178 results about "Patient state" patented technology

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.

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 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.

A system and method for processing vital information are provided. The system includes a memory, a processor, and a display. The processor performs following operations: acquiring patient data of a patient; analyzing the patient data to obtain multiple analysis results, wherein the multiple analysis results at least include a first trend of change for first information and a second trend of change for second information extracted from the patient data; determining whether the first trend of change and the second trend of change satisfy a preset condition; determining a patient state of a patient related to the preset condition when the preset condition is satisfied; and displaying indication information which indicates a patient state, the first trend of change and the second trend of change. A correlation between various trend of changes of patient data is taken into consideration in determination of the patient state, which improves the determination accuracy.

The invention provides a closed-loop reinforcement learning diagnosis method and system based on a high-fidelity virtual clinical environment, and the method comprises the steps: 1, carrying out the structural processing of basic information and an examination record set of a patient, and obtaining a standardized examination and diagnosis data set; 2, training to obtain a high-fidelity clinical environment system; and step 3, based on the high-fidelity clinical environment system, obtaining a diagnosis agent through reinforcement learning training. A simulation examination result highly consistent with the state of a patient can be generated based on real electronic health record data, a diagnosis agent performs multiple rounds of interaction in a virtual environment, autonomously explores a diagnosis path, and performs strategy optimization through a double-reward mechanism, so that the crossing of a diagnosis model from static prediction to dynamic decision is realized, and the diagnosis efficiency is improved. And the diagnosis accuracy and the reasonability of examination recommendation are greatly improved.

The invention relates to the technical field of medical information smart wards, in particular to a smart ward management method and system based on the Internet of Things. Environmental parameters in a smart ward and physiological parameters of a patient in the smart ward are obtained; performing comprehensive analysis according to the environmental parameters and the physiological parameters to obtain corresponding analysis results; and according to an analysis result, a control instruction used for adjusting environment equipment in the smart ward is generated and / or corresponding guidance information is sent to the management end, so that the ward environment and the patient state are combined, and integrity and collaboration are realized. Intelligent management and real-time monitoring and response of the ward are realized through the control instruction and / or guidance information, the timeliness of medical services is improved, dynamic allocation of medical resources is facilitated, the resource utilization efficiency is improved, the operation process is greatly simplified, and the use experience of medical staff and patients is improved.

The invention relates to an intelligent platform management system carrying infusion port patients, and relates to the technical field of data management, and the system comprises a monitoring unit which is used for collecting impedance data, temperature data and medicine use time sequence data; the intelligent analysis unit is used for constructing a dynamic health baseline of the patient, calling a preset machine learning model based on the dynamic health baseline and outputting a complication risk level of the patient; the closed-loop execution unit is used for triggering a three-dimensional life behavior guidance scheme matched with the patient state based on the complication risk level, calculating a corresponding infusion port maintenance period based on the real-time smoothness score, and generating a medicine supply instruction in combination with medicine use time sequence data; and the hierarchical interaction platform is used for generating an early warning view containing the risk level of the patient and the key attention information based on the complication risk level, and pushing maintenance appointment suggestions and planning contents at the patient side based on the maintenance period of the infusion port. The system can improve the management efficiency and safety of the patients in the infusion port.

The invention discloses a multi-modal drug recommendation method and system based on large language model driving, and relates to the technical field of medical informatics and artificial intelligence. The invention aims to solve the limitation of an existing drug recommendation model in the aspects of patient characterization construction, drug multi-modal modeling and large language model application. Comprising the following steps: constructing patient characterization, and aligning a patient state and a potential medication space in a characterization learning stage through a cooperative prompt project driven by a large language model; multi-modal drug characterization is constructed, and clinical logic and chemical characteristics of drugs are deeply depicted through semantic expert and molecular structure expert dual-channel design; a'static anchoring dynamic 'time sequence reasoning mechanism is provided, context modulation is performed on a dynamic evolutionary clinical event sequence by utilizing a static treatment baseline formed by global medication history, and finally, an accurate and safe medication combination is generated through a label sensing prediction module. According to the method, the accuracy, safety and clinical logic self-consistency of drug recommendation are remarkably improved.

The invention discloses a medical record data association mining method and system based on reinforcement learning, and relates to the technical field of medical record data association mining, and the method comprises the following steps: obtaining medical record historical data, carrying out multi-modal feature extraction, and constructing a medical record data multi-modal state vector; constructing state input of a reinforcement learning agent based on the multi-modal state vector, constructing an action space and a reward evaluation mechanism, performing updating and iteration according to the reward evaluation mechanism, and outputting potential correlation information of the medical record; a world model for simulating a patient trajectory is introduced in the reinforcement learning process, patient state transition characteristics are learned according to medical record historical data, and a simulation sample for auxiliary training is generated for reinforcement training; performing interpretable output on the finally obtained medical record potential association information according to an enhanced training result, and constructing a medical record association network; the method effectively solves the problems that multi-modal heterogeneous data fusion is difficult, dynamic evolution of the disease course is difficult to model, and potential association interpretability is insufficient.

The invention discloses a visual asynchronous event triggering and calling method based on a medical information system, which comprises the following steps of: asynchronously acquiring multi-source data streams such as clinical events, equipment monitoring, doctor advice execution and patient states in the medical information system to form a multi-source asynchronous medical data stream set; generating a system event triggering association group based on the set and creating an asynchronous event scheduling strategy table; performing system event association degree calculation on the data stream to obtain a multi-dimensional asynchronous event association matrix; and carrying out system asynchronous time delay compensation and event chain visualization processing on the incidence matrix based on the scheduling strategy table, and finally generating a dynamic asynchronous event correlation topological graph. According to the invention, by constructing a system event detection operator set, establishing cross-flow system relevance calculation and establishing a priority-based asynchronous scheduling optimization mechanism, intelligent triggering, calling and early warning functions of medical system events are realized.

The invention discloses a big data-based digestive system department prediction analysis system. The system comprises a data acquisition module, a knowledge graph fusion module, a disease course evolution prediction module and an intelligent diagnosis and treatment decision module. The invention relates to the technical field of medical health information, in particular to a big-data-based predictive analysis system for the department of gastroenterology. According to the scheme, a physiological signal is reconstructed by using a multi-scale mask auto-encoder, and the signal and a text are aligned; mapping knowledge graph regularization is introduced, the joint code is mapped to a digestive system knowledge graph space, and medical semantic representation is obtained; constructing a time sequence attenuation dynamic graph through the dynamic memory graph, and adding graph convolution gating; an embedded differential equation module simulates inflammation and tumor marker evolution to realize accurate prediction; patient state vectors are constructed based on multi-modal coding, medicines are rapidly selected by using a scoring function, a treatment process is modeled as a Markov decision, and a strategy network, a value network and PPO optimization are combined to realize balance of curative effect and income.