438results about "Medical references" patented technology

The invention relates to the technical field of computers, and discloses an integrated information system for clinical test archives and a data processing method thereof, and the method comprises the steps: constructing a standardized data model covering a whole process; multi-source data dynamic structured intake and integrity verification are carried out; performing multi-dimensional semantic association analysis based on a Bayesian network and a correlation coefficient; block chain type version tracing and difference comparison are carried out; performing role-sensitivity-operation-context four-dimensional access control; and task-driven collaborative sharing and auditing log records. The system comprises a unified modeling module, a data intake module, a semantic analysis module, a version tracing module, an access control module and a collaborative auditing module. The problems of data islands, semantic segmentation, poor dynamic adaptability, weak safety protection and the like in the prior art are solved, intelligent management, semantic interconnection, safety controllability and efficient collaboration of the whole life cycle of clinical test files are achieved, and data quality, audit compliance and multi-role collaboration efficiency are remarkably improved.

A system for the dynamic normalization of clinical terms and entity linking using a modular natural language processing (NLP) pipeline, consisting of: a preprocessing module configured to receive unstructured clinical text and perform operations such as de-identification, spelling correction, abbreviation expansion, and sentence segmentation; a tokenization and embedding processing unit configured to segment the preprocessed text into sub-word units and generate context-related embeddings using transformer-based biomedical language models; a processing unit for dynamic vocabulary expansion configured to update a specialized dictionary in real time by monitoring new abbreviations, acronyms, or institution-specific terms from incoming clinical data streams and feedback from clinical staff; a candidate concept generator configured to retrieve potential ontology mappings by performing an approximate nearest neighbor search via an embedding index linked to medical knowledge bases; a semantic disambiguation controller configured to evaluate candidate concepts using a graph neural network model that takes into account the local linguistic context, patient-specific metadata, and temporal relationships between entities; an ontology linking interface configured to map disambiguated entities to standardized concepts in one or more controlled vocabularies; and a feedback learning module configured to incorporate clinician corrections into the model parameters through reinforcement-based updates, thus enabling continuous self-improvement of entity normalization.

The invention discloses a diversified epiphyseal development map generation method and device based on a potential diffusion model. The method comprises the following steps: making a data set, covering and collecting an original X-ray film image, preprocessing data, labeling a skeleton maturity level and desensitizing the image; an original image is encoded to a potential space by using a variational auto-encoder, and the image detail generation capability is improved by optimizing a loss function; a fuzzy learning module is introduced in the de-noising stage of the diffusion model, and the diversity of generated skeleton features is enhanced through operations such as batch normalization and random disturbance; a progressive alignment strategy is adopted, skeleton basic structure learning is emphasized in the initial stage, a skeleton grade evaluation network is introduced in the later stage, and skeleton detail features are emphasized; optimizing the generation model by using the skeleton recognition model; and inputting development levels, ages and genders of 14 skeletons, and generating a corresponding left-hand skeleton X-ray film image. According to the method, diversified and high-quality skeletal development images are generated through a potential diffusion model framework in combination with VAE optimization, an FLM module and a progressive alignment strategy, skeletal development features are truly reflected, the problems of detail loss and insufficient diversity in the prior art are solved, the method is particularly excellent in performance when sparse data and long-tail distribution are processed, and the method is suitable for large-scale popularization and application. And the authenticity of the generated image and the practicability of medical diagnosis are improved.

The invention provides an intelligent medical information system based on multi-system integration, a data security management method and device, electronic equipment and a storage medium, and is applied to the technical field of medical data processing. According to the method, three types of information including core security elements, multi-system heterogeneous adaptation (including HIS / LIS / EMR interface specifications and the like) and full-process auditing (including operation logs and the like) are obtained. Normalizing the core security elements by using technologies such as AI multi-mode privacy identification and the like to generate a standardized security baseline file; heterogeneous adaptation information is processed through a lightweight API connector and the like, and a multi-system security access scheme is formed. Associating the baseline file with the access scheme, and generating a full-life-cycle protection strategy in a zero-trust architecture; and processing the audit information to generate a dynamic security audit report. And finally, based on a security-adaptation linkage decision engine and compliance verification, outputting a cross-system data security sharing scheme and a privacy disclosure emergency response instruction, and realizing a medical data security management closed loop.

The invention relates to a patient examination item recommendation method based on a medical knowledge graph. The method comprises the steps that firstly, historical data in the medical field is acquired, key information is extracted from the historical data to generate an entity relation triple, and then a medical knowledge graph is constructed; then, generating a patient state sub-graph based on patient input information, processing the medical knowledge graph by adopting an improved TransD model, and determining unique representation of an entity-relationship pair; mapping the patient state sub-map to a unique representation space, obtaining vector representation of a patient and an examination item related entity, and calculating a similarity value between vectors to obtain a similarity value matrix; and finally, performing priority ranking on the inspection items according to the matrix, and generating a final recommendation list through threshold screening and combination with graph neural network verification, optimization and adjustment of priorities. By adopting the method, examination items can be accurately recommended for patients, doctors are assisted to improve the treatment efficiency, the queuing time of the patients is shortened, and the medical experience of the patients is remarkably improved.

The invention discloses a chronic disease risk prediction method and system fusing a knowledge graph and a large language model, and the method comprises the following steps: obtaining a natural language problem related to a chronic disease, and carrying out the semantic analysis; according to the analysis content, hypothetical questions and answers related to chronic diseases are generated through a large language model, and key entities are extracted; mapping the key entities to corresponding nodes in a medical knowledge graph, exploring a semantic path and a causal relationship between the key entities, and constructing an inference chain pointing to potential disease risks from acquired information; introducing a fragment granularity sensing mechanism, performing fine granularity analysis on each fragment in the reasoning chain, and rearranging and optimizing a link sequence; and based on the optimized inference chain, converting the question and answer result into a structured diagnosis result for visual display. According to the method, the whole process from question asking to answer generation of the patient is optimized, the efficiency and accuracy of chronic disease risk prediction are effectively improved, and meanwhile, personalized health management service is provided for the patient.

The invention relates to the technical field of intelligent medical treatment, in particular to a household intelligent health management system and a method thereof.The system collects household environment parameters and user physiological indexes through a data collection subsystem, constructs a medical knowledge graph of a three-layer structure through a knowledge graph driving subsystem, and dynamically updates the medical knowledge graph; the multi-target health intervention subsystem generates a candidate intervention scheme set based on a knowledge graph, the digital twinborn verification subsystem predicts and selects a scheme with a health improvement index higher than a target by establishing a user, environment and interactive twinborn model, and the adaptive interaction subsystem pushes the verified scheme to a user in a personalized manner and collects feedback information. Meanwhile, the digital twinborn verification subsystem transmits the feedback information to the knowledge graph driving subsystem to trigger dynamic updating of the knowledge graph, the system comprehensively collects health influence factors, comprehensive data support is provided for health risk assessment, and personalized health management is achieved.

A computer-implemented system and computer instructions stored on non-transitory computer readable medium for bilingual medical inquiry in both Arabic and English, including multiple-choice question answering, open-ended question answering, and multi-turn question answering. The system and instructions use a mixture of experts large language model (MOE LLM) having a router network connected to multiple expert networks. The MOE LLM is trained with medical domain data and is used to receive the input bilingual text in a format for a medical inquiry, and output text in a format of a response to the medical inquiry, in sequence. The system and instructions incorporate an English-to-Arabic translation pipeline having a language translation model to generate Arabic language medical instruction sets from English language medical instructions, for large scale use in Arabic and English medical inquiry.

The invention discloses a cognitive disorder early screening and diagnosis system based on multi-modal data, and the system comprises the following modules: (1) a multi-modal data collection module which is used for obtaining image data, gene data, behavior data and clinical narrative information; the clinical narrative information comprises at least two of a patient chief complaint text, an interference factor hooking option and a symptom time axis; the causal purification module is used for filtering pseudo-correlation among the multi-modal data through clinical narrative anchoring and reverse intervention deduction, and comprises a narrative anchoring unit which is used for identifying data conflicts through a structured clinical narrative field based on a preset causal conflict rule base, and triggering freezing or weight descending operation; the intervention deduction unit is used for generating a reverse intervention verification packet containing a low-cost intervention measure and a review plan, and updating a causal rule base based on review data; according to the method, the statistical association limitation of the traditional technology is broken through, and the diagnosis and treatment normal form upgrading from correlation to causality is realized.

The invention discloses a medical document slicing and retrieval method and device, equipment and a storage medium, and the method comprises the steps: generating a high-fidelity semantic summary text and a child node of a structured keyword set through a father node containing an original complete paragraph text, and storing the child node to a vector database; storing the semantic summary text and the structured keyword set into a keyword database; and when a user query instruction is received, performing dual-channel retrieval through the vector database and the keyword database, obtaining related target child nodes, extracting target original contents of a target father node from the target child nodes, transmitting the target original contents to the large language model, generating a final answer, and sending the final answer to the user. According to the method, the relevance and accuracy of retrieval results can be remarkably improved, instant and accurate decision support basis is provided for doctors, semantic integrity can be reserved, efficient multi-modal retrieval can be achieved, and the speed and efficiency of medical document slicing and retrieval are improved.

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.

The application discloses a kind of cognitive impairment early screening and diagnosis system based on multi-modal data, including following module: (1) multi-modal data acquisition module, for obtaining image data, gene data, behavior data and clinical narrative information;The clinical narrative information includes at least two kinds of patient complaint text, interference factor hook item, symptom time axis;(2) causal purification module, for filtering the pseudo-correlation between multi-modal data by clinical narrative anchoring and reverse intervention deduction, including: narrative anchoring unit: based on preset causal conflict rule base, through structured clinical narrative field identification data conflict, trigger freezing or weight reduction operation;Intervention deduction unit: generate the reverse intervention verification package containing low-cost intervention measures and review plan, and update causal rule base based on review data;The application breaks through the statistical correlation limitation of traditional technology, realizes the upgrade of diagnosis and treatment paradigm from "correlation" to "causality".

The invention relates to the field of intelligent medical treatment, in particular to a traditional Chinese and western medicine multi-modal data association network construction method and device and a program product. Comprising the steps of S1, acquiring a traditional Chinese medicine inquiry data set and a western medicine clinical data set of a patient; s2, performing first feature extraction on the traditional Chinese medicine inquiry data set to obtain N time sequence traditional Chinese medicine features X1, wherein N is the number of doctor seeing times; s3, performing second feature extraction on the western medicine clinical data set to obtain N time sequence western medicine features Y1; s4, the correlation between the Mth time sequence traditional Chinese medicine feature and the Mth time sequence western medicine feature is calculated to obtain a traditional Chinese medicine related feature X2 and a western medicine related feature Y2 which have an incidence relation, M is smaller than or equal to N, Y2 is smaller than or equal to Y1, and X2 is smaller than or equal to X1; and S5, taking the Western medicine related features as labels, obtaining the traditional Chinese medicine related features corresponding to each Western medicine related feature based on the association relationship, and generating an N * Y2 Western medicine related feature-traditional Chinese medicine related feature association network. The application has good clinical value.

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 relates to pattern recognition and anomaly detection in the technical field of computers, and discloses a laying hen disease early-stage AI early warning method and a laying hen disease early-stage AI early warning system. The method comprises the following steps: obtaining a laying hen behavior video stream, an excrement image and body temperature data, and carrying out preprocessing such as timestamp alignment and equipment number mapping to generate a training and reasoning input structure; then target tracking, key point coding, region segmentation, contour feature coding and body temperature fragment statistical coding are executed to extract a multi-source feature sequence; performing time window alignment, scale normalization, pattern recognition and anomaly detection processing on the multi-source features to realize disease type recognition; and finally, generating an early warning event based on rule triggering and threshold judgment, and forming closed-loop disposal through prevention and treatment scheme retrieval and instruction arrangement. According to the method, collaborative analysis and early abnormity early warning of multi-source heterogeneous data are realized, and the disease recognition accuracy and early warning timeliness are remarkably improved.

The application relates to the technical field of computers and discloses an integrated information system for clinical trial archives and a data processing method thereof, the method comprising the following steps: constructing a standardized data model covering the whole process; dynamically structuring and inputting multi-source data and checking the integrity; performing multi-dimensional semantic correlation analysis based on a Bayesian network and a correlation coefficient; performing block chain version tracing and difference comparison; performing four-dimensional access control of roles-sensitivity-operations-context; and performing task-driven collaborative sharing and audit logging. The system comprises six modules, namely, unified modeling, data input, semantic analysis, version tracing, access control and collaborative audit. The application solves the problems of data islands, semantic fragmentation, poor dynamic adaptability and weak security protection in the prior art, realizes intelligent management, semantic interconnection, safe controllability and efficient collaboration of the whole life cycle of clinical trial archives, and significantly improves data quality, audit compliance and multi-role collaboration efficiency.

The invention relates to the technical field of medical literature translation, in particular to a medical literature translation method based on a large language model and a term library. Comprising the following steps: S1, reading a to-be-translated document to obtain a document object model (DOM); s2, screening out a special format in the document object model; s3, a translation database is constructed, the translation database comprises a term library, a memory library and an abbreviation library, an Aho-Corasick automaton and a vector index technology are used, the document to be translated is matched with the translation database, and a corresponding matching result is obtained for each translation database; s4, using an AI system to provide a matching result in the step S3 for the AI system as a reference, and completing translation of the to-be-translated document to obtain a translated text; s5, in the translated text translated in the step S4, the special format screened out in the step S2 is restored, and the accuracy and specialty of medical literature translation can be improved.

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

The invention provides a medication guidance system and method based on knowledge graph and vector retrieval fusion, and relates to the technical field of artificial intelligence, and the medication guidance system comprises an application access layer, a service interface layer and a mixed retrieval engine layer; the application access layer is used for receiving external demand information and transmitting the external demand information to the large model platform; the service interface layer is used for receiving a query instruction of a large model platform, transmitting the query instruction to the mixed retrieval engine layer and transmitting a retrieval result to the large model platform, so that the large model platform outputs the retrieval result; the mixed retrieval engine layer is used for generating a graph database query statement and a query semantic vector according to the query instruction, performing structured query on a graph database according to the graph database query statement, performing semantic similarity retrieval in a vector database based on the query semantic vector, and generating a graph retrieval result and a vector retrieval result to the large model platform. Through the medication guidance system, specialized medication guidance can be realized.

The invention relates to a multi-modal medical text extraction method based on adaptive domain knowledge fusion, and belongs to the technical field of medical text mining, and the method comprises the following steps: text entity-relationship mining: extracting related entities and semantic relationships from clinical structured and unstructured texts, and generating a preliminary text disease network; professional knowledge linking: performing knowledge alignment by using an external medical knowledge graph; and extracting a medical spatio-temporal event chain: extracting an event sequence with a time sequence and a spatial position from a clinical text, constructing a dynamic text disease knowledge network, and extracting a multi-modal medical text.

The invention belongs to the technical field of traditional Chinese medicine information processing, and provides a traditional Chinese medicine disease medical case database construction and management system, which comprises a data acquisition module for acquiring original traditional Chinese medicine medical case data from a plurality of heterogeneous data sources; the data processing module is used for sequentially carrying out preprocessing, standardization and deep structured processing on the original data; the knowledge graph engine module is responsible for constructing, storing and managing a traditional Chinese medicine case knowledge graph; the database storage module adopts a distributed storage architecture to store standardized medical case data and a knowledge graph; and the intelligent query and analysis module receives a request input by a user in a natural language or a structured query condition, executes multi-hop association retrieval and bidirectional path reasoning based on the traditional Chinese medicine medical case knowledge graph, and outputs medical case data meeting the condition and an association knowledge reasoning result. According to the system and the method, systematized integration and deep mining are performed on traditional Chinese medicine case data, and through cooperative work of multiple modules, the limitations of data dispersion, different formats and low retrieval efficiency in traditional medical case management are broken through.

The invention discloses an auditing method and device during function execution in reproductive medical service. A server side method comprises the steps that an information operation request sent by a client side is received; the information operation request carries a globally unique identifier and auditing parameters of the patient; checking whether the information operation request has an operation authority or not; when the information operation request has the operation authority and the parameter state of the auditing parameter is to-be-stored, performing data desensitization on original medical information in the auditing parameter to obtain to-be-stored medical information; constructing a first operation log based on a data operation function expression and a service attribute in the audit parameter; and storing the to-be-stored medical information to the medical data table by adopting the global unique identifier, and storing the first operation log to the audit data table. By adopting the embodiment of the invention, the security of data transmission and storage can be improved. Meanwhile, the operation behavior of each link is accurately recorded and monitored through a refined management mode, and the transparency and traceability of data are improved.

The invention discloses a clinical medicine practical skill multi-dimensional evaluation and analysis system, and relates to the technical field of clinical medicine education and skill evaluation, and the system comprises a multi-source data collection module which obtains operation and other multi-dimensional data; the data standardization preprocessing module unifies formats, corrects exceptions, encrypts and desensitizes; the data intelligent quantitative calculation module converts a quantitative result by using an algorithm; the multi-dimensional dynamic evaluation module constructs a three-dimensional model, sets three stages of periods, and combines AI and manual output scores; the personalized feedback module generates a report and a scheme and supports multi-scene data linkage; according to the method, multi-source data is integrated, the limitation of single data of traditional evaluation is broken through, the difficult-to-quantify ability is converted into objective indexes through an intelligent algorithm, a three-dimensional evaluation model and a three-level evaluation period are constructed, skill development is dynamically tracked, AI and manual rechecking are fused, efficiency and accuracy are balanced, and a personalized scheme and a multi-scene report are generated. And clinical medicine evaluation upgrading is promoted.

The invention provides a control method and system for interaction between emergency simulation training and a real respirator, and the method comprises the steps: collecting control parameters of the respirator, and carrying out the preprocessing of the control parameters, and obtaining an equipment parameter vector; calculating a physiological response state based on the device parameter vector; the physiological response state comprises alveolar pressure and oxygenation estimation; based on the physiological response state, performing quantitative judgment on the disease stage of the patient to obtain a current disease stage, and executing a corresponding evolution instruction set; a medical specification operation reference table is constructed, a target interval is extracted from the current illness state stage and the evolution instruction set, the matching degree of the target interval and a reference interval is calculated, and the reference interval is extracted from the medical specification operation reference table; determining whether to continue evolution, enter relieving or mark training is completed based on the matching degree; the breathing gas of the patient is simulated and restored through the gas compressor and the gas mixing device and is conveyed to the real breathing machine.

The invention relates to the technical field of physiological parameter monitoring, in particular to a patient physiological feature monitoring method based on nursing sampling data. Comprising the following steps: acquiring body temperature, pulse and systolic pressure data in different periods, marking time and sources to generate a period-divided sequence, dividing sampling frequencies in combination with parameter variation and historical data to generate hierarchical labels, identifying trend directions to construct a classification list, extracting time node marking trends to generate a positioning table, and marking the time node marking trends according to the positioning table. And integrating the frequency trend relationship to construct an atlas identification set. According to the method, the anomaly tracking efficiency is improved by integrating parameter data in different time periods and marking source time, enhancing data relevance, dynamically evaluating parameter change characteristics and historical fluctuation levels, dividing adaptive sampling frequencies, optimizing monitoring density and reducing redundancy, identifying trend directions and classifying structures, and constructing positioning labels; the spectrum identification is constructed by integrating the frequency and trend relation, real-time early warning is supported, the nursing strategy and the patient state are synchronized, and high-time-efficiency accurate intervention is supported.

The invention discloses a digital orthodontic treatment planning system, and relates to the technical field of orthodontics. The system comprises a data acquisition module, a three-dimensional model construction module, a case database module, a diagnosis model construction module and a treatment planning module. The data acquisition module is used for acquiring oral scanning and CBCT scanning data and generating an oral three-dimensional model; the three-dimensional model construction module performs fusion processing on the two types of models to construct a three-dimensional gum model; the case database module is used for constructing a case database on the basis of soft tissues, CBCT projection measurement hard tissues and three-dimensional gum model characteristics; the diagnosis model construction module constructs an intelligent diagnosis model through a self-adaptive deep neural network and generates a treatment scheme set; and the treatment planning module combines patient data to judge malformation, formulate a scheme, and output a feasible treatment scheme after collision detection, model adjustment and scheme evaluation.

The invention discloses a database tuning method and device based on quantum computing, a medium and equipment, and the method comprises the steps: obtaining the value range of a plurality of database parameters, and setting a tuning target; based on the value range, mapping the database parameters into an initial superposition state of the quantum bits, and inputting the initial superposition state into the quantum strategy network; carrying out training optimization on built-in parameters in the quantum strategy network based on an adjustment and optimization target in combination with database state information measured in real time in a training process; and after training is finished, measuring and decoding an output quantum state of the quantum strategy network to obtain optimized database parameter configuration. According to the method, an excellent performance optimization effect can be achieved without depending on a high-quality sample, and the parameter optimization efficiency is greatly improved.

A method for automatically generating a research and development proposal for an in vitro diagnostic kit, performed by a server for automatically generating the research and development proposal, includes: acquiring an initial proposal including intended use information for the kit; providing the acquired initial proposal to a pre-trained language model; acquiring a specification (spec) for the kit from the language model receiving the initial proposal, wherein the acquired specification for the kit includes at least one of a technical specification to be used for the research and development of the kit, a resource specification for the research and development, a design specification for the kit, a regulatory approval specification that is needed to meet for approval, and a performance specification for the kit; and controlling to generate the research and development proposal for the kit including the acquired specification and the intended use information for the kit.

The invention relates to the technical field of knowledge graph construction, in particular to a poultry disease knowledge graph construction method, which comprises the following steps of S1, defining a top layer ontology framework; s2, performing term merging on the multi-source poultry disease data to generate a standardized term set; s3, constructing an environment-pathogen-host chain type triple based on the ontology framework and the standardized term set; s4, performing space-time alignment and conflict resolution on the chain triad in the S3, and outputting a fusion knowledge base; s5, extracting a triple change sequence of continuous time slices based on the fusion knowledge base in the S4, and generating a dynamic propagation chain; and S6, executing logic verification and backtracking verification on the dynamic propagation chain in the S5, and outputting a finally constructed poultry disease knowledge graph. According to the method, through multi-source data standardization, dynamic propagation modeling and logic verification fusion, structured, dynamic and credible expression of the poultry disease propagation path is realized, and a complete and reliable knowledge support is provided for accurate prevention and control.

The present invention aims to provide appropriate answers to inquiries regarding medical reports. [Solution] The cloud management server 10 has a general-purpose LLM 16h, a database 15 that manages knowledge graph data 15a generated based on multiple vectorized information obtained by vectorizing text contained in multiple medical information, and an information presentation unit 16g that, when question data related to a medical report is received from a doctor's terminal 30, transmits answer data that responds to the question data in the medical report to the doctor's terminal 30 based on the database 15 and the LLM 16h.