223 results about "Patient state" patented technology

The invention discloses a gait rehabilitation body-equipped robot time series data modeling optimization method based on frequency domain learning, and the method comprises the steps: obtaining multi-modal sensor data, carrying out the preprocessing, calculating gait parameters, and generating a patient state vector; enhanced features are obtained by applying multi-scale convolution and frequency domain processing; performing block processing and position coding to obtain a coding enhancement feature, and performing noise suppression processing to obtain a residual fusion feature; a final fusion feature is obtained by applying a self-attention mechanism and frequency domain alignment fusion processing; performing frequency domain decomposition and multi-scale memory filtering to obtain space-time fusion features; evaluating the rehabilitation progress, generating a gait correction vector and a correction time sequence curve, and outputting a smooth safety control sequence. According to the method, the influence of high-frequency noise is reduced, so that the robustness and accuracy of the model are improved; stable characterization of key gait features is realized, and the precision and consistency of the model in processing complex time sequence data are enhanced; and the control performance and the simulation precision of the gait rehabilitation robot are improved.

The invention relates to the technical field of physiological status monitoring, in particular to a rehabilitation assessment method and system based on patient status awareness, and the method comprises the following steps: collecting myoelectricity, heart rate and skin conductance signal data of a patient, normalizing time sequence data, setting a time window, calculating a mean value and a mean square error, and analyzing a signal covariance variation trend. And generating a cross-domain signal stability list. According to the method, the physiological signals of the patient are collected and analyzed, the method has important significance on improvement of the rehabilitation effect, the physiological signals such as myoelectricity, heart rate and skin conductance are collected and analyzed in real time, tiny changes of the state of the patient can be carefully captured, more accurate state evaluation and rehabilitation progress monitoring are achieved, and the rehabilitation effect is improved. The accuracy of data processing and the efficiency of signal decoding are ensured by analyzing the signal stability and optimizing and adjusting the time window, and the rehabilitation scheme is further adjusted according to the specific response of the patient, so that the rehabilitation process is more personalized and targeted.

The invention discloses an information management method and system for a smart ward, a medium and a program product, and relates to the field of electrical digital data processing.The method comprises the steps that physiological index data and behavior characteristic data of a target patient are collected, and a characteristic data sequence is constructed; analyzing each piece of data in the feature data sequence, and determining a current characterization behavior and a behavior change sequence of the target patient; obtaining individual information of a patient, and inputting the individual information of the patient, the current characterization behavior and the behavior change sequence into a behavior evaluation model to obtain a risk characterization behavior and a behavior risk score; determining an early warning level and an early warning response time limit based on the behavior risk score; when the early warning level is higher than a preset early warning threshold value, generating a task execution list according to the early warning response time limit and the risk characterization behavior, and sending the task execution list to the medical care terminal; and updating the behavior risk score according to an early warning response result returned by the medical terminal. By implementing the application, patient state monitoring of the smart ward can be optimized, and potential safety hazards can be avoided to a greater extent.

The invention relates to the technical field of medical systems, in particular to an intelligent gastrointestinal postoperative intestinal obstruction diagnosis and intervention decision-making system and method, and the system comprises a data preprocessing module, a multi-modal data fusion module, a gastrointestinal function recovery model construction module, a self-adaptive progressive reinforcement learning optimization module AHRL and a diagnosis and intervention recommendation module. The self-adaptive progressive reinforcement learning optimization module optimizes an intervention strategy layer by layer through a five-layer progressive algorithm from the steps of dynamic adjustment of feature weights, recursive state propagation, layered accumulation optimization, symptom similarity matrix generation, progressive weight reverse feedback and the like, deep fusion of key symptoms by multi-modal data is achieved, and the accuracy of the key symptoms is improved. And the intervention scheme is updated in real time according to the patient state change. In the intervention decision recommendation module, the system generates personalized diagnosis suggestions based on the optimized strategy, carries out real-time risk assessment, and provides continuous and dynamic intervention support for the patient.

The invention discloses an intelligent remote follow-up visit method and system for tumor patients, and relates to the technical field of medical intellectualization, and the method comprises the steps: collecting follow-up visit data through a remote medical terminal, carrying out the processing of the follow-up visit data, obtaining a physical sign index sequence and a symptom feature vector, and generating a dynamic follow-up visit evaluation score through a time sequence prediction model; performing semantic analysis on the electronic medical record data of the patient based on the medical knowledge map, extracting tumor stage classification data, chemoradiotherapy measurement data, weight nutrition indexes and laboratory inspection data, determining a basic risk coefficient, a cumulative dose ratio and a body state score, and generating a baseline follow-up assessment score through a prediction model; and according to the dynamic follow-up evaluation score and the baseline follow-up evaluation score, calculating a comprehensive follow-up grade and generating a next follow-up plan. According to the invention, the accuracy of patient state evaluation is improved, accurate configuration of medical resources is realized, and the quality and efficiency of remote follow-up visit are effectively improved.

A computational evidence platform extracts clinical concepts from medical evidence sources and creates a database of elemental diagnostic factors and elemental investigations links to medical conditions. Input from a person groups factors and investigations makes corrections and adds a ranking. Elemental factors and investigations do not include information specific to their associated conditions but include synonyms and a link to a medical ontology. A patient state is determined by extracting patient known diagnostic factors and investigation results from the patient chart. These known factors and results are matched to the database and a ranking of likely conditions are output. Next-best actions per condition are output by determining factors not yet known and investigations not yet performed. Next-best actions across conditions are determined by performing a recursive tree search of the database and assuming that unknown factors are now known to generate a score for each assumption.

A computational evidence platform extracts clinical concepts from medical evidence sources and creates a database of elemental diagnostic factors and elemental investigations links to medical conditions. Input from a person groups factors and investigations makes corrections and adds a ranking. Elemental factors and investigations do not include information specific to their associated conditions but include synonyms and a link to a medical ontology. A patient state is determined by extracting patient known diagnostic factors and investigation results from the patient chart. These known factors and results are matched to the database and a ranking of likely conditions are output. Next-best actions per condition are output by determining factors not yet known and investigations not yet performed. Next-best actions across conditions are determined by performing a recursive tree search of the database and assuming that unknown factors are now known to generate a score for each assumption.

A computational evidence platform extracts clinical concepts from medical evidence sources and creates a database of elemental diagnostic factors and elemental investigations links to medical conditions. Input from a person groups factors and investigations makes corrections and adds a ranking. Elemental factors and investigations do not include information specific to their associated conditions but include synonyms and a link to a medical ontology. A patient state is determined by extracting patient known diagnostic factors and investigation results from the patient chart. These known factors and results are matched to the database and a ranking of likely conditions are output. Next-best actions per condition are output by determining factors not yet known and investigations not yet performed. Next-best actions across conditions are determined by performing a recursive tree search of the database and assuming that unknown factors are now known to generate a score for each assumption.

A computational evidence platform extracts clinical concepts from medical evidence sources and creates a database of elemental diagnostic factors and elemental investigations links to medical conditions. Input from a person groups factors and investigations makes corrections and adds a ranking. Elemental factors and investigations do not include information specific to their associated conditions but include synonyms and a link to a medical ontology. A patient state is determined by extracting patient known diagnostic factors and investigation results from the patient chart. These known factors and results are matched to the database and a ranking of likely conditions are output. Next-best actions per condition are output by determining factors not yet known and investigations not yet performed. Next-best actions across conditions are determined by performing a recursive tree search of the database and assuming that unknown factors are now known to generate a score for each assumption.

A computational evidence platform extracts clinical concepts from medical evidence sources and creates a database of elemental diagnostic factors and elemental investigations links to medical conditions. Input from a person groups factors and investigations makes corrections and adds a ranking Elemental factors and investigations do not include information specific to their associated conditions but include synonyms and a link to a medical ontology. A patient state is determined by extracting patient known diagnostic factors and investigation results from the patient chart. These known factors and results are matched to the database and a ranking of likely conditions are output. Next-best actions per condition are output by determining factors not yet known and investigations not yet performed. Next-best actions across conditions are determined by performing a recursive tree search of the database and assuming that unknown factors are now known to generate a score for each assumption.

A computational evidence platform extracts clinical concepts from medical evidence sources and creates a database of elemental diagnostic factors and elemental investigations links to medical conditions. Input from a person groups factors and investigations makes corrections and adds a ranking. Elemental factors and investigations do not include information specific to their associated conditions but include synonyms and a link to a medical ontology. A patient state is determined by extracting patient known diagnostic factors and investigation results from the patient chart. These known factors and results are matched to the database and a ranking of likely conditions are output. Next-best actions per condition are output by determining factors not yet known and investigations not yet performed. Next-best actions across conditions are determined by performing a recursive tree search of the database and assuming that unknown factors are now known to generate a score for each assumption.

The invention relates to a patient examination item recommendation method based on a medical knowledge graph. The method comprises the steps that firstly, historical data in the medical field is acquired, key information is extracted from the historical data to generate an entity relation triple, and then a medical knowledge graph is constructed; then, generating a patient state sub-graph based on patient input information, processing the medical knowledge graph by adopting an improved TransD model, and determining unique representation of an entity-relationship pair; mapping the patient state sub-map to a unique representation space, obtaining vector representation of a patient and an examination item related entity, and calculating a similarity value between vectors to obtain a similarity value matrix; and finally, performing priority ranking on the inspection items according to the matrix, and generating a final recommendation list through threshold screening and combination with graph neural network verification, optimization and adjustment of priorities. By adopting the method, examination items can be accurately recommended for patients, doctors are assisted to improve the treatment efficiency, the queuing time of the patients is shortened, and the medical experience of the patients is remarkably improved.

The invention relates to the technical field of electronic prescriptions, in particular to a dynamic electronic prescription generation method and system based on artificial intelligence. The method comprises the following steps: collecting the latest physiological state parameter flow of a patient, carrying out real-time health state evaluation, and generating a personalized patient state map; historical medical records of a patient are extracted, time sequence pathological evolution tracking is carried out, and a pathological evolution trajectory is generated; performing multi-parameter time sequence difference comparison calculation and drug curative effect quantitative evaluation based on the personalized patient state map and the pathological evolution trajectory to obtain a curative effect evaluation report; performing allergic drug identification on the patient based on the historical medical record of the patient, and performing secondary drug screening to obtain a drug candidate set; and performing intelligent matching calculation on the drug candidate set according to the curative effect evaluation report, and performing combinatorial optimization analysis to generate a final effective electronic prescription. The electronic prescription is automatically updated and adjusted based on the state change of the patient, the risk of allergic prescriptions is reduced, and the safety of the prescriptions is improved.

The invention discloses an illness state reasoning and medical record generation method and system based on large model fusion and knowledge enhancement. The method comprises the following steps: acquiring and preprocessing medical data of a patient; performing feature extraction and medical entity recognition on the preprocessed medical data based on a pre-constructed medical entity recognition model to establish a patient state sub-graph; performing topology analysis and multi-hop reasoning on the patient state sub-graph based on a graph neural network to obtain a diagnosis candidate set and a corresponding reasoning evidence chain set; performing double-track parallel illness state reasoning based on the medical data and the reasoning evidence chain set to respectively generate inner track illness state probability distribution and outer track illness state probability distribution, performing weighted fusion on the inner track illness state probability distribution and the outer track illness state probability distribution to generate optimized illness state reasoning probability distribution, and generating a structured medical record with an evidence traceability identifier according to the optimized illness state reasoning probability distribution; according to the method, the problems of logic fracture, knowledge illusion and uninterpretability in large-model medical application are solved, and the accuracy and transparency of clinical diagnosis reasoning are remarkably improved.

The invention discloses a chronic disease management AI recommendation anti-illusion method and system based on a knowledge graph, and relates to the technical field of medical health artificial intelligence, and the method comprises the steps: obtaining chronic disease medical data of a patient, extracting a feature vector, and constructing a multi-modal health trajectory vector; constructing a chronic disease tag vector and a directional causal knowledge graph; executing map path search to obtain candidate treatment paths; constructing structured cue words, and inputting the structured cue words into the medical large language model to generate candidate schemes; and calculating a comprehensive illusion score to carry out anti-illusion judgment and correction, and outputting a credible personalized treatment recommendation. By constructing a directional causal knowledge graph, the causal reasonability and semantic consistency of treatment path retrieval are enhanced, and the matching precision of candidate paths and patient states is improved; by calculating the comprehensive illusion score and introducing the four-dimensional score item for anti-illusion judgment, the credibility of the output content of the large language model is improved, and the availability of AI recommendation in clinical aid decision making is guaranteed.

Disclosed are systems and methods for assessment, management, and treatment of congestion in heart failure. The systems and methods may include or relate to wireless implants, reader modules, and uses thereof, which are configured to measure hemodynamic parameters of a patient in different patient states and positions. For example, the reader module may be hands free. For example, the reader module may be used at home by a patient. The reader module may be able to take readings and measurements when the patient is in a multitude of positions, including but not limited to, the supine and the prone positions. The reader module may be able to communicate and receive data from an implanted device when in a certain proximity of the implanted device and may be able to store, analyze, and / or transmit data to a different wireless device over a network, for example. In an embodiment, the reader module may be portable and wearable, e.g., through use of a garment or strap.

The invention relates to the technical field of medical data processing, in particular to an intelligent liver, gall, pancreatic and spleen disease nursing management system which comprises a parameter selection module, a feature extraction module, a risk assessment module, a node deduction module and a collaborative distribution module. According to the invention, through correlation analysis of disease types and physiological parameters, establishment of a monitoring parameter screening mechanism, avoidance of ineffective parameter interference, dynamic identification of key indexes, and quantitative evaluation of parameter deviation degree and organ function influence, the real-time evaluation capability of patient states is improved; risk level evolution trend prediction and intervention matching degree analysis are adopted, the timeliness and adaptation degree of nursing intervention measures are optimized, task pressure index calculation and role permission recognition are utilized, dynamic allocation of nursing resources among multiple roles is achieved, the response efficiency of a management system to an emergency state is enhanced, and the risk level evolution trend prediction and intervention matching degree analysis are combined. And the collaboration and the real-time performance of the nursing process are improved.

The invention provides a medical resource dynamic allocation method and system based on patient state data driving. The method comprises the following steps: receiving a patient state data set of a target user; generating target recommended medical resources; generating a candidate hospital sequence; acquiring a first adjustable medical resource of the first hospital in a first preset time; generating a first adaptation index; and when the first adaptation index is greater than or equal to a preset adaptation index, triggering a resource allocation instruction, and sending the target recommended medical resource to a first hospital. The technical problem that the medical resource preparation time is long due to the fact that data cannot be transmitted to a hospital in real time and medical resource scheduling analysis is lacked in the prior art can be solved, the vital sign data of the patient is transmitted in real time, the hospital is matched through intelligent analysis, the medical resource allocation scheme is generated, and the medical resource allocation efficiency is improved. Before the patient arrives at the hospital, the hospital is prepared correspondingly, so that the technical effect of shortening the waiting time of the patient is achieved.

The invention discloses a brain critical care analysis system and method based on multi-data fusion, and belongs to the field of medical care data analys.The brain critical care analysis system constructs a patient state evaluation model, introduces physiological monitoring parameters into the patient state evaluation model for patient state evaluation, constructs a medicine care effect evaluation model, and improves the medical care effect. Introducing the drug adaptation degree and cerebral hemodynamic into a drug nursing effect model to evaluate the drug nursing effect, constructing a complication nursing effect evaluation model, and introducing the complication severity and complication nursing action association degree into the complication nursing effect evaluation model to evaluate the complication nursing effect. A comprehensive nursing effect evaluation model is constructed, the comprehensive nursing effect of the patient is evaluated, and the brain severe nursing effect is comprehensively evaluated through the patient state, the medicine nursing effect and the complication nursing effect.

Disclosed is a system that includes a computer-readable medium (CRM) coupling a variety of devices. The variety of devices can include one or more patient wearable sensor devices, a paired patient device, and a patient devices docking station. The system can also include a smart patient care cart, a nurse station, a lift tracker station, and / or an agnostic aggregation station. The system can also include a patient datastore, a facility datastore, a caregiver datastore, a device datastore, a patient state datastore, a patient management datastore, a facility management datastore, a caregiver management datastore, an electronic health record (EHR) system datastore, a health / fitness datastore, and an agnostic aggregation datastore.

The invention relates to the technical field of medical informatics, in particular to a neurosurgery patient comprehensive diagnosis and treatment system which comprises a multi-mode state monitoring module, a dynamic trend evaluation module, a consciousness function analysis module, an optimization nursing intervention module and a nursing risk early warning management module. According to the method, key features are extracted in combination with image information, health indexes and behavior changes, serialized monitoring of intracranial lesions, physiological fluctuations and behavior modes is achieved, vital signs and behavior change trends are fused, continuity and objectivity of patient state evaluation are improved, the accuracy of risk identification is enhanced, and the risk identification efficiency is improved. According to the method, a health state change rate evaluation mode is introduced, the prediction capability of function recovery potential is improved, an unstable region is dynamically identified and potential risks are pre-judged in combination with an original state trajectory and intervention response difference, continuous optimization of an intervention path and active avoidance of nursing risks are realized, and the collaboration and continuity of comprehensive management are enhanced.

The application discloses a kind of intelligent service systems of advanced cancer pain pain, including: analgesic device, massage piece, driving source and ice piece, ice piece is located inside the mounting seat side surface, ice piece is used to contact cooling massage ball;Push module is used to daily push the predetermined check quantization table to patient, check quantization table at least includes one or more of the following each item: diet condition, sleep condition, body pain condition, self cognitive ability and mental state;Evaluation module, evaluation module obtains patient state evaluation result according to the data of patient to check quantization table, and according to evaluation result, push management scheme to patient and adjust analgesic device analgesic scheme, the living condition of patient every day is obtained regularly by push module, and the individualized, centralized pain intervention scheme is formed by evaluation module, one aspect realizes the timely evaluation of patient life quality, while quantized mode is convenient for others to view specific change situation, to improve specific condition of patient in a targeted manner.

The invention discloses a self-adaptive wearing and monitoring method of an intelligent drainage monitoring system, and relates to the technical field of medical intelligent wearing, and the method comprises the steps: collecting the drainage part data of a patient through multi-modal biological characteristics, optimizing the wearing parameters through a particle swarm algorithm, and driving a shape memory polymer to adjust the equipment fitting degree, biochemical indexes, pressure and flow of drainage liquid are detected in real time through a micro-fluidic chip and various sensors, intelligent early warning of abnormity is achieved in combination with a self-adaptive neural network, parameters such as drainage negative pressure are automatically adjusted according to physiological signals of a patient, risks are predicted through a digital twinborn model, and a maintenance scheme is generated. According to the intelligent drainage monitoring system, self-adaptive wearing is achieved, and the comfort level of a patient is improved; real-time accurate monitoring and intelligent early warning are achieved, and medical safety is guaranteed; drainage parameters are automatically adjusted according to patient states, and treatment efficiency is improved; the intelligent dredging and anti-infection functions are achieved, the medical risk and cost are reduced, and the clinical application value is high.

The invention relates to the technical field of intelligent medical treatment, in particular to a basic nursing risk intelligent identification and early warning system and method. The system comprises a multi-source data perception and fusion module which is used for acquiring and fusing dominant risk data and implicit risk data of a patient in real time and outputting a structured patient state feature set; the risk intelligent identification core module simulates physiological state changes through a patient personalized digital twinborn model and is coupled with a risk conduction knowledge graph to deduce a risk evolution trajectory so as to realize intelligent identification of potential risks and intervention measure simulation; the intelligent early warning and intervention module is used for executing hierarchical early warning and active intervention linkage; and the man-machine collaborative feedback and self-evolution module continuously optimizes the system model and strategy through clinical feedback data. The problems that in the prior art, the risk perception dimension is single, early warning lags behind and intervention is disjointed are solved, and prospective and precise recognition, prevention and control of the nursing risk are achieved.

The invention provides a patient abnormal state identification monitoring early warning method and system, and relates to the technical field of patient state monitoring. The method comprises the following steps: acquiring a facial image sequence of a patient, tracking a texture direction and constructing a variation comparison sequence, sorting linkage differences to generate dynamic clues, extracting non-shielding track mark picture distribution, identifying life fluctuations in a segmented manner, generating a fluctuation list through cross pairing, establishing a mapping chain to mark centralized anomalies, and generating an early warning scheme. According to the method, the recognition precision of facial subtle changes is improved, region credibility is calibrated in combination with a shielding ratio, non-shielding continuous image regions are screened, a physiological signal change sequence is segmented through trend continuity and direction consistency, high-sensitivity fluctuation paragraphs are extracted, and the credible image regions and physiological data are subjected to cross pairing in time and space; and merging intersection signals, identifying continuous high-frequency abnormal segments, and constructing a multi-dimensional mapping chain, thereby realizing high-precision early warning and real-time monitoring of complex state changes.

The invention belongs to the technical field of medical artificial intelligence, and discloses an auxiliary system based on artificial intelligence medical data analysis. A hidden trend turning point in time sequence physiological index data is analyzed and identified through a micro-period sliding window, time axis accurate alignment of multi-modal medical data is realized based on the turning point, and a time-space synchronization matrix is constructed to quantify a time coupling relationship among different modal data. And extracting a curvature distribution and tangent angle sequence by adopting geometric characteristic analysis of a feature vector change trajectory, and constructing a multi-branch path prediction model of illness state evolution. The system monitors the fit degree change rate of the patient state and the prediction path in real time, scientifically identifies the switching time of the illness state evolution branch, foresight marks the optimal intervention opportunity, and generates personalized clinical intervention suggestions. According to the invention, the change from passive monitoring to active prediction is realized, the timeliness and accuracy of clinical intervention are improved, and the medical resource configuration is optimized.

The invention relates to the technical field of medical data processing, in particular to a multi-feature recognition based postoperative anesthesia patient state quantitative evaluation method. According to the method, the anesthesia postoperative state is divided into a deep anesthesia stage, a superficial consciousness recovery stage and a cognitive recovery stage based on clinical observation. In the deep anesthesia stage, physiological feature data are collected at high frequency, and a physical sign health index is obtained by calculating the standardized deviation between the physiological feature data and a normal reference value; in the shallow consciousness recovery stage, physiological feature data and unconsciousness behavior data are collected, a weighted model is constructed in combination with the physical sign health index, and an unconsciousness behavior response index is obtained; in the cognitive recovery stage, physiological feature data and subjective feature data are collected, and short-term trend prediction is performed on a cognitive recovery index based on a grey prediction model. Furthermore, multi-source feature data of the three stages are fused to construct a dynamic trajectory prediction model, the postoperative recovery trend and state change of the patient are comprehensively analyzed, and quantitative evaluation of the postoperative delirium risk is achieved.

The invention discloses a personalized drug combination sequence generation method and system, and relates to the technical field of medical information, and the method specifically comprises the steps: obtaining any medical clinical data; inputting any medical clinical data into a pre-trained drug sequence decision model to obtain a drug combination sequence; the drug sequence decision model is constructed by using a Bayesian inverse reinforcement learning algorithm; and outputting the medicine combination sequence. According to the method, Bayesian reasoning and inverse reinforcement learning are combined under a Bayesian inverse reinforcement learning framework, uncertainty can be processed, posterior distribution of a reward function is deduced, new data are continuously updated and learned, a sepsis treatment guide is modeled into a decision tree to be embedded into the model, and the decision tree is more accurate. The relation between the patient state and the medicine is further identified, favorable support is provided for generation of personalized medicine combination sequences, the working intensity of doctors is reduced, and the working efficiency is improved.

The embodiment of the invention discloses a skin state classification system based on multi-modal feature fusion. A specific implementation mode of the system comprises a patient state text information input device, a skin image acquisition device, a main control chip and a classification result display, wherein the patient state text information input device inputs a patient state structured text information sequence and a patient state unstructured text information sequence; the skin image acquisition device acquires a skin lesion image of a patient; the main control chip is configured to obtain an initial first state text feature vector and an initial second state text feature vector sequence; performing image feature extraction processing on the skin lesion image of the patient to obtain an initial skin lesion region feature vector; inputting the initial first state text feature vector, the initial second state text feature vector sequence and the initial skin lesion region feature vector into a skin state classification model; and the classification result display displays the skin state classification result. According to the embodiment, the classification accuracy of the skin state can be improved.

The invention relates to the technical field of data processing, in particular to an intelligent data processing method and system based on surgical nursing, and the method comprises the steps: firstly obtaining food intake and physiological indexes of a patient within a preset time, comparing the food intake and physiological indexes with a standard food intake set, and generating a food intake standard score; in conjunction with the physiological metric, the system determines a real-time feeding score and a total feeding score. By comparing the real-time and total feeding scores at different time points, the system finally generates a feeding balance score of the patient. According to the method, food intake and physiological data are effectively integrated, a scientific and quantitative food intake evaluation tool is provided for surgical nursing, nutrition management of a patient is optimized, nursing quality is improved, the absolute value of the food intake is considered in the evaluation, the relevance between time distribution of food intake and physiological indexes is concerned, and the evaluation accuracy is improved. Therefore, more comprehensive and in-depth patient state information is provided for nursing personnel. Especially, due to the introduction of a feeding balance degree score, an important basis is provided for formulating a more reasonable nutrition intervention scheme.