110 results about "Clinical decision making" patented technology

The invention provides a collaborative reasoning method and system fusing a medical knowledge graph and a large model in the technical field of artificial intelligence. The method comprises the steps that S1, medical entities, medical relationships and medical attributes are extracted from a medical data set through a medical information extraction model to construct the medical knowledge graph; s2, monitoring the latest medical information through a medical information monitoring agent so as to update the medical knowledge graph; s3, creating a clinical decision collaborative reasoning model; s4, training and deploying the clinical decision collaborative reasoning model through the medical data set; s5, pushing the clinical decision collaborative reasoning model to a medical terminal through a federal gateway; and S6, the medical terminal inputs the query appeal carried by the query request into the clinical decision collaborative reasoning model to obtain a reasoning report. The method has the advantages that the reasoning ability, timeliness, interpretability and safety of medical reasoning are greatly improved.

The invention discloses a medical whole-course intelligent management system based on a large model, and belongs to the technical field of large models. Comprising a multi-modal data acquisition module, a privacy calculation preprocessing module, a dynamic knowledge enhancement module, a time sequence data analysis module, an intelligent decision engine module, a multidisciplinary collaboration module, a patient interaction platform module, a dynamic intervention feedback module and a system security center module. The cross-mechanism data security sharing is realized, and the compliance of sensitive information processing is also ensured; a two-channel medical knowledge base is constructed, authoritative guidelines can be synchronized, newest clinical research data can be analyzed in real time, the knowledge base is kept in the newest state all the time, and the frontier scientific basis is provided for clinical decisions; dynamic modeling and trend prediction are carried out on long-term monitoring data of a patient by adopting a hybrid neural network model, and potential health risks and development trends can be identified more accurately.

The invention provides a clinical decision-making method and system based on a large language model and a knowledge graph, and belongs to the technical field of artificial intelligence and intelligent diagnosis and treatment crossing. The method comprises the following steps: S1, training and deploying a created generative large language model and a knowledge extraction model; s2, extracting medical knowledge from the medical data set through a knowledge extraction model; s3, constructing a medical knowledge graph based on the medical knowledge; s4, acquiring an input medical question, inputting the medical question into the generative large language model, querying medical knowledge corresponding to the medical question by the generative large language model through the medical knowledge graph, generating a medical answer based on the medical knowledge, recording a decision basis chain in the query process, and feeding back the medical answer and the decision basis chain; and S5, recording a question and answer log including the medical questions, the medical answers and the decision basis chain. The method has the advantages that the accuracy, the reliability, the timeliness and the safety of clinical decision making are greatly improved.

The invention discloses an insulin infusion decision-making system and method combining reinforcement learning and metabolism simulation, and belongs to the field of clinical decision-making assistance. Comprising an acquisition processing module, a metabolism simulation module, a dynamic updating module, a path simulation module, a strategy control module, a monitoring constraint module, a decision analysis module, a correction processing module, a training optimization module and an injection control module. The method can accurately reflect the metabolic characteristics of the patient, makes the insulin decision more targeted, realizes automatic adjustment of the model, keeps real-time perception and response to the state of the patient, avoids the risk caused by model aging or error accumulation, enhances the long-term control ability, improves the control efficiency while guaranteeing the blood glucose stability, and reduces the cost. The fault-tolerant capability of the system is improved, the interpretability and safety guarantee of clinical decisions are improved, and the credibility of doctors is enhanced.

The invention discloses a liver cancer clinical decision support method and system based on a large language model and a medium, and relates to the technical field of artificial intelligence. Synthesizing the domain enhancement model into a high-quality liver cancer clinical reasoning instruction set containing an intermediate reasoning basis, and performing supervised instruction fine tuning on the domain enhancement model to obtain an instruction fine tuning model; constructing positive and negative sample pairs, and training the instruction fine tuning model by a grouping relative strategy optimization algorithm and Monte Carlo tree search, so that model output is aligned with human expert preferences, and a final liver cancer auxiliary diagnosis large language model is obtained for liver cancer clinical decision making. According to the method, medical guidelines, clinical data and expert experience in the liver cancer field are efficiently injected into a large language model through a three-stage training strategy of incremental prediction training, supervision fine tuning and preference alignment, so that the liver cancer field masters accurate diagnostic logic and term expression.

A personalised artificial-intelligence (AI) case-manager platform provides real-time, policy-constrained clinical decision support. It ingests heterogeneous data from electronic-health records (EHRs), connected medical devices, and clinician inputs; fuses them with a traceable, multilingual reasoning engine; and screens every candidate action through a multi-tier policy-constraint layer that respects patient-consent artefacts, safety grammars, and jurisdictional rules. Dual-factor credential verification and dynamic, role-based access control secure all protected-health-information (PHI) transactions, while a cryptographically chained audit trail—keyed by a global trace identifier—records inputs, rules, overrides, and triggered workflows. Authorised feedback is adjudicated and fed to adaptive learning modules that tune patient-specific and population-level behaviour. A conflict-detection service flags discordant data streams, and a governance-validated workflow engine can issue proactive alerts, automated record updates, or human-in-the-loop escalations. The platform thus delivers explainable, equitable, and continuously learning automation without compromising privacy or clinical accountability.

The invention discloses a depression auxiliary evaluation method based on a large language model agent. The depression auxiliary evaluation method is characterized by comprising the following steps: a) performing structured processing on a Hamilton depression scale; b) constructing a dynamic questioning agent based on a large language model, and realizing dynamic selection and adjustment of theme questions; c) constructing a multi-dimensional scoring agent to obtain sub-item scores of the depressive symptoms; and d) constructing a clinical decision-making agent, generating risk early warning, giving personalized diagnosis and treatment and intervention suggestions and the like. Compared with the prior art, the method has the advantages that real-time interaction in a natural language form is realized, a real interview process is simulated, the method is closer to psychological consultation practice, user experience and evaluation reliability and validity are enhanced, user language behaviors and scoring results can be analyzed in real time, high-risk signals such as self-injury tendency and severe depression can be identified, and the method is suitable for popularization and application. Personalized diagnosis and treatment suggestions and intervention measures are output, intelligent assistance is provided for clinical decision making, and the intelligent level of mental health screening and management is improved.

The present invention provides a multilingual, AI-powered healthcare system integrating a personalized medical assistant, real-time clinical decision support, and closed-loop device control. The system ingests multimodal patient-generated and institutional data, applies advanced language-model-driven reasoning for clinical insights and triage, and supports regulated, auditable actuation of medical devices. Key features include multilingual overlays, role- and jurisdiction-specific visualization, co-signature enforcement, audit traceability, and seamless integration with healthcare infrastructure. A “Multilingual Overlay” is a dynamically generated display layer that the system composites in real-time from clinical text, icons, and color-coded indicators, automatically adapting its language, reading direction, terminology, visual density, and role-based data visibility to the preferences, locale, and device form-factor of each authenticated viewer. The architecture supports provider- and patient-facing use cases, agentic AI for autonomous yet regulated decision-making, and robust safety and compliance features.

The invention discloses a clinical decision interaction method and system based on structured evidence reasoning, and relates to the technical field of computer technology and medical informatization. The method obtains a user input query; the method comprises the following steps: performing mixed entity identification from a query input by a user based on an entity identification model and a rule matching algorithm to obtain a candidate entity set, and performing conflict resolution and entity standardization processing on the candidate entity set to obtain a core entity; performing multi-granularity intention recognition based on confidence calculation between the classification systems of the core entity and the medical exclusive intention to obtain a target intention; performing screening query in a pre-constructed structured knowledge graph according to the core entity and the target intention to obtain a corresponding structured knowledge fragment; generating a cue word based on the structured knowledge fragment, user input query, instruction constraint and multiple verification; and inputting the cue word into the large language model, and outputting an answer. According to the invention, more accurate and more reliable dialogue service can be provided for the medical field.

The invention discloses a diagnosis model for hepatocellular carcinoma microvascular invasion and a construction method and application thereof, and belongs to the technical field of medical examination. Metabolite in the blood of a hepatocellular carcinoma patient is taken as a cut-in, preoperative clinical tumor indexes are combined, it is found that PIVKA-II, GGT, L-Serine, Sarcosine and tumor sizes have important significance on formation of hepatocellular carcinoma MVI, a diagnosis model based on multiple omics and multiple dimensions is established based on the diagnosis indexes, the diagnosis model is used for diagnosis of the preoperative MVI of the hepatocellular carcinoma patient, and the diagnosis model is used for diagnosis of the preoperative MVI of the hepatocellular carcinoma patient. The defects of existing diagnosis means in the aspect of preoperative diagnosis are overcome, and a more timely and accurate basis is provided for clinical decision making.

The invention discloses a reasoning and interaction system based on multiple dimensions. According to the inference system based on multiple dimensions, the complete process from multi-source data acquisition to inference result output is realized. The acquisition module is responsible for collecting out-hospital examination information and in-hospital examination information of a patient. And the pruning module predicts a targeted set by utilizing the interactively collected patient information and a preset diagnosis knowledge base, dynamically prunes the full-amount question-examination thinking sub-trees according to the targeted set, and optimizes the pruned question-examination thinking sub-trees. And the feature extraction module extracts key features from the interactively collected out-of-hospital and in-hospital question examination information based on the optimized question examination thinking sub-tree. And finally, the inference module inputs the key features into a pre-trained diagnosis model, and the model is combined with a Bayesian algorithm, a deep learning algorithm and a clinical decision consensus to output a final inference result. The problem that dynamic adaptation and efficient and accurate reasoning of multi-dimensional data cannot be achieved on a task set with specific requirements in the prior art is effectively solved.

The invention discloses an interpretable clinical decision support system based on label generation and a knowledge graph. The method comprises the following steps: based on a breast cancer domain knowledge enhanced version Qwen-BrCaAdapt of a general large language model Qwen, analyzing an unstructured medical record text of a patient, and generating a structured result containing tags, values, evidences and explanations; calculating a reasoning label through a path matching engine by utilizing an editable structured path rule table, and matching a candidate treatment scheme according to the reasoning label; and taking the matched treatment scheme as a central node, calling a medical knowledge graph to bind entity information including clinical evidence, recommendation levels, medical insurance information, medication risks, usage and dosage, and generating a traceable JSON structure and a visual report. The method has the beneficial effects that the accuracy and efficiency of tag generation in the breast cancer field are improved, the rule maintenance cost is reduced, the interpretability and traceability of a clinical decision scheme are enhanced, and the acceptability of a doctor to a recommendation result is improved.

The invention belongs to the field of computational medicine, particularly relates to a method and a system for constructing full-parameter digital twinning of a patient and performing treatment simulation, and aims to solve the problems that an existing digital twinning model cannot fuse multi-scale data, lacks dynamic optimization capability and is disjointed with a clinical decision process. The method comprises the following steps: acquiring and integrating multi-source heterogeneous data of a patient; performing cross-scale fusion modeling based on the data, and constructing a personalized digital twinborn initial parameter set containing genetic background correction; driving a multi-physics field coupling engine to simulate an intervention effect, constructing a reverse optimization problem taking real-time monitoring data as a dynamic constraint, and solving an optimal intervention scheme parameter; and carrying out verification simulation on the optimization scheme and generating a treatment report containing quantitative evaluation and risk early warning. According to the invention, by constructing a full-parameter and mechanism model and introducing a dynamic closed-loop optimization verification process, the personalized precision, safety and clinical decision support value of treatment scheme simulation are significantly improved.

The invention relates to the field of medical artificial intelligence, in particular to a clinical scoring system combining large language model multi-agents and machine learning and a self-evolution method of the clinical scoring system, and provides the clinical scoring system based on the large language model multi-agents and the self-evolution method of the clinical scoring system. The objective of the invention is to overcome the defects of data missing sensitivity, insufficient interpretability, weak dynamic updating capability and the like of an existing clinical scoring system. According to the system, through a multi-agent collaboration architecture, the natural language interaction capability of a large language model (LLM) and the precise prediction capability of a machine learning model are deeply fused, and a self-evolution mechanism is introduced to realize dynamic optimization, so that the reliability, flexibility and transparency of clinical decisions are remarkably improved.

The invention provides a clinical decision knowledge graph construction method and system, and the method comprises the steps: extracting standardized entities corresponding to diseases, symptoms and diagnosis and treatment elements from medical knowledge data; constructing a clinical concept knowledge graph for representing a medical concept logic relationship and a causal relationship according to the semantic association relationship and the causal dependency relationship among the standardized entities; a diagnosis event, an examination event and a treatment event related to the patient are extracted, link evidences among the events are determined based on the event chain relation among the events, and a clinical event knowledge graph used for representing the disease course evolution process of the patient is constructed according to all the link evidences; and performing knowledge element fusion based on an entity association relationship between the clinical concept knowledge graph and the clinical event knowledge graph, and generating a target knowledge graph for clinical decision analysis. By adopting the scheme of the invention, the cross-map fusion of the static medical concept knowledge and the dynamic disease course event chain relationship can be realized, and the clinical decision knowledge structure with the reasoning ability can be constructed.

A personalised artificial-intelligence (AI) case-manager platform provides real-time, policy-constrained clinical decision support. It ingests heterogeneous data from electronic-health records (EHRs), connected medical devices, and clinician inputs; fuses them with a traceable, multilingual reasoning engine; and screens every candidate action through a multi-tier policy-constraint layer that respects patient-consent artefacts, safety grammars, and jurisdictional rules. Dual-factor credential verification and dynamic, role-based access control secure all protected-health-information (PHI) transactions, while a cryptographically chained audit trail—keyed by a global trace identifier—records inputs, rules, overrides, and triggered workflows. Authorised feedback is adjudicated and fed to adaptive learning modules that tune patient-specific and population-level behaviour. A conflict-detection service flags discordant data streams, and a governance-validated workflow engine can issue proactive alerts, automated record updates, or human-in-the-loop escalations. The platform thus delivers explainable, equitable, and continuously learning automation without compromising privacy or clinical accountability.

The invention discloses a pancreatitis severity assessment method based on layered reliable evidence fusion, which comprises the following steps of: constructing a layered reliable evidence fusion model, and sequentially executing three stages of multi-view evidence generation, opinion mapping and opinion fusion: generating an image based on image and clinical data, and clinical and pseudo-view evidence, mapping the opinions into subjective logic opinions containing belief quality and uncertainty, and finally fusing all the opinions by using a logarithmic opinion pool fusion algorithm to generate final fused opinions; then training the layered reliable evidence fusion model based on the training set; then testing, and evaluating the acute pancreatitis severity degree of the patient corresponding to the test sample according to the belief quality of each category in the final fusion suggestion corresponding to the test sample; the method has the advantages that multi-source information can be effectively coordinated, evidence conflicts are converted into uncertainty, an accurate and reliable severity degree evaluation result is output according to belief quality in fused opinions, and powerful support is provided for clinical decision making.

Disclosed are systems and methods for building and using a data platform to perform intelligent treatment selection to assist in clinical decision making. The present disclosure relates to a server configured to transform a subject record, which characterizes various non-numerical features of a subject, into a transformed representation that is consumable by machine-learning or artificial intelligence models. The output of the machine-learning or artificial-intelligence models can be evaluated to enable the intelligent treatment selection and other enhanced analyses, such as, for example, automatically detecting and defining subject populations, predicting treatment protocols with likely positive responsiveness, and identifying the similarity or dissimilarity between subject records along one or more dimensions.

The invention provides an electronic health record enhanced reasoning method and system based on a thinking map and reinforcement learning, and the method comprises the steps: carrying out the definition and classification of an electronic health record EHR analysis task, and dividing the EHR analysis task into a clinical decision task and a risk prediction task; constructing an EHR reasoning data automatic generation process, including entity extraction and co-occurrence analysis, construction of a thinking graph based on a knowledge graph, reasoning path synthesis and data generation; and performing reasoning enhanced EHR analysis large language model training, including continuous pre-training, EHR reasoning data instruction fine tuning and EHR analysis task reinforcement learning. According to the method, the external medical knowledge graph is utilized to construct the thinking graph, two-stage post-training and reinforcement learning strategies are combined, and the reasoning ability, diagnosis accuracy and generalization of LLM in various EHR analysis tasks are improved.

The invention discloses a medical aid decision-making method and system, a clinical decision-making support instrument and a storage medium. The method comprises the following steps: acquiring medical problem data; performing retrieval enhancement labeling on the medical problem data to obtain problem knowledge fragments; performing semantic fusion analysis on the medical problem data and the problem knowledge fragments according to a fully trained large language model to obtain auxiliary decision data of the medical problem data; wherein the auxiliary decision-making data comprises medical result data and question knowledge fragments; according to the method, professional knowledge fragments related to medical problems are obtained by retrieving enhanced labels, reliable knowledge support is provided for a large language model, illusion or errors possibly generated due to the fact that the large model only depends on training data of the large model are avoided, and medical result data obtained after semantic fusion analysis better conform to medical professional logic; as the auxiliary decision-making data further comprises question knowledge fragments, the output auxiliary decision-making data is associated with the corresponding professional basis, so that the source of the auxiliary decision-making data can be understood conveniently.

The present invention relates to a data fusion processing method and system for an MEMS-based intelligent sensor, including the following steps: decomposing the multi-modal physiological parameter time series data stream to obtain a key vital sign feature sequence; calculating the phase coupling degree of the key vital sign feature sequence to obtain an organ-interfunction association strength matrix; performing high-dimensional feature space projection on the critically ill patient based on the organ-interfunction association strength matrix to obtain a patient pathological state feature map; performing non-linear dynamics analysis on the patient pathological state feature map to obtain a disease progression dynamic trajectory; and performing clinical decision support reasoning based on the disease progression dynamic trajectory to obtain personalized treatment recommendations, solving the technical problem that existing analysis methods are often limited to the observation of a single physiological parameter or simple statistical analysis and lack the ability to understand the disease mechanism from an overall perspective.

The present invention provides a clinical decision-making method and system based on a large language model and knowledge graph in the field of the intersection of artificial intelligence and intelligent diagnosis and treatment. The method includes: step S1, training and deploying a created generative large language model and a knowledge extraction model; step S2, extracting medical knowledge from a medical dataset using the knowledge extraction model; step S3, constructing a medical knowledge graph based on each piece of medical knowledge; step S4, obtaining an input medical question, inputting the medical question into the generative large language model, and querying the medical knowledge corresponding to the medical question through the medical knowledge graph, generating a medical answer based on the medical knowledge, recording the decision-making chain during the query process, and providing feedback on the medical answer and the decision-making chain; step S5, recording a question-and-answer log including the medical question, medical answer, and decision-making chain. The advantages of the present invention are: greatly improving the accuracy, reliability, timeliness, and security of clinical decision-making.

The invention discloses an application of a PLSCR1 protein in preparation of a biomarker for evaluating early warning of sepsis. The invention determines and verifies that the PLSCR1 is obviously increased in sepsis patients for the first time. According to the invention, the PLSCR1 is used as a novel biomarker for early warning and prognosis evaluation of sepsis, so that the clinical early recognition and prognosis capability is effectively improved. The PLSCR1 shows an independent prediction value, detection of the PLSCR1 level of a suspected or diagnosed sepsis patient can provide a more accurate and more comprehensive basis for clinical decision making, and improvement of the survival outcome of the patient and optimization of the treatment effect are facilitated.

The invention discloses a clinical decision support method based on QSP and deep reinforcement learning. The method comprises the following steps: preprocessing and standardizing physiological data of a patient and existing drug parameters; constructing a QSP model of the medicine and the patient; based on the LSTM model and the ARIMA model, constructing a patient physiological state prediction model; a state space and an action space are defined, and a transition probability and a reward function are obtained; calculating a Q value and using the Q value to initialize the Q value of the DQN; accumulating experience in the experience pool by using an epsilon-greedy strategy; training the Double DQN model until the Q function is converged; and updating parameters of the Q network based on a genetic-annealing algorithm. By collecting and analyzing the detailed physiological data of the patient, a customized treatment scheme is provided for each patient, the treatment effect is improved, and unnecessary side effects are reduced.

A computer-implemented method for clinical decision support is described. The method comprises obtaining, at a computing device, subject data associated with a subject, the subject data comprising: at least one vital parameter determined for the subject, and at least one laboratory parameter determined for the subject; and computing, based on the obtained subject data, at least one first indicator indicative of the appropriateness for multiple overnight hospitalization of the subject.

The embodiment of the invention provides a multi-role adaptive interaction method and system based on a stomatology knowledge base, and the method comprises the steps: recognizing a query role when a query signal is received, and obtaining the query content; calling a preset query engine matched with the query role, accessing a pre-constructed knowledge base according to the currently called query engine and the query content, and obtaining a differential diagnosis set containing a plurality of diseases with a sorting relationship; and performing multi-dimensional differential diagnosis reasoning on the diseases in the differential diagnosis set one by one according to the sorting relationship, correspondingly evaluating the comprehensive confidence coefficient of the reasoning result, and when the comprehensive confidence coefficient is not less than a preset threshold value, taking the corresponding disease as a diagnosis disease, and outputting a query report matched with the query role. According to the invention, doctors can be assisted to make professional clinical decisions, and convenient oral common sense popularization services can be provided for patients.

The invention relates to the technical field of artificial intelligence assisted medical treatment, and discloses a multi-modal help risk assessment method based on order-preserving calibration, and the method comprises the steps: mapping the multi-modal data of a to-be-tested sample to a joint risk measurement manifold space, and carrying out the fusion; then, a clinical semantic anchor point sequence subjected to order-preserving constraint is used as a reference system, and a modal conflict vector is calculated to quantify semantic inconsistency between modals; perturbation is applied to the anchor points based on the conflict intensity to generate an anti-fact anchor point cloud, and the pairwise dominant probability of the to-be-tested sample relative to the virtual anchor points is calculated through a constructed differential partial sequence discriminator; and finally, performing statistical aggregation to obtain a partial order dominance degree sequence, and defining a dynamic risk confidence interval. According to the method, through explicit modeling of modal conflicts and construction of dynamic calibration boundaries, the problem of assessment uncertainty caused by heterogeneous data is solved, risk underestimation is effectively avoided, and an auxiliary diagnosis basis with high robustness and interpretability is provided for clinical decision making.

This application discloses a method and apparatus for constructing a Traditional Chinese Medicine (TCM) diagnostic and treatment knowledge model. The method includes: transforming a general-purpose basic language model into a model specifically for the TCM field to obtain a basic TCM model; controlling the obtained basic TCM model to learn diagnostic and treatment thinking within the TCM field to obtain a basic TCM diagnostic and treatment model; aiming to enhance the flexibility of logical reasoning in the obtained basic TCM diagnostic and treatment model, controlling the obtained basic TCM diagnostic and treatment model to learn the diagnostic reasoning paths of clinical cases in the TCM field to obtain a TCM reasoning diagnostic and treatment model; controlling the obtained TCM reasoning diagnostic and treatment model to learn the unique TCM knowledge and unique diagnostic and treatment thinking of a target renowned physician to obtain a unique TCM diagnostic and treatment model; and controlling the obtained unique TCM diagnostic and treatment model to favor the unique diagnostic and treatment thinking of the target renowned physician to obtain a TCM diagnostic and treatment knowledge model. This improves the diagnostic accuracy and clinical decision-making reliability of the model.

The present invention provides methods relating to the discovery of olfactomedin 4 (OLFM4) as a biomarker for acute kidney injury (AKI) and need for renal replacement therapy, and further as a biomarker for responsiveness to the furosemide stress test (FST). The methods described here are useful in clinical decision support and personalized therapy for AKI, as well as for clinical trial design.

The invention relates to the field of medical care informatics, and discloses a lupus nephritis condition monitoring system based on TWEAK and CD163 dynamic change, which comprises a data adaptation gateway module, a data rationality arbitration engine, a time sequence regularization engine module, a high-order feature extraction engine module and a risk layering module, the time sequence regularization engine module switches an interpolation or smoothing algorithm based on a qualitative result of the data rationality arbitration engine and a time domain where a data point is located so as to generate a continuous time function. Through the information processing architecture, the technical problem that sparse asynchronous original medical data is prone to being affected by outlier pollution and end point distortion is solved, and the accuracy of data processing is improved. The system reconstructs discrete data snapshots into continuous mathematical objects, provides a stable and analyzable technical basis for subsequent extraction of interpretable high-order dynamic features, and improves the credibility of clinical decision support.