191 results about "Care protocols" patented technology

The invention discloses a nursing decision scheme optimization analysis system based on artificial intelligence, and relates to the technical field of data processing. The system comprises a nursing knowledge base module, a data acquisition and preprocessing module, a nursing scheme matching degree analysis module, a nursing demand prediction module, a nursing scheme optimization module and a nursing scheme monitoring feedback module. According to the nursing decision scheme optimization analysis system based on artificial intelligence, the medical history, the diagnosis result, the physiological parameter data and the real-time nursing record multi-source information of the patient are integrated, comprehensive and dynamic evaluation of the health condition of the patient is achieved, deep mining and analysis are conducted on the data, and therefore more accurate nursing decision suggestions are obtained; individual nursing schemes are provided according to individual differences of patients, pertinence and effectiveness of nursing measures are ensured, in addition, execution conditions of the nursing schemes are monitored in real time, deviation is found and corrected in time, and scientificity and accuracy of nursing decision making are further improved.

The invention discloses an ear-nose-throat nursing scheme intelligent recommendation system based on cloud computing, and relates to the technical field of big data analysis. The intelligent recommendation platform is in communication connection with a data acquisition and preprocessing module, a knowledge graph construction module, a patient portrait construction module, a nursing scheme recommendation module, an evaluation and adjustment module and a user interaction and feedback module. According to the intelligent recommendation system for the ear-nose-throat nursing scheme based on cloud computing, deep analysis and mining are carried out on real-time physiological data, ear-nose-throat related symptoms, wound healing conditions and function recovery progress of patients by utilizing the powerful data processing capability of a cloud computing platform, individual differences and rehabilitation requirements of the patients are accurately recognized, and the intelligent recommendation system for the ear-nose-throat nursing scheme based on cloud computing is provided for the patients. And personalized nursing schemes are customized for the patients, so that the pertinence and effectiveness of nursing are improved, and the prolonged rehabilitation time and the discomfort of the patients caused by the improper schemes are greatly reduced.

The invention relates to the technical field of medical care, in particular to an intelligent query method for a nursing knowledge base, which comprises the following steps of: performing multi-form standardized processing on a nursing problem input by a user, deeply analyzing a query request by utilizing a semantic understanding model, and obtaining an intelligent query result based on an inference algorithm; the method comprises the following steps: automatically selecting related literatures, clinical data and nursing experience data from a nursing knowledge base, generating a preliminary nursing scheme candidate set in combination with a nursing professional knowledge graph, and performing personalized screening and optimization on the scheme by using an intelligent recommendation model in combination with the current illness state, treatment progress and historical nursing data of a patient; the intelligent recommendation model is self-optimized through user feedback, and the nursing scheme recommendation precision is continuously improved. The nursing knowledge base automatically fuses newest medical research results and clinical nursing practice data. The problems that in the prior art, nursing scheme recommendation accuracy is low, updating is not timely, and the individuation degree is insufficient are effectively solved.

The invention relates to the technical field of medical monitoring, in particular to a multi-mode intelligent management system for monitoring and preventing stress injury. The system comprises a multi-modal data acquisition and preprocessing module, a deep fusion risk prediction model module, an intelligent path planning and resource allocation module, a personalized intelligent intervention module and a data security module, so as to acquire time sequence physiological data and generate a body pressure thermodynamic diagram in real time, and realize standardization through normalization; a risk prediction model is generated through modeling, the contribution degree of each modal feature is calculated, and a risk level is determined according to a preset early warning grading mechanism; an optimal path is generated through intelligent planning, a nursing scheme is dynamically optimized and generated according to the risk level, and the nursing scheme is encrypted and transmitted to a cloud platform through HTTPS to be regularly subjected to security vulnerability scanning and repairing. According to the invention, the skin condition, the body position change and the pressure distribution of the patient can be accurately monitored, so that the closed-loop management of the pressure injury is realized.

The invention relates to the technical field of medical aid decision making, in particular to a system and method for generating an individualized nursing scheme based on the comprehensive evaluation condition of the elderly. According to the method, various physiological time sequence data of the old people in a specific life scene are collected and the periodic fluctuation characteristics of the data are extracted, so that the subtle change trend of the daily rhythm of the old people can be accurately identified, the dynamic influence of the chronic disease progress on the physiological state is revealed by further comparing the rhythm deviation degree and continuity in different time periods, and the accuracy of the physiological state of the old people is improved. Then, in combination with the trend change rate and directionality thereof, an interaction mode between physiological changes and chronic disease indexes is described in continuous observation, so that the measurement of the coupling adaptation degree between individual physiological responses and chronic disease conditions is achieved, through normalized amplitude difference and matching interval screening, individual physical ability and chronic disease adaptation labels are accurately given, and the accuracy of the physical ability and chronic disease adaptation labels is improved. And further comprehensively measuring the adaptation priority of each intervention element in the individual in a multi-dimensional scoring mode, and constructing a nursing intervention combination oriented to a specific crowd state.

The invention discloses a nursing system and a monitoring method thereof, and the method comprises the following steps: collecting the physiological data of a patient through an intelligent sensor, forming a dynamic health data flow, carrying out the analysis in combination with historical health data, extracting key health features based on a time sequence attention mechanism, and carrying out the monitoring of the key health features. A machine learning model is adopted to predict the health state of a patient, a health mode between the patient and a similar patient is calculated to generate a personalized nursing scheme, and combined optimization is performed on health state prediction and a nursing recommendation scheme in combination with a time sequence collaborative multi-task learning framework, so that dynamic adjustment of the nursing scheme is realized; the system also includes an adaptive care intervention module that optimizes a care intervention strategy according to the latest health data of the patient and continuously iteratively optimizes the care scheme based on the care execution feedback. According to the invention, a complete nursing closed loop from data acquisition, analysis, prediction and recommendation to optimization and intervention is constructed, and accurate monitoring and personalized nursing management of the health state of the patient are realized.

The invention discloses an intelligent auxiliary fracture monitoring system for orthopedic nursing, which comprises the following steps that: the system acquires mechanical parameters, movement track data and biological signals of a fracture patient through a multi-mode sensor, and intelligently analyzes the data by using a deep learning algorithm; generating a fracture state diagnosis result and recovery trend prediction data; the low-power-consumption wireless communication module realizes real-time transmission of diagnosis data and supports remote system parameter adjustment; the personalized nursing scheme generation module generates a rehabilitation plan, a load adjustment strategy and an auxiliary device configuration scheme through an optimization algorithm based on the patient biomechanical model and the diagnosis result; and the adaptive feedback adjustment module dynamically adjusts a data acquisition strategy and processing parameters to ensure the real-time response capability of the system to the state of the patient. The system is suitable for the technical field of intelligent auxiliary monitoring, and can realize real-time monitoring, intelligent diagnosis and personalized rehabilitation guidance of fracture patients.

The invention discloses a traditional Chinese medicine nursing management system and method based on a cloud follow-up visit management platform, and belongs to the technical field of intelligent nursing. The management system comprises the following modules: a dynamic data acquisition module for acquiring physiological parameters, living habits and environmental data of a patient in real time and performing primary processing; the dynamic health archive construction module is used for constructing and continuously updating an individualized health archive of the patient based on the data collected in real time; the traditional Chinese medicine knowledge map and reasoning module depends on the traditional Chinese medicine nursing knowledge map and combines an intelligent reasoning technology to realize accurate traditional Chinese medicine syndrome differentiation and treatment analysis and automatically generate a personalized nursing scheme; the multi-dimensional patient feedback module is used for constructing a feedback mechanism fusing objective data, patient subjective feeling and medical care evaluation so as to extract key nursing effect information; and the nursing scheme iterative optimization module dynamically adjusts and optimizes the traditional Chinese medicine nursing scheme based on the feedback data, and realizes personalized and self-adaptive continuous improved nursing.

The invention relates to the technical field of nursing scheme intelligent recommendation systems, in particular to a cardiology patient personalized nursing scheme intelligent recommendation system optimization method, which comprises a dynamic rare case recognition engine for accurately capturing and recognizing cardiology rare cases by using medical data and text data, and comprises a data preparation module, a data retrieval module, a data processing module and a data processing module, the candidate sample classification and recognition module is used for recognizing whether rare cases exist or not, and the problems existing in the prior art are effectively solved by introducing innovation modules such as a dynamic rare case recognition engine, a knowledge graph builder and a rare case simulation generator. According to the invention, continuous learning and updating can be realized, rare cases can be accurately identified, and customized nursing suggestions can be provided based on individual features of patients. Meanwhile, the decision-making process of the system has good interpretability, decision-making basis can be provided for doctors, and usability and credibility of the system in clinical practice are greatly improved.

The invention discloses a patient rehabilitation monitoring method and system based on nursing data, and particularly relates to the technical field of rehabilitation monitoring. Core rehabilitation indexes are extracted by acquiring and classifying nursing data of a patient; analyzing the nursing data, identifying the current rehabilitation stage of the patient and evaluating the matching degree of the rehabilitation target and the core rehabilitation index; performing trend analysis on the time sequence of the nursing data, identifying abnormal data and evaluating the interference degree on the rehabilitation process; judging whether the rehabilitation progress of the patient meets the expectation or not according to the evaluation results of the matching degree and the interference degree; when the rehabilitation progress does not conform to the expectation, analyzing the demand change of the patient and evaluating the help effect of the nursing scheme; by comprehensively analyzing the matching degree, the interference degree and the nursing effect, the rehabilitation effect of the patient is evaluated, whether the nursing scheme needs to be adjusted or not is judged, individuation, accuracy and effectiveness of the nursing scheme are improved, the rehabilitation process of the patient can be accelerated, and the nursing quality is improved.

The invention discloses an ear-nose-throat nursing scheme recommendation method based on a knowledge graph, and relates to the technical field of ear-nose-throat nursing scheme recommendation, and the method comprises the following steps: 1, based on medical research results, clinical cases, treatment methods and nursing key points in the ear-nose-throat field, collecting a data source, and establishing an initial ear-nose-throat knowledge graph based on the collected data source. According to the ear-nose-throat nursing scheme recommendation method based on the knowledge graph, a monitoring mechanism is set, the situation that a nursing scheme is inaccurate due to old knowledge is avoided, expert verification is carried out on the inclusion of new knowledge through expert verification and scientific research trend monitoring, accuracy and reasonability are ensured, newly appearing research hotspots are included in the knowledge graph, and the knowledge graph recommendation efficiency is improved. Therefore, the nursing scheme recommendation is more frontier and more scientific, the newly added knowledge is ensured not to conflict with the existing knowledge graph, and the stability and reliability of the knowledge graph are ensured, so that a solid foundation is provided for generating an accurate nursing scheme.

A wearable electronic device, a system and methods of monitoring with a wearable electronic device. The device includes a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources, as well as a wireless communication sub-module to selectively transmit an LPWAN signal to provide location information based on the acquired data. The device may also include one or more sensors to collect one or more of environmental data, activity data and physiological data. The device may transmit some or all of its acquired data to a larger system, including a cloud-based server to, in addition to providing location-based data, be used as a part of a predictive health care protocol to correlate changes in acquired data to salient indicators of the health of a wearer of the device. In one form, the predictive health care protocol uses a machine learning model.

The invention discloses a rehabilitation nursing auxiliary system based on multi-modal data driving. The problems that in the prior art, data collection is single, a nursing scheme lacks individuation, and a feedback mechanism is insufficient are solved. The system comprises a multi-modal data acquisition module, a data processing and fusion module, an intelligent analysis and decision support module, a feedback and adaptive adjustment module and a patient interaction and nurse assistance interface module. According to the system, physiological data, behavior data, image data and environment data of a patient are collected in real time through Internet of Things equipment, cross-modal features are extracted through a deep learning model, and a comprehensive state vector S (t) is generated. Based on a reinforcement learning algorithm, a personalized nursing scheme C (t) is dynamically generated, and a multi-modal data weight theta and the nursing scheme are optimized through patient historical data and real-time feedback F (t); through the rehabilitation progress evaluation function P (t), the system can adjust the nursing plan in real time, and the rehabilitation effect is improved.

The invention discloses a clinical depression and melanoma patient nursing intervention system and method, which utilizes big data and artificial intelligence technology to establish a digital twinborn model according to the information, illness state, depression state and the like of a patient, collects existing cases for training to continuously optimize the model, and improves the precision of the digital twinborn model. And then similar cases are recommended by utilizing the database, the patient information and the digital twinborn model, a nursing scheme is generated, and then the nursing scheme is input into the digital twinborn model to be tested and predicted and is continuously improved. According to the mode, the efficiency of making the nursing scheme can be greatly improved, personalized customization can be carried out according to the actual condition of the patient, and the nursing effect is greatly improved. Meanwhile, the physiological data of the patient is detected by adopting the wearable equipment, whether the patient is in a depression state or in a depression mood is speculated by utilizing the physiological data, and psychological nursing is carried out through nursing intervention measures set in advance, so that the depression state of the patient is effectively improved.

The embodiment of the invention provides a trauma nursing scheme generation method and system based on artificial intelligence, and the method comprises the steps: obtaining first basic medical data of a target object to be subjected to trauma nursing, and the first basic medical data comprises individual feature data, gene data, trauma text data and medical history text data; and performing data preprocessing and reconstruction operation on the first basic medical data to generate second basic medical data of the target object. And inputting the second basic medical data into a pre-constructed nursing analysis model to obtain at least one subsequent nursing stage label of the target object. And based on the subsequent nursing stage label, a nursing knowledge base and historical nursing data of a plurality of reference objects, determining a wound nursing recommendation scheme of the target object, thereby improving the accuracy of wound nursing scheme generation.

The invention relates to the technical field of medical care, and provides a patient risk dynamic assessment system in medical care teaching, which comprises a data acquisition module used for acquiring multi-modal image data of a postoperative gastric organ anastomosis area of a patient; the data processing module is used for preprocessing the multi-modal image data, establishing a three-dimensional model of organs around the anastomotic stoma and analyzing edge features of the anastomotic stoma; and the risk assessment module is used for constructing an anastomotic stoma state assessment model and outputting comprehensive scores of an anastomotic stoma blood perfusion condition, a tissue metabolism abnormal probability, an anastomotic stoma leakage risk, anastomotic stoma scar hyperplasia and healing trend prediction. A quantum dot fluorescence image, a Raman spectrum image, a CT image and an intraoperative high-definition white light image are enhanced through the system and evaluated, a nursing scheme is obtained in combination with opinions of medical staff, and compared with manual evaluation of the medical staff, the accuracy is higher, the feasibility of the nursing scheme is higher, and medical nursing use is facilitated.

The invention discloses an AI-based postoperative rehabilitation nursing system and method for old people, relates to the technical field of medical treatment, and aims to calculate a rehabilitation coefficient of a current nursing period based on a feature set, accurately evaluate rehabilitation states of a patient in different nursing periods and intelligently judge whether current nursing intervention needs to be adjusted or not. By dynamically optimizing the intervention strategy, the problem of excessive or insufficient intervention in traditional nursing is avoided. And the intervention optimization instruction is received and executed, and the intensity adjustment coefficient of the nursing intervention in the next time period is automatically generated, so that the intelligent adjustment of the nursing intensity is realized, the nursing scheme is more personalized, and the intervention blindness is reduced. In the optimized nursing intervention process, physiological and metabolic parameters of the patient are continuously monitored, an optimized feature set is generated, the nursing intensity is further adjusted, a closed-loop nursing process is formed, and the closed-loop dynamic optimization mechanism improves the nursing response speed and adjustment precision and greatly shortens the rehabilitation cycle of the patient.

The invention provides a wound nursing intelligent decision-making method and system based on high-precision image recognition. The method comprises the following steps: in response to a received wound monitoring request, extracting a patient identifier, wound image information corresponding to a multi-angle high-precision image, wound background information and multi-modal sensor information from the wound monitoring request; a pre-trained three-dimensional convolutional neural network model is used for automatically recognizing the wound state, and a wound recognition result is obtained; based on the wound image information and the wound background information, assisting in positioning through an augmented reality technology; performing wound healing environment assessment based on the information of the multi-modal sensor; retrieving matched nursing strategy information from a preset nursing scheme database based on patient historical information and personalized treatment preference information corresponding to the patient identifier, the wound identification result, the wound positioning information and the wound healing environment evaluation information; and pushing the nursing strategy information. According to the scheme, the wound nursing quality is improved.

The invention discloses a self-adaptive analysis nursing method based on a wound image. The method comprises the following steps: S1, acquiring multi-modal image data of a wound; s2, performing fusion processing on the acquired multi-modal image data to generate a fused image; s3, analyzing the fused image by using a deep learning target detection model, identifying boundary, type, area and depth information of the wound, and forming a wound information set; s4, according to the identified wound information set, combining the physical condition data of the patient, and generating a model through a self-adaptive nursing strategy to formulate a personalized nursing scheme; s5, in the nursing process, the change condition of the wound is monitored in real time, the multi-modal image data are collected again at regular intervals, the steps S2 to S4 are repeated, and the nursing scheme is dynamically adjusted according to a new analysis result. According to the method, wound information is accurately obtained, a personalized nursing scheme is formulated, dynamic adjustment is achieved, and the accuracy, effectiveness and intelligent level of wound nursing are remarkably improved.

The invention discloses a pediatric nursing quality prediction and optimization method based on transfer learning, and the method comprises the following steps: S1, collecting target pediatric nursing data, and carrying out the preprocessing; s2, selecting source data, and extracting source data features; s3, constructing and training a nursing quality prediction model; s4, carrying out migration and fine tuning on the nursing quality prediction model by adopting an improved fine tuning method; s5, inputting target pediatric nursing data to the fine-adjusted nursing quality prediction model, calculating a nursing quality index, and evaluating a prediction result; s6, generating a nursing scheme based on the prediction result, and optimizing the nursing scheme according to the execution effect; and S7, applying the optimized nursing scheme to actual nursing work, and establishing an updating mechanism, so that the nursing quality prediction model is combined with new data to carry out iterative optimization. The pediatric nursing quality prediction and optimization are realized by adopting transfer learning and deep learning methods, and the pediatric nursing quality prediction and optimization method has the advantages of high data adaptability, high prediction accuracy and intelligent optimization of the nursing scheme.

The invention discloses an integrated management system and method for postoperative care of orthopedic patients, and relates to the technical field of medical treatment. Comprising a bone mineral density dynamic monitoring value, six-dimensional mechanical parameters of a joint movement track, emotion spectrum characteristics in voice interaction, microenvironment temperature and humidity changes collected by wearable equipment and skin surface pressure distribution; inputting the recovery data into a multi-modal decision-making model based on orthopedic biomechanics improvement, and generating a nursing scheme including a skeleton stress safety interval, a virtual rehabilitation scene dynamic parameter and an interdisciplinary intervention priority; the scheme parameters are dynamically optimized through a layered reinforcement learning framework special for the orthopedics department, and the deformation logic of the 3D printing brace and the correction strategy of the nursing robot are synchronously controlled; based on the postoperative complication space-time prediction map, a multidisciplinary collaborative response instruction strictly matched with the rehabilitation stage of the patient is triggered.

The invention belongs to the technical field of medical treatment, and particularly relates to a colorectal cancer patient tracking nursing method and system, and the method comprises the steps: obtaining the physiological data of a colorectal cancer patient; analyzing the physiological data, and determining a nursing target and a nursing scheme of the colorectal cancer patient; acquiring nursing data in real time when the colorectal cancer patient is nursed according to the nursing scheme; reading target data in the nursing target, and calculating a nursing deviation between the nursing data and the target data; adjusting the nursing scheme based on the nursing deviation to obtain an optimized nursing scheme, and continuously tracking the nursing condition of the colorectal cancer patient based on the optimized nursing scheme. According to the method, the nursing scheme is intelligently generated according to the postoperative condition of the colorectal cancer patient, the nursing scheme is optimized in real time according to the actual nursing condition, nursing measures matched with the current postoperative condition of the colorectal cancer patient can be provided, other diseases caused by inaccurate and incomplete nursing are avoided, and unnecessary injury to the patient is avoided.

The invention relates to the technical field of medical care, and particularly discloses an oral cancer perioperative nursing decision method, which comprises the steps of image acquisition, model generation, preoperative evaluation, dynamic nursing, risk evaluation and decision support. According to the scheme, the tumor area is extracted from the three-dimensional image data based on the U-Net model, the tumor area is separated from surrounding healthy tissue, complex tumor forms are automatically processed, higher segmentation precision and robustness are provided, and finally the accurate three-dimensional anatomical model is generated; according to the scheme, the individualized data, the real-time physiological indexes and the treatment progress of the patient are obtained, the intelligent optimization algorithm is used for calculating the priority of each nursing measure, the individualized nursing decision is dynamically generated according to the priority, the most suitable nursing scheme is automatically provided for the patient, and individualized requirements of different patients are better met.

The invention relates to the technical field of traditional Chinese medicine nursing or health improvement, in particular to a method for generating a traditional Chinese medicine personalized nursing scheme for preventive treatment of diseases. The method comprises the following steps: firstly, screening out all initial reference individuals of a current individual based on a similar condition between corresponding physique information of the individuals; further determining all concerned symptoms and corresponding concerned coefficients of the current individual according to the universality and change condition of each symptom of the current individual in the initial reference individual; screening out all reference individuals according to the attention coefficient of the common attention symptom of the current individual and each initial reference individual; and finally, generating a personalized nursing scheme of the current individual based on the nursing schemes of all the reference individuals. According to the method and the device, the reference individuals capable of providing similar nursing references are accurately screened out from the historical disease prevention individuals based on the similar physical conditions of the individuals and the similar conditions of the symptoms which are mainly concerned during nursing, so that the generation effect of the personalized scheme of the current individual is improved.

The invention discloses a chronic wound nursing scheme recommendation method and system based on clustering and a knowledge graph, and relates to the technical field of chronic wound nursing, chronic wound nursing associated data is collected to obtain chronic wound nursing multi-source data, and the chronic wound nursing multi-source data is preprocessed; and analyzing the preprocessed chronic wound nursing multi-source data, clustering the core demand problem and the nursing scheme data, and establishing a correlation database between the core demand problem and the nursing scheme. Through a technical path from multi-source data fusion to semantic cluster generation by layer clustering to cluster-level map construction to dynamic semantic matching, three major pain points of knowledge fragmentation, inaccurate demand matching and lack of recommendation basis in chronic wound nursing are solved. Compared with a traditional method, image volume accumulation captures multi-dimensional association, and semantic noise is reduced through cluster mapping; the evidence chain runs through the whole process of problems, schemes, wound types and literature sources.

The invention discloses a chronic disease patient nursing management system based on big data, which relates to the technical field of medical nursing and comprises a disease course development analysis unit, a patient health analysis unit, an online diagnosis and treatment unit, a nursing management unit, a nursing recommendation unit and a nursing correction unit. According to the invention, the disease course development model of the chronic disease is constructed according to the disease course development data, the disease development evaluation trend is graded and judged according to the physiological data of the patient, the judgment result is corrected through the online diagnosis and treatment unit, and the matched nursing list is obtained according to the judgment result. The nursing list is sent to a remote medical terminal of a nurse and an intelligent mobile terminal of a family member of the chronic disease patient, physiological data of the patient is obtained after each nursing judgment period is completed, the correction trend of the nursing influence coefficient is judged, and the effect of the nursing scheme is evaluated. The nursing list is timely corrected according to the chronic disease development process of the patient, the disease development condition of the patient is better matched, and chronic disease deterioration is inhibited.

The invention discloses a post-radiotherapy patient nursing auxiliary decision-making system based on artificial intelligence, and relates to the field of medical rehabilitation nursing, and the system comprises a management and control module which serves as a core management end of each function module and carries out the editing and sending of a control instruction; the data archive library is used for storing all the collected user personal data and analysis data thereof; the data acquisition module is used for acquiring basic physique data, illness state related data and radiotherapy plans of the patient through a preset period; individual data of patients are analyzed through a prediction model, personalized nursing suggestions are generated based on medical history, health indexes and radiotherapy plans, personalized nursing schemes are provided for each patient according to curative effect analysis of similar patients, customization is carried out according to unique illness states, physiques and treatment plans of the patients, professional knowledge and data in the medical field are combined, and the patient health management system is established. Potential abnormal changes are output, and patients and medical staff are helped to make timely decisions.

A wearable electronic device, a system and methods of monitoring with a wearable electronic device. The device includes a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources, as well as a wireless communication sub-module to selectively transmit an LPWAN signal to provide location information based on the acquired data. The device may also include one or more sensors to collect one or more of environmental data, activity data and physiological data. The device may transmit some or all of its acquired data to a larger system, including a cloud-based server to, in addition to providing location-based data, be used as a part of a predictive health care protocol to correlate changes in acquired data to salient indicators of the health of a wearer of the device. In one form, the predictive health care protocol uses a machine learning model.

The invention discloses a rehabilitation nursing intelligent monitoring method and system based on image acquisition, and belongs to the technical field of rehabilitation nursing intelligent monitoring, and the method comprises the steps: collecting limb image data of a patient and a synchronous pain response value to generate a monitoring model; processing the current image data based on a monitoring model to evaluate and output a pain risk value; analyzing the fluctuation trend of a pain response curve formed based on the pain risk value to generate and output a nursing scheme; collecting subsequent image data in the process of executing the nursing scheme, monitoring a limb recovery state and outputting recovery feedback information to adjust the nursing scheme; according to the method, continuous assessment of pain risks is achieved by establishing quantitative association of visual features and pain responses, the nursing scheme is generated based on dynamic fluctuation trend analysis, a closed-loop management process is formed by recovering feedback information, and the efficiency and safety of rehabilitation nursing are improved.

The invention discloses a neurology department clinical nursing potential safety hazard analysis method and device, and relates to the technical field of clinical nursing potential safety hazard analys.The method comprises the steps that physiological data and behavior data of a patient are collected, and multi-source data are preprocessed through weighted Kalman filtering; performing multi-dimensional fusion on the processed data, calculating a comprehensive health score by adopting a weighted average method, and evaluating the potential safety hazard of the patient in combination with a convolutional neural network; and based on the comprehensive health score, generating a personalized nursing scheme, and providing nursing decision support for medical staff. According to the clinical nursing potential safety hazard analysis method for the neurology department, the physiological data and the behavior data are combined, multi-dimensional data fusion is carried out through weighted Kalman filtering and the convolutional neural network, the accuracy of patient health assessment is improved, noise and errors are reduced, and the consistency and reliability of the data are ensured.