227 results about "Decision making support systems" patented technology

The invention relates to an infection risk assessment method and system for nursing, and the method comprises the steps: collecting multi-source heterogeneous data of a patient, and fusing the multi-source heterogeneous data into a unified original data set; obtaining a standardized risk feature vector based on the original data set; inputting the risk feature vectors into a multi-modal risk assessment model, and outputting risk probabilities of individual infection and group infection; forming a personalized risk index based on the risk probability; according to the personalized risk indexes, the decision support system outputs hierarchical intervention measures matched with the risk levels, and records and feeds back execution conditions and intervention effect data of the hierarchical intervention measures for dynamically updating parameters of the risk assessment model; and integrating the intervention effect data and the early warning signal into feedback information to form a dynamic infection risk assessment system adapted to specific hospital characteristics. The accuracy of infection risk assessment of nursing can be improved.

The invention relates to the technical field of medical decision, and discloses a medical decision support system based on a knowledge graph, and the system comprises a knowledge graph construction module which constructs an initial knowledge graph based on a medical ontology library, and the knowledge graph comprises entities and association relationships of diseases, symptoms and drugs; the data acquisition module is used for acquiring data from an electronic medical record, wearable equipment, a medical literature library and a hospital information system and normalizing the data through a standardized protocol; the dynamic knowledge updating module is used for processing normalized data through an incremental graph neural network; a multi-source knowledge fusion module; a context awareness module; a dynamic deduction module; and a decision optimization closed loop module. And triggering a preset clinical rule in real time based on the pathological state of the patient, dynamically adjusting the intensity value of the related edge in the factor graph, and persistently storing the intensity value back to the knowledge graph, so that logic adaptation and individualized experience precipitation of general medical knowledge in a special pathological state are realized, and the individualized treatment accuracy is ensured.

The invention discloses a property operation decision support system based on digital twinning and generative AI, which relates to the technical field of property operation management and comprises a sensing layer, a data layer, a digital twinning layer, a generative AI layer and a decision execution layer which are connected in sequence. In the invention, the generative AI layer is based on a special Transform architecture model, fuses historical operation data and digital twinning real-time data, can automatically generate an executable optimization strategy in multiple scenes such as facility maintenance, resource allocation, personalized service, emergency disposal and the like, verifies feasibility and effects in a virtual environment, and improves the implementation efficiency. The decision execution layer efficiently implements a strategy subjected to simulation verification into a physical space by using an intelligent scheduling and equipment linkage mechanism, performs quantitative evaluation and strategy iterative optimization based on an execution effect, deeply fuses a high-fidelity digital twin with a generative AI, constructs an AI decision closed-loop system based on simulation verification, and performs decision making on the AI decision closed-loop system. And a high-feasibility and feasible optimization scheme can be generated for a complex and dynamic property operation environment.

The invention discloses a ship equipment maintenance support system and method based on artificial intelligence, and the system collects the operation data of equipment in real time through a data collection and integration module, and digitalizes and integrates the historical maintenance data into a unified database; a fault diagnosis model is constructed through the model diagnosis module to carry out fault diagnosis and output a diagnosis result; an optimal maintenance scheme is provided through a maintenance decision support system according to a fault diagnosis result in combination with multi-dimensional data; real-time monitoring and trend analysis are carried out on operation data of the equipment through the fault prediction module, potential faults of the equipment are predicted, and a maintenance plan is made in advance. According to the invention, equipment operation data is collected in real time through a sensor, accurate fault diagnosis and prediction are realized in combination with a deep learning algorithm, an optimal maintenance scheme is formulated in combination with multi-dimensional data, and a maintenance plan is dynamically adjusted.

The invention provides an energy storage power station optimization operation mode decision-making method and system, and relates to the technical field of energy storage power station optimizing.A hybrid prediction model is constructed to realize high-precision decomposition prediction of power load, and meanwhile, the internal resistance of a battery is estimated in real time by adopting a recursive least square method; and the battery capacity and internal resistance parameters are dynamically corrected in combination with a temperature compensation mechanism. Through health state multi-index fusion evaluation, self-adaptive distribution of charging and discharging power is achieved, and compared with the prior art, the problem that a traditional static model cannot adapt to complex environment changes is solved. The battery capacity fading risk can be predicted in advance by introducing a double-compensation mechanism of an environmental influence index and an electric power influence index. According to the scheme, the response speed and economical efficiency of energy storage in a high fluctuation load scene are remarkably improved, and a reliable dynamic optimization decision support system is provided.

The invention discloses a motion intervention multi-dimensional decision support system and method for CIPN remission, and relates to the technical field of decision support, and the system comprises an intervention effect prediction unit which is used for collecting the multi-dimensional data of a patient, constructing a patient simulation model, and carrying out the simulation of different motion intervention schemes through the Markov decision process theory, establishing an intervention effect prediction model based on the simulation result; an exercise intervention recommendation unit; an encouraging and exciting unit; the optimal motion recommendation unit is used for updating a patient simulation model according to the constructed encouraging and incentive mechanism, analyzing a causal relationship between a motion effect and neural restoration through a hypergraph gradient causal discovery algorithm, and screening out an optimal motion recommendation scheme; and a visualization unit. According to the invention, a personalized exercise intervention scheme can be customized according to the individual characteristics and physiological status of the patient, and the effects of different intervention strategies are predicted, so that the pertinence and effectiveness of exercise treatment are improved.

A decision support system and a computer-implemented method of enterprise decision support include a data consolidation module, domain-specific machine learning models, an enterprise decision intelligence model, and a user interface layer. The data consolidation module collects data from both internal and external data sources. The domain-specific machine learning models generate decisions based on the collected data. The enterprise decision intelligence model integrates real-time trends and the decisions to provide context-aware recommendations. The enterprise decision intelligence model maintains a decision graph that connects decision variables of the domain-specific machine learning models in a causal relationship, with the domain-specific machine learning models interacting as an interconnected network. The decisions are influenced by affects of decision variables from other domain-specific machine learning models. The user interface layer facilitates interactive decision-making by way of the enterprise decision intelligence model and visualizing the recommendations.

The invention discloses an intelligent decision support system and method for grassroots social governance based on multi-modal fusion. The method comprises the following steps: S1, acquiring and preprocessing multi-modal data in the field of grassroots social governance; s2, constructing a heterogeneous graph structure according to the data type and the treatment subject category; s3, extracting single-modal features respectively and generating cross-modal dynamic association features; s4, performing collaborative optimization on the heterogeneous graph structure and the cross-modal fusion parameters by adopting a flying fox optimization algorithm; s5, analyzing data in real time according to the optimized heterogeneous graph, and generating potential risk early warning, event trend prediction and event association deduction information; s6, pushing an auxiliary decision-making scheme and a disposal strategy in real time; and S7, continuously optimizing the heterogeneous graph structure by using a feedback result. The decision-making efficiency and accuracy of grassroots social governance are effectively improved.

The present invention relates to a cognitive computing-assisted clinical decision support system designed to enable personalized neurological rehabilitation. The system acquires structured user data across clinical, anatomical, radiological, etiological, pathological, and rehabilitation domains to create individualized profiles. These profiles are mapped against a repository of historical cases using analog matching and similarity scoring to generate stratified, evidence-based rehabilitation recommendations. Real-time monitoring of rehabilitation progress is performed using global recovery and function outcome indicators, allowing for dynamic adjustment of treatment plans. Clinician intervention modules ensure safety, interpretability, and context-aware customization. The system incorporates a continuous feedback mechanism to refine future predictions and recommendations, making it increasingly adaptive over time. The invention improves rehabilitation outcome prediction accuracy, reduces recovery variability, and optimizes functional outcomes by transforming static rehabilitation models into intelligent, responsive, and personalized care pathways.

The invention provides a medical information retrieval enhancement method and device based on a large model and related equipment, and relates to the technical field of artificial intelligence. The method comprises the following steps: in response to a medical information query request, executing double-path joint retrieval of keywords and semantics in a pre-constructed medical knowledge base to obtain a candidate data set containing at least one medical information query result, the medical knowledge base is a database which analyzes the multi-modal medical data based on a multi-modal medical data deep analysis large model and is constructed according to the analyzed medical text data; and adopting a plurality of sorting tools to resort the medical information query results in the candidate data set, and performing fusion processing on a plurality of resorting results obtained by resorting to obtain a medical information query result after retrieval enhancement. According to the method and the device, the precision improvement and response efficiency optimization of medical knowledge retrieval can be realized, a high-credibility knowledge enhancement service is provided for a clinical decision support system, and the method and the device have important application value.

Described herein is a platform and supported graphical user interface (GUI) decision-making tools for use by medical practitioners and / or their patients, e.g., to aide in the process of making decisions about a course of cancer treatment and / or to track treatment and / or the progress of a disease.

A clinical decision support system and method for patients with pulmonary arterial hypertension is disclosed herein. The system may comprise a processor to process instructions to execute one or more pulmonary arterial hypertension risk algorithms configured to generate a risk score value associated with a patient surviving within a given time period. The system may comprise a means for input and output, wherein input variable data may be received and a set of risk score values may be displayed. A method for operating the clinical decision support system is also disclosed.

The invention discloses an industry and trade enterprise public health service resource optimization decision support system, and relates to the technical field of public health management, and the system comprises a data perception and access layer, a semantic fusion and modeling layer, a knowledge graph and risk calculation layer, an intelligent early warning and decision layer, and a feedback optimization closed loop. Multi-source heterogeneous data are collected through a protocol adapter, a unified data view is constructed through industry and trade public health field ontology semantic fusion, a space-time knowledge graph is dynamically generated, risks are deduced in combination with an improved SEIR space-time propagation model and a Monte Carlo method, visual early warning and interactive decision making are achieved based on digital twinborn bodies, and the method is applied to the field of industry and trade public health. According to the method, data islands can be broken, accurate advanced early warning of public health risks is realized, resource allocation is optimized, and the method is suitable for industry and trade enterprises with dense personnel such as machine manufacturing and electronic processing.

The invention relates to the technical field of intelligent medical multi-modal data analysis and nursing aid decision making, in particular to a neurology nursing risk dynamic early warning and intelligent decision support system. Comprising the steps that a data acquisition module acquires a physiological time sequence and nursing logs to obtain a multi-modal original data set; the feature extraction module performs nonlinear dynamic analysis on the physiological data to generate a state manifold vector, and performs semantic embedding on a log to generate an intervention state tensor containing spatio-temporal features; the causal reasoning module constructs a dynamic causal map, maps vectors and tensors as nodes, and adjusts transmission weights by using the tensors to suppress non-pathological fluctuations; the decision-making module calculates dynamic risks and difference values based on atlas anti-fact deduction, and generates graded early warning and an optimal nursing path; the feedback module updates the atlas parameters according to the processing result. According to the system, the problem of high-frequency false alarm caused by daily nursing actions of traditional monitoring equipment is effectively solved, the alarm fatigue of medical staff is relieved, and focusing on real critical events is ensured.

The invention discloses an infectious disease intelligent prediction and decision support system and method based on multi-source data, and relates to the technical field of infectious disease intelligent prediction, and the system comprises a prediction module and a decision module. Predicting the outbreak time period, the propagation scale and the propagation path of the target infectious disease; and the decision module is used for making a decision according to the predicted outbreak time period, the propagation scale and the propagation path of the target infectious disease. According to the method, the limitation of a traditional single data source is broken through by multi-source data integration, the multi-dimensional characteristics of infectious disease transmission can be comprehensively captured, and the accuracy and reliability of prediction are remarkably improved. The prevention and control strategy can be dynamically adjusted according to the real-time change of the epidemic situation, resource waste and excessive prevention and control are avoided, and the utilization efficiency of public health resources is improved.

The invention relates to a large-model-driven data governance decision support system, which belongs to the technical field of intelligent manufacturing and industrial automation control, and comprises a physical field evolution modeling module for establishing a real-time global stress field based on a collected multi-modal state data set and outputting a time sequence stress field data set; the dynamic drift evaluation module is used for receiving the time-sequenced stress field data set, comparing the time-sequenced stress field data set with a preset reference stress field data set and calculating a drift risk level; the defect evolution prediction module is used for inputting the time sequence stress field data set and the collected defect correlation characteristic data set into a fusion prediction model to generate a latent defect risk index; and the closed-loop self-adaptive control module is used for generating self-adaptive control gain by integrating the drift risk level and the latent defect risk index, calculating the deviation between the time sequence and the reference stress field data set, and generating a control instruction for adjusting production parameters based on the gain and the deviation. According to the method, the crossing from discrete sensing data to a continuous dynamic physical field model is realized, and a high-fidelity data basis is provided for decision making.

The invention relates to a building engineering green low-carbon construction evaluation and decision support system, in particular to the field of building engineering, and aims to construct a space-time atlas to comprehensively characterize a construction process by integrating an Internet of Things sensor, a building information model and meteorological data, and accurately estimate a future carbon emission trend by using a dynamic prediction mechanism. And key influence factors and contribution degrees thereof are identified through factor analysis, and finally real-time optimization suggestions are provided by virtue of a visual interface, so that scientific decision support for green low-carbon construction is realized, carbon emission is effectively reduced, the resource utilization efficiency is improved, and the environmental sustainability of constructional engineering is promoted.

The invention relates to the technical field of new energy power generation and control, and discloses a new energy station operation decision support system based on multi-objective optimization, which comprises a multi-source state space reconstruction module, a Riemannian manifold geometry engine module, a self-adaptive inertia Hamiltonian evolution module and a symplectic geometric integral and instruction mapping module. The system collects station data to construct a dimensionless state space, constructs a Riemannian metric tensor according to physical constraints to reconstruct a Riemannian manifold space, and calculates a geometric connection strength factor; the factor is used to adaptively modulate a virtual inertia matrix, and a dissipative Hamiltonian kinetic model is constructed; and finally, solving the steady-state generalized coordinates through a pungent-preserving numerical integration algorithm, and decoding the steady-state generalized coordinates into an equipment control instruction. According to the method, physical constraints are converted into geometric measurements, and a self-adaptive inertia mechanism is introduced, so that the problem of optimization convergence under multivariable strong constraints is solved, and the safety and accuracy of a control instruction are ensured.

The invention relates to the technical field of medical intelligent decision, in particular to an operating room nursing key node decision support system, which comprises the following steps: establishing a nursing operation reference containing a standard execution sequence and a parameter interval based on historical cases; by integrating discrete operation and continuous monitoring data in a real-time operation, a nursing process event chain with unified semantics is formed. The system compares the chain of events with an execution criterion to identify potentially risky segments and automatically extracts, for each segment, a complete operating room multi-dimensional state snapshot within a specific time window before and after its occurrence. And dynamically constructing a cause deduction network according to the state snapshots, and speculating cause chains possibly causing risks by traversing the network. And integrating all deviation information and cause analysis results, and generating a structured decision intervention prompt. According to the invention, deepening from automatic risk identification to root intelligent diagnosis can be realized, and accurate and efficient decision support is provided for operating room nursing.

The invention relates to a multi-hospital-area manpower dynamic allocation decision support system based on a hierarchical response mechanism, which integrates hospital information system, electronic medical record and office automation data in real time through a multi-source data fusion module, and constructs a distributed data warehouse through standardized cleaning. During operation of the system, the data acquisition layer captures abnormal events of the courtyard area in real time, and drives the prediction engine to output early warning after cleaning and verification. And when the response threshold value is reached, the decision-making unit automatically matches the optimal scheduling scheme and synchronizes the task instruction through the mobile terminal. Full-chain tracking is implemented in the execution process, and the resource state is dynamically updated. And afterwards, multi-index scoring is carried out based on dimensions such as human efficiency and business load, strategy library iteration and model weight adjustment are driven, and a'monitoring-prediction-decision-execution-evaluation 'closed loop is formed. According to the invention, intelligent collaboration and elastic scheduling of human resources in multiple courtyards are realized, and the emergency scene response efficiency and the medical resource utilization rate are remarkably improved.

The invention discloses an intelligent diversion and recycling decision support system for rural domestic sewage, and particularly relates to the technical field of sewage treatment.The intelligent diversion and recycling decision support system comprises a sensing acquisition module, an initial judgment identification module, a data quantification module, a path prediction module and a path execution module, the initial judgment identification module judges whether discharge abnormity exists or not and generates a quantization signal, the data quantization module calculates a resource potential index and a processing risk index, the path prediction module outputs a prediction path value based on the indexes, and the path execution module matches a shunting operation type according to the path value and calls a corresponding sewage processing path; according to the method, a closed-loop control mechanism from data to execution is constructed through structured acquisition of water quality data, abnormal triggering of a quantification process and double-index driving path prediction, dynamic and accurate scheduling of a sewage treatment path is realized, and the data utilization efficiency and the responsiveness and intelligence of a treatment decision are improved.

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.

Systems, methods, and computer-readable storage media are provided for determining and ascribing clinical conditions or diagnoses to patients and provide them to a caregiver, such as attending clinicians or other appropriate health services personnel. In particular, embodiments of the disclosure determine likely phenotypic findings that are salient to the decision-making context for a current human patient, based on anticipative sequence-mining and trajectory-mining. A sequential pattern mining and sequence itemset matching system is provided for determining likely, temporally-relevant concepts that are manifested in the information that is produced during the course of a patient's care. A clinician or caregiver may be provided the sequence itemset matching by generating a list or notice. In addition or alternatively, the results may be stored in an EHR associated with the patient.

The invention provides an AI doctor assistant agent medical information processing method based on voice interaction and large model driving, and relates to the technical field of intelligent medical treatment, and the method comprises the steps: obtaining the audio information of a doctor-patient conversation and a voice instruction through a voice collection module, and converting the audio information into a text in real time based on a real-time voice conversion module, the intelligent transfer service module is used for carrying out intention classification on texts and distinguishing doctor-patient conversations and voice instructions, the AI medical big model intelligent agent service module is used for generating structured medical information for the doctor-patient conversations, the structured medical information is automatically recorded into the electronic medical record system, and the voice instructions are automatically recorded into the electronic medical record system. And analyzing and executing corresponding desktop operation through the instruction response module and the desktop analysis large model service module. The method can consider performance, cost and data security, improves the accuracy and credibility of medical information, is suitable for electronic medical record input, clinical decision support, system operation automation and other actual scenes, and can help medical institutions improve the diagnosis and treatment efficiency and quality.

A Decision Support System for prescription verification may include software and / or hardware configured to supplement analysis and decision-making in the workflow of a pharmacist and / or take action regarding certain steps in the prescription fulfillment process. The Decision Support System may include a plurality of software modules each configured to provide information to a user for a particular review, evaluation, or check regarding the Prescription Verification process. One or more the software modules of the Decision Support System may use or be implemented as an artificial intelligence (AI) module or algorithm, e.g., one or more decision trees, predictive models, large language models (LLMs), RAG enhanced LLMs, or rules-based engines. Decision Support System is configured to check information regarding the patient receiving the medication, the provider prescribing the medication, and the medication itself to ensure data accuracy and to determine whether it is safe to dispense the medication to the patient.

The invention relates to the technical field of deep learning, in particular to a forage grass yield prediction model construction method based on deep learning, which comprises the steps of constructing a multi-source data fusion module, establishing a cold start mechanism, designing a multi-modal deep learning prediction network, integrating a physical constraint mechanism and constructing a management decision support system. A seasonal attribution analysis function is realized; in the prior art, a simple data superposition or static weighted fusion scheme is generally adopted, and inherent defects of deficiency, different scales and heterogeneity of multi-source data are difficult to process, so that the fusion feature quality is poor; according to the method, firstly, a data blank is accurately filled through an intelligent algorithm based on space-time continuity, then heterogeneous data is unified to a standard grid by using a multi-scale pyramid engine, and finally, deep fusion is performed through an attention mechanism for dynamically calculating importance of each data source; the integrity, the consistency and the information density of the input data are remarkably improved, and a solid and reliable data foundation is laid for subsequent accurate prediction.

The invention belongs to the cross technical field of artificial intelligence and decision support systems, and discloses an intelligent decision support method based on a dynamic knowledge graph and multi-modal fusion, which comprises the steps of 1, acquiring and preprocessing multi-source heterogeneous original data, 2, constructing and updating the dynamic knowledge graph, carrying out multi-modal fusion, outputting multi-modal fusion features, and 3, carrying out multi-modal fusion on the multi-modal fusion features. The method comprises the following steps of: 1, obtaining a multi-modal fusion feature and a knowledge graph embedding matrix, 2, receiving the multi-modal fusion feature and the knowledge graph embedding matrix, and outputting a candidate decision path set, decision probability distribution and a state value, 4, outputting dual uncertainty of each path by an uncertainty estimation module, 5, calculating a comprehensive reward of each path, 6, carrying out meta reinforcement learning and strategy optimization, and 7, carrying out multi-modal fusion. And step 7, outputting a final decision result, an uncertainty evaluation report and an interpretable reasoning path, and completing the decision. According to the method, the defects in the prior art are effectively overcome, and more accurate, reliable and explainable decision support is provided for a complex scene.

A mathematical model of type 1 diabetes (T1D) patient decision-making can be used to simulate, in silico, realistic glucose / insulin dynamics, for several days, in a variety of subjects who take therapeutic actions (e.g. insulin dosing) driven by either self-monitoring blood glucose (SMBG) or continuous glucose monitoring (CGM). The decision-making (DM) model can simulate real-life situations and everyday patient behaviors. Accurate submodels of SMBG and CGM measurement errors are incorporated in the comprehensive DM model. The DM model accounts for common errors the patients are used to doing in their diabetes management, such as miscalculations of meal carbohydrate content, early / delayed insulin administrations and missed insulin boluses. The DM model can be used to assess in silico if / when CGM can safely substitute SMBG in T1D management, to develop and test guidelines for CGM driven insulin dosing, to optimize and individualize off-line insulin therapies and to develop and test decision support systems.

The invention, which belongs to the technical field of VTE risk assessment, discloses a VTE intelligent assessment and prevention decision support system comprising an adaptive acquisition module, an assessment module, a decision module and a tracking optimization module. The self-adaptive acquisition module is used for receiving stock medical data and dynamic physiological data of a target patient and setting a clinical trigger mechanism to obtain directional clinical data; the evaluation module is used for screening recessive features, distributing weights based on time decay correction and dominant features, generating a fusion feature set, and constructing a dynamic risk portrait; the decision-making module is used for constructing a multi-fusion model, carrying out collaborative evaluation, calculating a fusion risk probability of a target patient, generating a risk prediction sequence, and generating a net risk probability in combination with a bleeding risk probability; and the tracking optimization module is used for constructing a dynamic scheme library, performing intervention adaptation, generating a recommended scheme list, realizing closed-loop optimization from evaluation to intervention, and comprehensively improving the accuracy and effectiveness of VTE prevention and treatment.

The invention discloses a digital asset value evaluation method and system for power dispatching. The method comprises the steps of firstly obtaining a digital asset set in a target scheduling scene, then determining internal attributes of the digital asset set based on a power system physical topology and an operation rule, and determining external value characteristics of the digital asset set based on a power market environment and a scheduling decision demand. Thirdly, value calculation is carried out on various digital assets by utilizing the model in combination with real-time and historical scheduling operation data, and a basic value score is output; and generating a comprehensive value evaluation result and a value label according to the score in combination with a preset rule and an adjustment factor. Finally, the evaluation result and the label are applied to a power dispatching auxiliary decision support system, and a quantitative basis is provided for dispatching plan optimization, data asset internal pricing and external transaction. According to the method, accurate, dynamic and business value evaluation of the power dispatching digital assets is realized, and value release and efficient utilization of data elements are effectively supported.