28 results about "Clinical state" patented technology

A dialogue system, comprising: an input configured to obtain input data relating to speech or text provided by a user; an output configured to provide output data relating to speech or text to a user; and one or more processors, the one or more processors being configured to: receive, by way of the input, input data relating to speech or text provided by a user; receive, at a first module, structured information comprising information relating to a clinical state of the user, the structured information being generated from the input data, the first module comprising a subject understanding module and a subject recommendation module, wherein the subject understanding module comprises one or more subject understanding models, each of the one or more subject understanding models configured to take as input the structured information and provide as output subject profile information; generate, at the subject understanding module, subject profile information based on the structured information; determine a subject recommendation comprising an intervention for the user, determining the subject recommendation comprising providing the subject profile information as input to the subject recommendation module; and output, by the way of the output, system responses as a part of a dialogue with the user, the system responses delivering an intervention.

A dialogue system, comprising: an input configured to obtain input data relating to speech or text provided by a user; an output configured to provide output data relating to speech or text to a user; and one or more processors, the one or more processors being configured to: receive, by way of the input, input data relating to speech or text provided by a user; receive, at a first module, structured information comprising information relating to a clinical state of the user, the structured information being generated from the input data, the first module comprising a subject understanding module and a subject recommendation module, wherein the subject understanding module comprises one or more subject understanding models, each of the one or more subject understanding models configured to take as input the structured information and provide as output subject profile information; generate, at the subject understanding module, subject profile information based on the structured information; determine a subject recommendation comprising an intervention for the user, determining the subject recommendation comprising providing the subject profile information as input to the subject recommendation module; and output, by the way of the output, system responses as a part of a dialogue with the user, the system responses delivering an intervention. [FIG. 14(a)]

The invention provides an ICU patient gastrointestinal complication risk prediction method and system based on time sequence data and a neural network. The method comprises the following steps: collecting time sequence physiological monitoring, static clinical and medical event data with timestamps of a patient, and constructing a dynamic time sequence knowledge graph in real time to represent clinical state evolution; map semantic embedding features are extracted from the map and fused with time sequence dynamic features extracted from the physiological data and static risk features extracted from the static data, and the fusion process is guided by map entity association; historical data and the corresponding atlas are used for training the risk prediction model, and atlas attenuation type label propagation based on atlas association is carried out on training labels according to the atlas structure and the time sequence path during training so as to optimize parameters; and inputting real-time data of a patient to be predicted into the trained model, and outputting a risk prediction result of gastrointestinal complications in a future time window. According to the method, the dynamic knowledge graph and deep learning are fused, and the accuracy and interpretability of risk prediction are improved.

This application provides a multi-agent, multimodal collaborative method, device, equipment, and storage medium for diagnosing the condition of patients in the intensive care unit (ICU), belonging to the field of medical artificial intelligence and intelligent analysis technology for intensive care. The method includes: receiving multimodal clinical monitoring data in an ICU setting, including bedside medical images, continuous life time series, and critical care clinical text; identifying and adaptively routing the input data through a modality detection agent, distributing it to corresponding domain expert agents for pathological and physiological feature extraction; constructing a patient-centric graph structure representing the dynamic clinical condition of ICU patients through a knowledge graph agent based on the features output by each domain expert agent, this graph structure integrating structured medical knowledge of entity extraction and relational reasoning; further, inputting the constructed graph structure and original multimodal features into a collaborative agent, generating the final ICU patient condition diagnosis result (such as mortality prediction, ICU long-stay prediction, etc.) and a traceable reasoning path through graph traversal and collaborative reasoning. This application effectively breaks down data silos between different monitoring devices in the intensive care unit environment, dynamically captures cross-modal associations specific to critically ill patients, and significantly improves the accuracy of critical care clinical diagnosis and medical trust.

A computer-implemented method of generating sensor data during an artificial procedure that can be used to train an artificial intelligence (AI) module is presented. The method comprises the steps of: providing a clinical data structure describing a plurality of clinical states of a clinical procedure and defining transitions between the plurality of clinical states, thereby allowing different pathways of the clinical procedure (step S1); providing at least one geometric model for modeling a surgical scene in one or more of said clinical states of said clinical procedure (step S2), said at least one geometric model comprising a plurality of geometric model parameters and boundary conditions of said geometric model parameters; wherein the geometric model parameters describe the surgical scenario in the clinical state of the clinical procedure, and wherein a specific combination of the geometric model parameter values defines a specific surgical scenario for the clinical state of the clinical procedure; the method further comprises the steps of defining a single path of the clinical procedure by selecting at least one transition between a first clinical state and a second clinical state of the clinical data structure (step S3); providing, as input to the geometric model, a set of geometric model parameter values for the at least one geometric model for the first and second clinical states of the defined single path, the first and second single surgical scenarios are modeled (step S4), and during-artificial surgical sensor data for the single path is generated based on the modeled first and second single surgical scenarios (step S5).

According to the clinical patient intelligent sickbed fused with the AI voice robot and the application method of the clinical patient intelligent sickbed, the voice intelligent sickbed is designed, a pressure sensor (used for sensing the clinical state of a patient), the AI voice robot, scanning equipment, a terminal and the like are integrated, and the clinical time of the patient can be sensed; and activating the voice robot to perform intelligent questioning and answering and collecting questioning and answering pair information of the patient. Meanwhile, the sensor scanning equipment scans and records patient information. The AI voice robot can replace a nurse to complete basic inquiry, examination and other work when a patient is clinically examined, so that nursing resources are saved, clinical inquiry or nursing work of an assembly line is removed, and the nursing pressure of a hospital is relieved.

The invention discloses a medical knowledge graph-driven diagnosis and treatment decision support system, and relates to the technical field of medical knowledge graph data processing, and the method comprises the steps: obtaining a clinical data flow of a patient, mapping the clinical data flow into an updating instruction for a pre-constructed medical knowledge graph, and dynamically updating the attribute data of entity nodes and relation edges; generating a vectorization representation of the node by taking the target entity node as an original point according to the attribute data of the relation edge between the target entity node and the associated node; screening nodes to be compounded according to a preset rule, and calculating a composite vector between the target core node and the vectorized representation of the nodes to be compounded; and finally, generating a clinical state evaluation result according to the mathematical characteristics of the composite vector. According to the method, the complex medical relationship is quantified into the computable vector space model, dynamic, objective and quantitative evaluation and risk early warning of the clinical state of the patient are achieved, and the accuracy and foresight of diagnosis and treatment decisions are remarkably improved.

The invention discloses a clinical tag labeling method, system and equipment based on a standardized time sequence and a medium, and relates to the technical field of clinical event fusion and dynamic tag generation, and the specific steps are as follows: obtaining clinical event data of a patient from different clinical business systems in real time, dividing cycle attribution for the clinical event data, and determining the clinical event data; and establishing a dynamic weight calculation model to allocate dynamic weight values, establishing a periodic clinical state inference model by adopting a convolutional neural network and training the periodic clinical state inference model, inputting weighted summary clinical feature vectors in a current patient period according to the trained periodic clinical state inference model, and outputting clinical tags corresponding to patients. According to the invention, clinical label labeling based on a standardized time sequence is realized, multi-source clinical event data from different clinical business systems can be collected in real time, event weights are reasonably distributed through a dynamic weight calculation model, a periodic clinical state is deduced by using a convolutional neural network, and clinical labels of patients are automatically generated.

The invention relates to application of a curcumin analogue C1 in preparation of a medicine for treating endotoxemia, and relates to the technical field of biological medicines. According to the application, after an endotoxin-induced mouse model is intervened by adopting the curcumin analogue C1, TNF-alpha, IL-6, MCP-1, BUN and BNP in serum are all found to be remarkably reduced, and the pathological injury condition of lung tissues and the survival rate / clinical state of mice are all remarkably improved, the pharmacological value of the curcumin analogue C1 in endotoxemia is disclosed, and the curcumin analogue C1 can be used for treating endotoxemia. And a new thought is provided for treatment of inflammatory diseases.

This invention discloses a digital intelligent medical rehabilitation diagnosis method and system based on closed-loop management, comprising the following steps: analyzing data using hierarchical reasoning and interpretable generation mechanisms to provide relevant suggestions, mapping these suggestions to training action templates; dynamically calculating training intensity based on the patient's current functional state, and dynamically assembling prescription parameters based on the training intensity using an incremental prescription update mechanism to update the prescription; updating the prescription and follow-up plan based on a dynamic strategy of clinical status. This invention supports dynamically assembling training intensity and frequency according to the patient's real-time status, forming truly personalized electronic prescriptions; finally, through prescription versioning management, intelligent follow-up node planning, and an automatic re-evaluation triggering mechanism, a data-driven closed loop covering the entire rehabilitation cycle is constructed, ensuring that the intervention plan continuously evolves with the patient's recovery process.

A method is described herein comprising generating a predictive shape model using three dimensional representations and feature parameters of a plurality of subjects, wherein the feature parameters include age, weight, height, and percentage body fat, applying the predictive shape model to target feature parameters of a target plurality of subjects to predict three dimensional representations of the target plurality of subjects, wherein the target feature parameters include age, weight, height, and percentage body fat, using the predicted shapes of the target plurality of subjects to measure anthropometric dimensions, and using the anthropometric dimensions to determine at least one clinical state of the target plurality of subject.

The invention discloses a peritoneal dialysis personalized prescription recommendation method and system based on a knowledge graph, and the method comprises the steps: constructing a peritoneal dialysis knowledge graph, and obtaining the multi-source clinical data of a target patient; performing normalization processing on the structured data, and associating the normalized structured data with a corresponding medical entity in the peritoneal dialysis knowledge graph; performing entity and attribute extraction on the unstructured text data to obtain text entities and text attributes, and matching the text entities and the text attributes with corresponding medical entities in a peritoneal dialysis knowledge graph; executing graph traversal or rule reasoning based on associated and matched medical entities and corresponding entity relationships in the peritoneal dialysis knowledge graph, and calculating a prescription element candidate set compatible with the clinical state of the target patient; and generating personalized prescription recommendation of the target patient according to the prescription element candidate set. According to the method, personalized and standardized balanced recommendation can be realized, and the accuracy and credibility of a peritoneal dialysis prescription are improved.

The application discloses a clinical auxiliary decision-making method and system based on integrated time-series multi-modal data, relates to the field of medical information technology, and comprises the following steps: collecting and time-aligning time-series multi-modal clinical data of a patient, performing feature analysis and cross-modal fusion, and generating a unified multi-dimensional time-series health state feature spectrum.Based on the feature spectrum and a medical knowledge graph, a clinical state evolution model is established, the evolution distance between the current state of the patient and each key clinical event node is calculated, and then a dynamic risk assessment surface is constructed to identify a high-risk evolution path.Aiming at the high-risk path, the key feature combination is located back, corresponding intervention measure evidence chains are searched from the knowledge graph, and finally a clinical decision support report is generated.The application realizes dynamic quantitative risk assessment of disease evolution and automatic evidence-based decision recommendation.

This application discloses a digital management method and system for hospital nursing quality indicators, relating to the field of medical and nursing quality information technology. The method includes the following steps: setting assessment rules for evaluating nursing process quality indicators; acquiring patient nursing records and constructing a patient-centered event time series; based on the assessment rules, filtering nursing events related to the corresponding nursing process from the event time series; dynamically adjusting the strictness of the assessment rules based on the identity information of nursing staff and the clinical status information of patients; verifying the event time series according to the adjusted assessment rules and obtaining verification results; determining the compliance of the nursing process based on the verification results, and generating a compliance conclusion and audit report for the nursing process. This application can intelligently distinguish between genuine nursing quality defects and discrepancies in record appearances caused by differences in personnel and context, solving the problems of fragmented processes, isolated data, and delayed assessment in traditional nursing quality management.

The invention discloses a dual-channel-based ECMO offline success rate prediction method and system and a storage medium. The method comprises the following steps: inputting structured time sequence data and multi-modal heterogeneous data into a dual-channel data processing architecture; the first channel processes the structured time series data to obtain a first feature vector, and the second channel processes the multi-modal heterogeneous data to obtain a second feature vector; obtaining a clinical state feature vector and a clinical sensitive index vector according to the structured time sequence data and the multi-modal heterogeneous data; inputting the first feature vector, the second feature vector, the clinical state feature vector and the clinical sensitive index vector into a gating fusion network to obtain a fusion feature; inputting the query statement and the fusion feature into a knowledge injection layer to obtain a plurality of related knowledge fragments; inputting the query statement and the fusion feature into a data feedback layer to obtain a local rule; the fusion features, the knowledge fragments and the local rules are constructed into cue words; and inputting the cue word into the large language model to obtain the ECMO offline success probability and reason.

The invention relates to the technical field of medical care informatics, and discloses a personalized medical service recommendation method based on a user portrait, and the method comprises the steps: dynamically constructing a chronic portrait layer and an acute context layer through calculating the occurrence frequency, duration, baseline sign statistical deviation degree and other time sequence characteristics of health data items, and getting rid of the dependence on static labels; checking the activation state of the acute context layer; performing clinical priority decision to select a dominant context when a plurality of activation acute entries exist; based on dominant or unique acute context priority recommendation, the recommendation is used as a temporary taboo filter to examine chronic and secondary acute recommendation of silence conflicts, and context awareness and dynamic suitability of recommendation results are realized by dynamically deducing clinical states and setting in priority decision. And clinical risks caused by state mismatch are avoided.

This invention discloses a multidisciplinary pre-consultation method and system based on multi-agent collaboration. The method includes: 1) Clinical state initialization: constructing a structured clinical state and dividing it into a case feature information set and a diagnosis and planning set; 2) State-driven agent scheduling: scheduling one or more specialist agents related to the current symptoms based on the current clinical state and historical dialogue information; 3) Parallel analysis and suggestion generation: each scheduled specialist agent independently analyzes the current state and outputs problem suggestions and state update suggestions; 4) Clinical state update: semantically aggregating the outputs of multiple agents, updating the case feature information set, and generating the next round of patient-facing inquiries; 5) Diagnosis generation: when the case feature information set meets the overall integrity condition, generating a diagnostic conclusion and treatment plan based on the set, forming the initial medical record. This invention improves the structuring level, diagnostic accuracy, and clinical interpretability of intelligent consultation without requiring real multidisciplinary consultation resources, and has broad application prospects.

The invention discloses a clinical auxiliary decision-making method and system based on integrated time sequence multi-modal data, and relates to the technical field of medical information, and the method comprises the steps: collecting time sequence multi-modal clinical data of a patient, carrying out the time alignment, carrying out the feature analysis and cross-modal fusion, and generating a unified multi-dimensional time sequence health state feature spectrum. On the basis of the characteristic spectrum and in combination with a medical knowledge graph, a clinical state evolution model is established, the evolution distance between the current state of the patient and each key clinical event node is calculated, and then a dynamic risk assessment curved surface is constructed to recognize a high-risk evolution path. And for the high-risk path, backtracking and positioning the key feature combination, retrieving a corresponding intervention measure evidence chain from the knowledge graph, and finally generating a clinical decision support report. According to the invention, dynamic quantitative risk assessment of disease course evolution and automatic evidence-based decision recommendation are realized.

The invention relates to the technical field of medical intelligence, in particular to an intelligent infusion control method and system for a nursing nutrient solution. Patient information is collected through multi-modal data, a basic formula is screened from a formula library through an intelligent matching algorithm, adaptive adjustment is conducted in combination with the clinical state of a patient to generate a personalized formula, and pipeline physical parameters and patient physiological parameters are monitored in real time in the infusion process by calculating an initial infusion parameter set. According to the method, infusion parameters are dynamically adjusted based on a comparison result of a monitoring data stream and an expected target, a comprehensive risk score is calculated by adopting a three-dimensional quantitative factor, graded early warning and self-adaptive emergency processing are realized, and after an infusion period is finished, the infusion effect is evaluated in a mode of combining short-term effect quantitative evaluation and long-term effect trend evaluation. And the evaluation result is used for optimizing an intelligent matching algorithm and dynamic adjustment logic.

The application relates to the technical field of medical big data intelligent analysis, and discloses a medical big data intelligent analysis method and system based on deep learning, which comprises the following steps: acquiring patient image data, case text data and wearable device monitoring data, carrying out pretreatment, and standardizing and encoding various modal data. The dynamic weight of each modal data is calculated, and a multi-level fusion weight is synthesized. A modal value-clinical emergency degree double-factor resource decision model is constructed by combining the fusion weight and the patient clinical state parameter, and a priority score is obtained. According to the priority score, the patient is divided into different emergency treatment grades, and different treatment strategies are proposed. The model can maintain high prediction ability in various clinical scenes, improves the accuracy of clinical decision, and makes the resource allocation of the hospital more scientific and reasonable. In the emergency environment, real-time priority assessment of the patient can significantly improve the efficiency of emergency treatment, reduce the waiting time, and improve the survival rate and treatment effect of the patient.

The application discloses a kind of based on knowledge graph's electronic medical record accurate search method and system, comprising the following steps: obtaining data and preprocessing, form clinical observation time series data sequence;Based on clinical observation time series data sequence constructs medical knowledge graph and generates medical knowledge control path;Continuous time clinical observation control path is constructed, and double control path input structure is formed;Improved neural controlled differential equation is input, and initial clinical state continuous trajectory is obtained;Medical reachable domain determination and reachable domain projection operation are executed, and target clinical state continuous trajectory is obtained;According to search request, reverse continuous time integral calculation is executed, and candidate historical clinical state continuous trajectory set is obtained;The stability evaluation value of trajectory is calculated;According to trajectory stability evaluation value, sorting is carried out and electronic medical record search result is output.The application combines knowledge graph and neural controlled differential equation, realizes the time continuity and stability of electronic medical record accurate search.

The present application belongs to the field of patent application, and relates to an infection risk prediction method caused by gastrointestinal bleeding, comprising the following steps: collecting multi-source clinical data of patients with acute gastrointestinal bleeding, and constructing an infection risk feature set; constructing an initial state diagram, and determining the node state of the graph node in the initial state diagram and the activated graph node and graph edge based on the infection risk feature set to obtain a clinical state diagram; extracting the path of the graph node and the graph edge in the clinical state diagram to obtain an infection transmission path set; quantifying the risk of each infection transmission path based on the node state to obtain the path risk value of the infection transmission path; and performing aggregate calculation on the multiple path risk values of the infection transmission path set to obtain the total infection risk; the risk prediction can not only give a probability result, but also explain the risk source, thereby significantly improving the practical value of the clinical decision support system.

The present application relates to the technical field of healthcare informatics, and discloses a personalized medical service recommendation method based on user portrait, comprising: dynamically constructing a chronic portrait layer and an acute context layer by calculating the occurrence frequency, survival duration and baseline sign statistical deviation of health data items, and getting rid of the dependence on static labels; checking the activation state of the acute context layer; when there are multiple activated acute items, making a clinical priority decision to select a dominant context; based on the dominant or unique acute context, preferentially recommending, and taking it as a temporary contraindication filter to review the silent conflict between chronic and secondary acute recommendations, the present application dynamically infers the clinical state and internally builds a priority decision, realizes the context awareness and dynamic suitability of the recommendation result, and avoids the clinical risks caused by state mismatch.

The invention relates to the field of medical image information systems, and discloses a medical image data storage space management method and system. The storage space management method comprises the following steps: actively pre-caching image data of a patient to a local storage space in a bandwidth idle period according to a patient list managed by a doctor; monitoring the utilization rate of the local storage space, the clinical state information of the patient corresponding to the image data sequence and the storage time of the image data sequence; and when the corresponding trigger condition is monitored, calculating a retention value score of the corresponding image data sequence according to a storage space cleaning mode triggered by the trigger condition, and cleaning the image data sequence of which the score is lower than a preset score threshold so as to realize local storage space management. According to the storage space management method, the I / O bottleneck problem in massive image data retrieval is effectively solved, the cache hit rate is maximized in a limited physical storage space through asynchronous event driving and multi-dimensional retention value calculation, cache jitter is avoided, and efficient scheduling of storage resources is achieved.

This invention discloses a data analysis-based intensive care unit (ICU) data management system and method, relating to the field of ICU management technology. The management method includes the following steps: analyzing and integrating changes in patients' clinical status; capturing alarm nodes during changes in patients' clinical status and generating alarm records; effectively judging any alarm record; conducting difference analysis between different alarm records; clustering patients' similar clinical statuses based on the differences in alarm records at each alarm node; extracting the patient's clinical status from any alarm record, effectively evaluating the alarm records, and individually optimizing the alarm thresholds for effective alarms; performing real-time alarm evaluation on patients' real-time clinical data and sending alarm reminders when personalized alarm thresholds are met; effectively reducing false alarms caused by fixed threshold settings, ensuring resources are focused on critical events, and improving clinical response efficiency.

The invention provides a multifunctional intelligent blood glucose monitoring method and system which are applied to an integrated household blood glucose monitoring intelligent workstation. The method comprises the steps that in response to a monitoring starting instruction of a user, a central control unit calls a perioperative period nursing voice library to play standardized operation guidance; the automatic blood sampling and sterilizing device is controlled to execute a sterile sampling process, and automatic puncture and blood sample conveying are completed in a micro-negative pressure environment; acquiring a real-time blood glucose value, reading a clinical state tag of the user, and calling the irritable hyperglycemia risk assessment model to generate a grading assessment result; dynamically adjusting the disinfection strategy of the ultraviolet bacteriostasis bin based on the biological load risk of the detected numerical value; monitoring data and evaluation results are stored in a database, and a perioperative period blood glucose fluctuation file is constructed. According to the system, through software and hardware cooperation, sterile precise monitoring and complication risk early warning of the blood glucose of the patient in the perioperative period are achieved.

The invention discloses a drug recommendation method based on multi-dimensional clinical information fusion of a large language model, and the method comprises the steps: obtaining an electronic medical record data set of a patient, which comprises a multi-dimensional clinical information set and a clinical actual drug use information set; carrying out textualization preprocessing on the multi-dimensional clinical information set to obtain a clinical state description text; respectively injecting the clinical state description text and the clinical actual medication information set into a preset cue word template to obtain an instruction fine tuning data set; performing supervised fine tuning on the pre-trained large language model by using the instruction fine tuning data set to obtain an updated optimization parameter of the pre-trained large language model and a corresponding adaptive model; and inputting the clinical state description text of the to-be-recommended patient into the adaptive model, and outputting a drug combination recommendation result. According to the method, the screened pre-training large language model is combined, the multi-dimensional clinical information set of the electronic medical record data set and the semantic information in the medical text are effectively utilized, and the recommendation of the medicine suitable for the patient is assisted, so that the purpose of personalized recommendation is achieved.

The invention discloses a method for detecting the metabolic function of peripheral blood mononuclear cells (PBMC) through a Seaharse biological energy analysis technology and application of the method. According to the method, firstly, a linear interval for detecting various indexes of the metabolic function of the peripheral blood mononuclear cells through a Seaharse biological energy analyzer is determined, and the determination of the linear interval provides a basis for establishing a standardized detection process, so that contrastive analysis among different samples and unification of cross-center data are facilitated; a solid technical foundation is laid for metabolic disease diagnosis, curative effect evaluation and prognosis prediction; according to the method, different disease types are further selected for analysis, the corresponding relation between the peripheral blood mononuclear cell bioenergy parameters and the clinical state is determined, and therefore the accuracy of related disease diagnosis, curative effect monitoring and prognosis prediction is remarkably improved.