalized intervention measures module formulates personalized intervention measures according to the risk assessment results and the patient's recovery status.

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