53 results about "Clinical decision making" patented technology

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 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 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 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 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 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 present disclosure relates to techniques for generating a ranked list of a set of subjects by predicting their potential health benefit from an intervention to prioritize subjects that may be at a risk of a negative outcome and likely to benefit from a proposed intervention. Additionally, the ranking may further account for potential cost-savings associated with early intervention to avoid acute-care utilization by applying a cost-modeling technique. The disclosed techniques may include analyzing subject-specific data, including demographic, clinical, and historical information, to compute a total net-benefit score by combining a predicted benefit probability with cost and revenue metrics. The benefit probability may be calculated using causal inference models to estimate a potential improvement in health outcomes from the proposed intervention or treatment. The disclosed techniques may further facilitate personalized subject care by dynamically updating rankings based on real-time data, enhancing clinical decision-making, and optimizing resource allocation.

The application discloses a large language model liver cancer prediction method and system based on an improved sampling strategy, relates to the technical field of artificial intelligence medical diagnosis, and comprises the following steps: acquiring patient electronic medical records and performing time series reconstruction to construct structured prompt words; inputting the structured prompt words into a general large language model with parameter freezing, using a random decoding strategy to generate multiple reasoning paths in parallel; constructing a target distribution model based on a gamma distribution, and using an importance sampling algorithm to calculate the normalized weight of each path to suppress low-quality paths and amplify the weight of paths conforming to medical logic; finally, the diagnostic conclusion is weighted and aggregated based on the weight to obtain a prediction result, and the reasoning process with the highest weight is output as an explainability report. The application can significantly improve the accuracy of liver cancer prediction and the transparency of clinical decision-making without model fine-tuning.

A computer-implemented method is for clinical decision support. In an embodiment, the method includes receiving patient data of a target patient; determining, based on the patient data, a number of potential clinical outcomes associated with the target patient; calculating, for each respective potential clinical outcome, a respective probability of being indicated by the patient data; selecting, based on the plurality of probabilities calculated, one or more anamnestic questions from a set of anamnestic questions stored; presenting the one or more anamnestic questions selected to a user via a user interface; receiving one or more answers to the one or more anamnestic questions selected, from the user; and adapting the plurality of probabilities based upon the one or more answers received.

Embodiments disclosed herein establish relationships between changes in cardiovascular indicators and drug combinations. These relationships are established by tracking causal chains described by linear relationships, from drug combinations to cardiovascular parameters and from cardiovascular parameters to cardiovascular indicators, by measuring the direction and sensitivity of hemodynamic changes caused by changes in cardiovascular parameters (resulting from drug administration). Some embodiments also model myocardial oxygen consumption, which cannot be directly measured, and establish relationships between hemodynamic changes in myocardial oxygen consumption and changes in one or more cardiovascular parameters. Because these embodiments qualitatively and quantitatively establish causal chains from drugs to cardiovascular indicators, they support clinical decision-making, thereby reducing the level of mere guesswork and uncertainty in determining the optimal drug combination for patients with acute heart failure.

This invention discloses a method and apparatus for constructing a medical hypergraph that integrates individual case data and medical guidelines. First, case data and medical guideline data are processed separately to construct corresponding knowledge graphs. Then, semantic anchoring relationships are established between case entities and guideline terms, and entity standardization mapping is achieved based on a semantic matching model validated by causal mechanisms. On this basis, the patient's treatment process is path-modeled and divided into stages according to the sequence of treatment events. In each stage, relevant clinical entities and corresponding guideline terms are connected to form hyperedges, thereby constructing a medical hypergraph structure that integrates case information and medical guideline knowledge. This method overcomes the limitations of traditional knowledge representation in multi-entity joint modeling, improves the semantic expressiveness and computability of medical knowledge, and provides support for clinical decision support and medical intelligent systems.

A method for assisting a patient in weaning from a therapy, the method being executed by a controller, and the controller being configurable with different at least one weaning strategy. The method includes setting, based on at least one input by a user, a target weaning strategy among the at least one weaning strategy, where the at least one input corresponds to a clinical decision determined by the user.

The present disclosure provides systems and methods for clinical decision support of the diagnosis of infection. In an aspect, a system or method of clinical decision support (CDS) may comprise CDS of metagenomic next-generation sequencing results of clinical samples, (e.g., cerebral spinal fluid) to evaluate sample quality and microbial composition to diagnose a microbial infection (e.g., of the central nervous system).

This invention proposes an intelligent medication recommendation method based on configurable weights and dynamic score aggregation, belonging to the field of medical recommendation technology. The method includes: nonlinearly quantifying the clinical outcome indicators of each historical case to obtain an outcome quality score for each historical case; iteratively optimizing the initial weights using the importance scores of each similarity dimension configured by the pharmacist as initial weights to obtain optimized weight vectors for each similarity dimension; dynamically determining the fuzzy matching threshold for each dimension, and compiling the query conditions for each dimension into weighted search statements in conjunction with the optimized weight vectors to obtain several similar historical cases and their similarity scores; using the similarity scores as weights, weighted aggregation of the medication records of the retrieved historical cases, and outputting medication plans with support levels not lower than a preset threshold as the medication recommendation results for the current patient. This invention can achieve more valuable medication plan recommendations for the current patient, improving the clinical decision support capabilities in pharmacy consultations.

This invention discloses a multi-parameter intelligent early warning method and system for the risk of hypotension during hemodialysis, belonging to the field of intelligent medical data analysis and clinical decision support technology. The method addresses the inherent contradiction between prediction lead time and accuracy caused by the heterogeneity of multiple etiologies in dialysis-related hypotension events. The perception layer reconstructs the phase space of the hemodynamic time series and detects the state transition signals of the hemodynamic system in real time by calculating kinetic indicators. The cognition layer inputs the kinetic indicators as novel observational evidence into the causal inference network, infers the etiological type of hypotension in real time, and outputs the corresponding optimal prediction window. The simulation layer dynamically adjusts the weight configuration of the physiological constraint equations in the physical information neural network based on the etiological classification results, constructing an individualized digital twin for forward simulation prediction. The system can achieve a dynamic optimal balance between prediction lead time and accuracy, and outputs a graded early warning and targeted intervention suggestions containing three layers of interpretable evidence.

Systems and methods for enhancing clinical decision-making and workflow optimization is proposed. An example method includes the steps of collecting data from multiple healthcare sources, including electronic health records (EHRs), medical imaging, and patient-reported outcomes. The example method also includes analyzing the collected data using artificial intelligence (AI) and machine learning (ML) algorithms to generate actionable insights. Additionally, the example method includes presenting the generated insights to healthcare providers through a unified interface.