77 results about "Clinical decision making" patented technology

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 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 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 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.

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 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 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 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.

An AI algorithm may be used in a clinical setting to perform one or more tasks to assist medical personnel. The results produced by the AI algorithm may affect not only patient care, but also the cost of the care. The AI algorithm may be trained on auxiliary data to incorporate the impacts on patient care and cost.

The invention discloses a knowledge base generation method and device and a visual language model training method and device, and belongs to the technical field of medical knowledge processing. The method comprises the steps of obtaining a clinical text and extracting a medical entity; constructing a knowledge graph according to the medical entities and the relationship between the entities; obtaining an inference path between entities based on the knowledge graph, wherein the inference path comprises mapping the medical entities to knowledge graph nodes and obtaining an association path; generating thinking chain data according to the reasoning path; and generating a knowledge base according to the knowledge graph, the reasoning path and the thinking chain data. In the entity mapping process, candidate matching nodes are obtained by calculating the similarity, and final matching nodes are obtained by adopting a multi-stage matching strategy. The invention further provides a visual language model training method using the generated knowledge base, a visual language network is trained through a multi-stage fine tuning strategy, and a diagnosis report containing a clinical reasoning process is output. The knowledge base constructed by the method can effectively support medical diagnosis reasoning, and the accuracy and interpretability of clinical decisions are improved.

A computer-implemented method for clinical decision support includes obtaining, at a computing device, subject data associated with a subject, the subject data at least including: at least three vital parameters determined for the subject, and the age of the subject; and computing, based on the obtained subject data, at least one indicator indicative of the likelihood that the subject needs at least one critical emergency department intervention within a time period.

The invention discloses an organ transplantation clinical aid decision-making method and device based on an AI large model, equipment, a medium and a product, and relates to the technical field of medical information, and the method comprises the steps: obtaining an organ transplantation clinical consultation question of a user; according to the organ transplantation clinical consultation question, adopting a clinical decision model to obtain a clinical decision suggestion corresponding to the organ transplantation clinical consultation question; the clinical decision model is a large language model which is finely adjusted by adopting a training sample set in advance; the training sample set comprises a plurality of organ transplantation sample consultation questions and sample clinical decision suggestions obtained by adopting a plurality of retrieval enhancement generation methods and answer fusion methods according to each sample consultation question. According to the method, the problems of scarcity of professional data in the organ transplantation field and insufficient reasoning ability of the existing model are effectively solved, and the accuracy and reliability of the large language model in the organ transplantation field are remarkably improved.

The application provides a clinical conclusion generation method and device, comprising: obtaining a clinical query request; generating a candidate clinical assertion list based on the clinical query request; for each candidate clinical assertion in the candidate clinical assertion list, simultaneously initiating an independent retrieval request to a preset clinical guideline library, a preset evidence-based literature library and a preset patient medical record library to obtain an evidence triple retrieval result corresponding to each candidate clinical assertion; verifying the integrity of the evidence triple corresponding to each candidate clinical assertion, and if the evidence triple of the candidate clinical assertion is complete, generating a corresponding clinical conclusion based on the complete evidence triple; the application realizes the binding of the clinical conclusion and the multi-source evidence, effectively solves the problems of soft coupling, single evidence source and lack of evidence integrity verification in the prior art retrieval and generation, reduces the illusion risk of the medical large model, and can meet the actual application requirements of clinical decision support.

The invention provides a disease assessment system and method, an electronic device and a medium wherein a data acquisition module is used for acquiring form data and clinical text record data of a target patient; the large model reasoning module is connected with the data acquisition module, and the large model reasoning module is used for performing semantic understanding and reasoning on the clinical text record data based on the medical knowledge base and outputting at least one intermediate semantic understanding result; the rule engine module is connected with the data acquisition module and the large model reasoning module, and the rule engine module is used for receiving the form data and the at least one intermediate semantic understanding result and performing combinational logic operation on the at least one intermediate semantic understanding result according to a predefined evaluation rule; and the assessment result generation module is used for generating a final disease risk assessment conclusion of the target patient according to the logical operation result of the rule engine module. According to the invention, automatic and accurate disease risk assessment is realized, and the clinical decision-making efficiency and quality are improved.

The application discloses a kind of based on structured electronic questionnaire etiology screening and auxiliary decision-making method and system, it is related to digital health assessment and clinical decision support technical field, including obtaining the disease characteristics of patient, utilize disease characteristics to enter corresponding etiology diagnosis question group etiology identification, based on feature mapping rule etiology diagnosis question group carries out etiology score calculation, obtains candidate etiology score set, based on etiology threshold etiology score set is judged and the etiology that meets requirement is output, obtains etiology preliminary judgment result;According to etiology preliminary judgment result obtains final auxiliary decision-making;Final auxiliary decision-making is used to assist doctor to make clinical diagnosis and disposal.The application realizes disease history standardization, quantifiable, traceable by structured questionnaire and logic branch, reduces the omission and repeated inquiry situation of patient information acquisition process, improves patient treatment efficiency, reduces the work intensity of doctor.

A navigable directed graph interface representing a medical guide, where the medical guide includes a decision tree that includes a plurality of clinical decisions and preconditions. A navigation interface presents a portion of a directed graph that is based on the recorded preconditions and / or the sequence of clinical decisions of the medical guide and a portion of the directed graph that diverges from the recorded preconditions and / or the sequence of clinical decisions. A divergent portion of the directed graph is graphically distinguished from the portion of the directed graph based on the recorded preconditions and / or clinical decisions. In response to receiving a selection of an alternative node from the divergent portion of the directed graph, a graphical representation of the directed graph displays one or more child nodes of the selected alternative node.

The present invention relates to a medical device comprising a processing unit configured to receive and analyze videos showing images of target tissues of a patient, to identify and track said tissues, to determine intervention areas or volumes, and to recognize the position and movement of the video acquisition device in relation to the target tissues. Furthermore, the processing unit identifies actions associated with the movement of the video acquisition device based on predefined actions. The output device of the system of the invention emits signals if it is detected that the video acquisition device is moving out of or has recently moved out of the intervention area of a target tissue without having completed a planned action of the video acquisition device within said area.

A safety receipt layer is interposed between an electronic health record (EHR) system and clinical decision support intervention (DSI) engines, including AI-assisted DSIs. For each DSI episode, the layer constructs a canonical clinical context envelope, evaluates a policy graph, and computes a permit outcome (permit, guarded permit, override, deny) that is enforced in a permit-before-action, fail-closed configuration. A time-of-check-to-time-of-use latch binds policy evaluation and any anchoring precondition to rendering and EHR write-back so outputs cannot be finalized without an affirmative permit or recorded override. The layer emits a structured, machine-verifiable safety receipt encoding policy identifiers, the context envelope or a cryptographic digest of a canonicalized field list, the permit outcome, requested and effective behavior modes, and clinician response. Receipts are anchored in an append-only verifiable log with maximum-merge-delay signed heads and inclusion and consistency proofs, enabling verification, role-based views, monitoring, and mappings to health IT transparency or certification requirements.

The invention belongs to the technical field of intelligent medical treatment and clinical decision, and particularly relates to an idiopathic membranous nephropathy prognosis prediction method and system based on deep learning and a cross-modal attention mechanism. In order to realize accurate prognosis evaluation of IMN, depth features of a kidney tissue pathological image are automatically extracted through a deep learning technology and are innovatively and deeply fused with clinical data of a patient, and finally a high-precision and explainable intelligent prediction model is constructed to guide clinical early intervention and individualized treatment decision.

The invention discloses a double-granularity drug recommendation method (LLM-CIDGMed) based on large language model driven causal reasoning, which is used for solving the key limitation of the existing drug recommendation system in the aspects of medical knowledge driven causal reasoning, patient perception multi-granularity drug characterization fusion and long sequence time sequence modeling. The method aims at solving the problems that a traditional statistical algorithm lacks deep understanding of medical field knowledge, drug representation learning lacks a multi-granularity information integration mechanism perceived by a patient, and a traditional time sequence modeling method loses long-term historical information of the patient. Experimental results show that the method is remarkably superior to an existing method in the aspects of accuracy, safety and calculation efficiency, and more reliable intelligent support is provided for clinical decision making.

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.