359 results about "Specialty" patented technology

The invention relates to an intelligent diagnosis and treatment method and system based on a knowledge graph and a large language model. The method comprises the steps of constructing a basic framework of a medical field knowledge graph based on collected medical field data, constructing multi-level knowledge graph expression, obtaining symptom description of a patient through multiple rounds of dialogues, diagnosing the patient and generating a diagnosis report, rechecking and adjusting the diagnosis report by a professional physician, and finally outputting the diagnosis report. The intelligent diagnosis and treatment system comprises a knowledge graph construction module, an intelligent question and answer system, an entity matching module, an intelligent diagnosis module, a doctor review and diagnosis report adjustment module, a diagnosis report output module and the like. And main diseases are screened, so that the coverage is wider. And three levels of knowledge maps are constructed, so that text matching with finer granularity can be carried out. The non-standardized expression of the patient is firstly converted into standardized medical terms through a large model, and drug allergy and the like are taken into consideration, so that the diagnosis accuracy is improved, and the diagnosis and treatment risk is reduced.

The invention discloses an elderly disease health question and answer method based on multiple agents and a knowledge graph, and belongs to the crossing field of artificial intelligence and medical information technologies. Structured representation of medical knowledge is realized by constructing a multi-modal medical knowledge graph and through multiple entities and association relationships thereof. In combination with a domain customized large language model and a multi-agent dynamic routing mechanism, a two-stage training strategy is adopted to optimize a base model, including all-parameter pre-training and parameter efficient fine tuning based on a low-rank decomposition technology, so that the professionality and reliability of the model are effectively improved. In a multi-agent collaborative architecture, the system dynamically selects an optimal processing path according to input characteristics, including knowledge graph query, professional answer generation or external knowledge retrieval, and maintains context continuity of multiple rounds of conversations through a dynamic abstract compression algorithm. According to the method, the multi-agent collaborative architecture is combined with the knowledge graph reasoning ability, and the advantages of relation query and semantic matching are integrated through the mixed retrieval strategy.

The invention relates to the technical field of diagnosis and treatment path planning, in particular to an intelligent diagnosis and treatment path dynamic planning system based on symptom ontology reasoning, which comprises a symptom specialized mapping module, a holographic panoramic navigation module, a path optimization core module and a real-time feedback module, wherein the symptom specialized mapping module generates a dynamic medical resource recommendation parameter set by fusing a symptom ontology feature vector, medical institution treatment efficiency data and a specialized association rule base, and dynamic medical resource recommendation parameters comprise a multi-dimensional matching degree tensor and a treatment efficiency quantification matrix; the holographic panoramic navigation module receives the multi-dimensional matching degree tensor and constructs a three-dimensional medical resource topological graph, wherein the three-dimensional medical resource topological graph comprises hospital nodes and department connecting lines; the path optimization core module analyzes hospital nodes in the three-dimensional medical resource topological graph, and generates a path with optimal cost performance based on a multi-objective optimization algorithm of a treatment efficiency quantification matrix; and the real-time feedback module acquires and updates the treatment efficiency quantization matrix through a back propagation algorithm to form a closed-loop optimization system.

The invention relates to the technical field of information security management, and discloses an information security management method and system based on digital medical treatment. The method comprises the following steps: verifying doctor professional qualification, patient authorization range, data sensitivity level and time permission validity in a multi-institution medical data access request, and generating a permission verification result; analyzing ontology semantic attributes and sensitivity characteristics of medical search keywords input by a user, and determining a corresponding hierarchical encryption strategy based on the permission verification result; performing homomorphic encryption transformation on the medical term synonym set according to the hierarchical encryption strategy, and constructing a medical ciphertext database; and receiving a medical query request, executing symptom disease association matching and ciphertext state logical reasoning operation in the medical ciphertext database, and outputting an encrypted state search result. According to the method, the privacy leakage risk caused by search behavior trajectory analysis is effectively prevented, and the security and availability of medical data are improved.

Disclosed is a system and method for standalone automated patient referral management. The system comprises an intake module that accepts and processes referrals from diverse referral communication channels as entry points for patient referrals. A processing module undertakes the analysis of patient information to pinpoint a healthcare provider based on a healthcare need, a preference, and a schedule availability. A scheduling module confirms the appointment, and engages in dynamic three-way communication between the patient, the referring entity, and the healthcare provider. A feedback module dispatches feedback to the referring entity, offering a report on an outcome of the appointment. A natural language processing (NLP) module extracts information from conversational speech. The system utilizes algorithms to match patients with the healthcare providers based on criteria including specialty, availability, and patient preferences. A feedback module communicates with the originating EHR systems and provides updates to maintain continuity of care.

The invention discloses an agent interaction method and system for enhancing specific industry knowledge, and belongs to the technical field of artificial intelligence. According to the method, multi-modal original data of a target industry is collected and preprocessed, dominant entities and relationships are extracted based on an industry ontology to generate a triple, and implicit knowledge is fused to construct an iterative mixed knowledge graph; converting a user query into a vector, retrieving a Top-K candidate knowledge vector, and then performing interpretable scoring in combination with a query vector, a candidate vector and a triple to obtain an enhanced feature; and dynamically determining an expert sub-network set, and fusing reasoning results of the experts to generate a final answer. The system comprises a knowledge graph and a feature enhancement and reasoning module, and dynamic updating and accurate reasoning of knowledge are achieved. According to the method, the industry knowledge coverage degree, the reasoning accuracy and the interpretability are improved, the illusion problem of a large language model is relieved, and the method is suitable for intelligent interaction services in the professional fields of medical treatment, finance and the like.

The invention discloses a Meta analysis method and device, equipment and a storage medium, according to the scheme, automatic semantic analysis is carried out on a research problem input by a user through a preset large language model, target literature is processed through a pre-training language model, an efficient and reliable automatic tool is provided for evidence-based medicine, and the efficiency is improved. According to the method, the dependency of Meta statistical analysis on professionals is reduced, the analysis period is shortened, personal errors are reduced, the intelligent degree, efficiency, accuracy and repeatability of Meta statistical analysis are remarkably improved, and standardized and intelligent development of evidence-based medical research is promoted; in addition, retrieval is carried out based on the target structured form, the retrieval strategy is optimized, the retrieval accuracy is improved, and therefore the performance and accuracy of Meta statistical analysis are further improved.

The invention relates to a recording method and system of an electronic medical record, and aims to comprehensively and systematically collect health information of a patient through diversified input terminals, improve information input efficiency, ensure information integrity and promote team cooperation. The system comprises a medical professional terminal, a patient direct related terminal and other patient related terminals, supports information input in various forms of pictures, characters, videos, audios and the like, and covers various input devices such as portable devices, professional examination instruments and the like. And the information storage module ensures accurate homing of the data and supports cross-mechanism data integration and sharing. The system also has the functions of electronic medical record automatic generation, treatment tracking, symptom and sign extraction, intelligent analysis and early warning and the like, provides efficient and accurate information support for medical services, optimizes medical resource configuration, and enhances medical experience of patients. The method is suitable for modern medical management, and particularly has remarkable advantages in the aspect of improving the intelligence and integration level of an electronic medical record system.

The invention relates to the technical field of auxiliary diagnosis and treatment systems, in particular to an intelligent auxiliary diagnosis and treatment system for doctors. Comprising a system initialization and role service registration module, a user input analysis and intention recognition module, an intelligent routing and professional tool set selection module, an SSE streaming response initialization and workflow starting module, a knowledge graph retrieval and reasoning module, a diagnosis and treatment logic reasoning and scheme generation module and a streaming result output and node state updating module. A dynamic knowledge update triggering and verifying module; according to the invention, on the system integration level, efficient integration of multi-professional medical resources is realized based on role-as-a-service architecture, and complete coverage of multi-professional knowledge modules of internal medicine, surgery, oncology and the like is ensured through a role service registration validity evaluation mechanism.

Novel tools and techniques are provided for implementing intelligent assistance (“IA”) or extended intelligence (“EI”) ecosystem for pulmonary procedures. In various embodiments, a computing system might analyze received one or more first layer input data (i.e., room content-based data) and received one or more second layer input data (i.e., patient and / or tool-based data), and might generate one or more recommendations for guiding a medical professional in guiding a surgical device(s) toward and within a lung of the patient to perform a pulmonary procedure, based at least in part on the analysis, the generated one or more recommendations comprising 3D or 4D mapped guides toward, in, and around the lung of the patient. The computing system might then generate one or more XR images, based at least in part on the generated one or more recommendations, and might present the generated one or more XR images using a UX device.

The invention relates to the technical field of medical decision assistance, and particularly discloses a doctor-patient co-decision multi-issue negotiation method and system and a readable storage medium, and the method comprises the following steps: firstly collecting preference information and behavior characteristics of a doctor and a patient for a plurality of negotiation issues; constructing a fuzzy constraint and satisfaction function based on preference information and behavior characteristics; constructing a proxy behavior model based on the fuzzy constraint and the satisfaction function, and forming a distributed negotiation problem; and dynamically optimizing the negotiation strategy through a value function by adopting an ADP (adaptive dynamic programming) algorithm until the negotiation is consistent to generate a common negotiation result or the negotiation fails and terminates the negotiation, and sending a recommendation scheme to the doctor agent and the patient agent. According to the doctor-patient co-decision multi-issue negotiation method and system and the readable storage medium, the negotiation success rate and efficiency are improved, the medical specialty and patient individuation requirements are balanced, the doctor-patient satisfaction is remarkably improved, and the doctor-patient co-decision multi-issue negotiation method and system are suitable for a multi-issue medical decision scene.

The invention provides a medical record data extraction script generation method and device, computer equipment and a medium, and relates to the technical field of medical informationization, and the method comprises the steps: extracting information of a plurality of three-party medical record fields in a three-party medical service system from an interface document of a medical record data interface in the three-party medical service system, according to the identification information of each three-party medical record field, determining identification information of a target structured medical record field matched with the three-party medical record field in a target medical service system and target library table information to which the target structured medical record field belongs, and according to a current field mapping relationship between the identification information of the three-party medical record field and the identification information of the target structured medical record field, and the target library table information, generating a target medical record data extraction script for the three-party medical service system. According to the method, automatic generation does not need to be performed by professional medical personnel, the target medical record data extraction efficiency is improved, the labor cost is reduced, and the data extraction accuracy is improved.

According to the time sequence risk control method and system based on the multi-level mixed experts, heterogeneous data in the medical field is fused and mapped to the unified representation space through multi-modal representation learning, and therefore data information is utilized more comprehensively. The system adopts a multi-level hybrid expert (MoE) architecture, tasks can be accurately routed to a more professional expert group according to patient characteristics through a hierarchical gating network, and the accuracy and resource utilization efficiency of the model are greatly improved. Meanwhile, through bidirectional interpretability and a causal inference mechanism, not only can the reason of a risk prediction result be explained, but also anti-fact analysis and specific intervention measure guidance can be provided, so that the model is converted from a pure prediction tool to an intelligent partner for auxiliary decision making.

The invention provides a diagnosis and treatment model training method and system and electronic equipment, and relates to the technical field of medical diagnosis and treatment. The method comprises the following steps: performing field pre-training on a basic large language model by utilizing a medical corpus to obtain a pre-trained diagnosis and treatment model; corresponding diagnosis reasoning process information is generated for each piece of target medical record data in the target medical record data set, a supervision fine tuning data pair set is obtained, and each supervision fine tuning data pair in the supervision fine tuning data pair set comprises medical record information and a diagnosis conclusion containing the diagnosis reasoning process information; and performing supervision fine tuning training on the pre-trained diagnosis and treatment model by using the supervision fine tuning data pair set to obtain a trained diagnosis and treatment model. According to the method, the diagnosis and treatment model can master professional contents such as disease classification, clinical symptoms, diagnosis processes and drug treatment, and the effect of simulating a doctor to gradually analyze the symptoms to obtain a diagnosis result is achieved, so that the accuracy of the diagnosis and treatment result is improved.

The invention discloses a traditional Chinese medicine clinical auxiliary decision making system and method based on a multi-mode large language model. According to the method, through a multi-modal information perception and fusion step, visual feature extraction is carried out on a tongue picture image, and the tongue picture image is fused with clinical text semantics; through a case matching step of retrieval enhancement, similar cases are retrieved from a clinical case knowledge base; a clinical auxiliary evaluation report including syndrome discrimination, diagnosis conclusion, traditional Chinese medicine prescription and reasoning process is generated through a context-aware diagnosis generation step; and the clinical rationality of an output result can be ensured by adopting field customization indexes through specialized evaluation steps. A corresponding system comprises a tongue diagnosis generator, a clinical case knowledge base, a diagnosis generator and other components, and end-to-end clinical decision support is achieved. According to the method, the problems of difficulty in integrating multi-modal information, lack of professional data sets and the like in traditional Chinese medicine diagnosis are effectively solved, and doctors are assisted to improve the accuracy and reliability of diagnosis results.

The invention discloses a robot-assisted operation error detection method based on cooperative agent reasoning. The method comprises the following steps: constructing a multi-classification operation error system and a clinical informed knowledge base; based on a multi-classification operation error system and a knowledge base, a chain type reasoning prompt base is constructed according to different professional levels and analysis perspectives; for an input operation video clip, by calculating a composite risk score of a technical complexity score and a clinical influence score, allocating error instances of different risk scores to corresponding professional level reasoning paths; and deploying a time analysis agent, a space analysis agent and a process analysis agent for each professional level reasoning path, calling a corresponding chain reasoning prompt by each agent to analyze the operation video clip, and outputting a final error detection result based on an optimization threshold value by weighting and fusing analysis results of each agent. According to the invention, accurate detection of various surgical errors can be realized, and detection efficiency and clinical suitability are considered at the same time.

The invention provides a three-dimensional science popularization animation generation method and system based on an AIGC technology, and relates to the technical field of animation generation. The method comprises the following steps: firstly, fusing structured knowledge such as an anatomy term library, a diagnosis and treatment process guide and a drug specification with electronic medical records and CT / MRI volume data, and generating a split script and a medical term list through a large language model; then reconstructing an organ or focus grid by using a script-image combined driven text-three-dimensional diffusion model, and executing anatomical consistency correction according to the medical knowledge graph; automatically arranging a time axis, a camera track and a skeleton action, and synchronously synthesizing multilingual parastyle subtitles; and finally, frame-level rendering and compression packaging are completed in a real-time rendering engine, and efficient, professional and iterable medical animation generation is realized by means of an expert feedback closed-loop fine tuning model.

The invention belongs to the field of artificial intelligence, and particularly relates to a medical pre-inquiry real-time interaction method and system, a terminal and a medium. Screening medical knowledge contexts similar to the consultation text from a professional medical knowledge base, and recording the medical knowledge contexts as related texts; based on a pre-constructed prompt template, constructing a current consultation prompt through the consultation text and the related text; inputting the current consultation prompt into the large language model, and outputting consultation reply content; performing voice synthesis on the consultation reply content to generate consultation reply voice data, storing the consultation reply voice data in the buffer area, extracting reply voice data from the main buffer area for voice output, and transferring data in the auxiliary buffer area to the main buffer area when the data in the main buffer area is less than a threshold value; speech synthesis and speech output are asynchronously processed. According to the invention, the professionality and accuracy of medical pre-inquiry are improved, the playing lagging caused by waiting for synthesis is effectively avoided, and the interaction experience effect is improved.

The invention discloses a security and robustness automatic testing method for a large language model in the medical field, and the method comprises the steps: constructing a field perception harmful problem generator which can automatically learn and map medical knowledge to potential harmful queries; according to the method and the system, the real security boundary of the large language model in the medical field can be efficiently and systematically evaluated, and particularly, the evaluation depth and the evaluation breadth are provided in complicated situations involving professional knowledge misleading or potential harm. A generator is optimized through a customized weighted composite reward function, so that the model is effectively guided to generate a field perception harmful problem which has high harmfulness, is coherent and smooth and conforms to grammar, and the quality and pertinence of a data set are remarkably improved; according to the framework, the dependence on a large amount of time-consuming manual intervention is fundamentally eliminated by integrating automatic scoring and a multi-index similarity filtering mechanism of a hazard detection model.

The invention provides a medical diagnosis reasoning method and system based on multi-agent coevolution, and the method comprises the steps: driving a large-scale language model to decompose a medical problem through a problem decomposition agent, and determining a to-be-retrieved sub-problem through a self-adaptive retrieval agent, and selecting a proper knowledge base and a proper retrieval algorithm to obtain multi-source retrieval data, and verifying that the intelligent agent performs de-reforming synthesis on the data to obtain an integrated abstract. The expert recruitment agent builds an expert team according to the abstract, the expert agent diagnoses and reasones to build a dynamic reasoning library, the reasoning library is iteratively optimized through judgment, cross reasoning and the auto-reflection physician agent, the dynamic convergence controller monitors the reasoning library, iteration is terminated when conditions are met, and a final diagnosis decision is output. According to the method, information is directionally obtained from a plurality of authoritative and professional medical knowledge bases through problem multi-granularity retrieval and population evolutionary reasoning, and reasoning efficiency can be improved while reasoning correctness is guaranteed.

Systems and methods for providing an intelligent network to onboard patients to specialty medications are described herein. A server system for managing communication is configured to receive from a user at a healthcare practice, an electronic request to obtain a medical prior authorization (medPA) for a specialty drug to administer for a patient of the healthcare practice; select, using a rules service, a communication pathway to submit the medPA to a healthcare payer from a plurality of communication pathways; and initiate a connection with the healthcare payer using the selected communication pathway.

The invention relates to a community household rehabilitation resource matching and scheduling method and device based on multiple agents. The method comprises the following steps: dynamically acquiring rehabilitation service demand data and rehabilitation resource data in a community through a data acquisition and preprocessing agent; evaluating the rehabilitation service demand data and the rehabilitation resource data of the patient through a demand evaluation agent, and generating a rehabilitation demand priority of the patient and a professional field preference list of a rehabilitator; generating a rehabilitation resource portrait of the rehabilitation demand priority and the rehabilitator professional field preference list through a resource portrait agent; and performing reinforcement learning on the rehabilitation resource portrait through a matching scheduling agent, and decomposing a matching problem into two sub-tasks of coarse-grained matching and fine scheduling. According to the invention, a solution with adaptive capability is provided for community household rehabilitation resource matching and scheduling, so that the overall level of community household rehabilitation service is improved.

The invention discloses a multi-modal data-based auxiliary decision-making system suitable for a perioperative period and an intensive care period, which can provide multi-dimensional reference contents such as real-time monitoring, attribution analysis and decision-making recommendation of physiological parameters for a user, can provide auxiliary information for decision making of medical personnel, and can improve decision-making efficiency. Meanwhile, the system greatly reduces the requirements for medical knowledge and personal professional literacy of medical personnel, not only reduces the requirements for personnel ability, but also weakens the dependence on human experience, and can realize dynamic adjustment of physiological parameter calibration data, so that compared with the traditional technology, the use reliability is improved; therefore, the method is very suitable for large-scale application and popularization.

The invention provides a medical question answering method and system based on multi-agent cooperation, and relates to the technical field of artificial intelligence, the method comprises the following steps: decomposing a medical consultation question input by a user according to an intention category to obtain at least one sub-question; distributing each sub-question to a target question and answer agent associated with the corresponding intention category in the question and answer agent set; according to the answer content returned by each target question and answer agent, generating current summarized content; performing risk review on the current summarized content by using a plurality of preset review intelligent agents to obtain a current review result; wherein different review intelligent agents correspond to different professional fields; and according to the current review result, determining target answer content corresponding to the medical consultation question. According to the invention, the completeness and accuracy of the answer content are improved, the risk detection capability is improved, and the safety and reliability of the system are improved.

The invention relates to the technical field of medical education, and discloses a virtual simulation method for an interactive anesthesia crisis skill training scene, and the method comprises the following steps: constructing a high-fidelity operating room and a crisis processing scene in a virtual environment; physiological parameter changes of the patient are simulated in real time and fed back to the student interface; recording and analyzing operation steps, decision-making time and physiological feedback of the trainee in training; and the learning path of the trainee is dynamically adjusted through a multi-objective optimization algorithm, so that the training effect is enhanced. According to the invention, through a personalized learning path and a real-time feedback mechanism, the effectiveness of anesthesia crisis skill training is improved. Students practice in a vivid virtual environment, and the clinical decision-making ability and the operation accuracy are enhanced. Meanwhile, the system is continuously updated to ensure that the training content is consistent with the latest medical practice, high-quality medical professionals are cultivated, and the medical service quality is comprehensively improved.

A computer-implemented method of evaluating a user image of a patient to enable identification of a historical twin of the patient. The method includes organizing a plurality of medical images in an archive and receiving from a medical professional each of: (i) a region of interest; (ii) a textual description; (iii) selections for binary criteria; and (iv) weights of weighable criteria. The method comprises using a natural language search to create a relevant set of medical images and creating an optimal set from the relevant set of medical images by discarding medical images from the relevant set based at least on the selections for binary criteria. The method includes image processing medical images in the optimal set using the weight of the features of the region of interest to create medical image results. The relevant set comprises less than ten percent of the medical images in the archive.

At least one database stores clinical activity data indicative of clinical activities of medical professionals or maintenance activity by medical equipment servicing personnel. Consumption of educational content units related to one or more medical devices by a medical professional (or maintenance thereof by a servicing person) is tracked. Future clinical (or maintenance) activities to be performed by the medical professional (or servicing person) is predicted based on the clinical (or maintenance) activity data. One or more metrics are calculated related to the medical professional's (or servicing person's) knowledge and / or experience for a future time. The metrics may include a knowledge metric based on the tracked consumption of educational content units, and / or an experience metric based on the clinical (or maintenance) activity data and the predicted future clinical (or servicing) activities. One or more refreshment educational content units are recommended based on the one or more metrics.

The embodiment of the invention provides a medical information processing method, an information processing method, equipment, a storage medium and a program product. In the embodiment of the specification, an automatic model selection mechanism is provided, and cooperative work of an inference model and a non-inference model is realized through a problem classification model. The problem classification model is used as a'routing center ', and the user requests are intelligently distributed to different medical question and answer models for processing according to the types of the medical problems, so that the advantages of the different medical question and answer models are fully played, and optimal configuration of system resources and improvement of overall performance are realized. Besides, intelligent routing is carried out to the corresponding medical question and answer model for question answering, the answering quality is enhanced, quick response of simple questions and deep analysis of complex questions are ensured, and optimal configuration of system resources is realized. In addition, the user can obtain the medical answer information without selecting knowledge by a professional model, so that the answering efficiency and the user satisfaction of the medical question-answering system are improved.

The invention belongs to the technical field of artificial intelligence, and more specifically relates to a medical large model migration training method based on multi-matrix aggregation multi-dimensional features. The method comprises the following steps: collecting professional medical data, and constructing a professional medical data set; federal migration training is performed on the pre-trained large model, and specific professional medical knowledge is injected into the pre-trained large model, specifically, for the pre-trained Baichuan large model, a low-rank matrix is initialized, and the low-rank matrix and the weight are quantized; the quantized low-rank matrix is adapted to a linear layer and an attention layer of a Baichuan large model; extracting three different dimensions of knowledge including original knowledge, low-rank knowledge and global complex knowledge from the professional medical data set through multi-round efficient migration training; and fusing the extracted knowledge of three different dimensions and carrying out weight aggregation. The problems that an existing training method needs a large amount of high-quality data and computing resources and is poor in performance in specific medical scenes with stricter requirements are solved.

The invention provides a medical form generation method and system and related equipment, and the method comprises the steps: receiving a form creation instruction, and analyzing a medical context in the instruction through natural language processing; screening from the specialized template library based on the medical context to obtain a matched basic template, and generating a field candidate set; screening the field candidate set through a collaborative filtering algorithm in combination with medical business rules to obtain a recommended field list; performing form rendering on the basic template according to the recommended field list, and initiating a query request to a clinical term service when a medical term field is rendered; adopting multi-strategy mapping to bind the medical term fields with standard clinical terms in the clinical term service; front-end immediate verification and back-end deep verification mechanisms are deployed for user input of the base template. According to the medical form generation method, context sensing, dynamic term binding and multi-level verification are fused to cooperatively realize form generation, and the flexibility and safety of form generation are improved.