120 results about "Patients symptoms" patented technology

The invention discloses a medical intelligent decision-making method based on Deepseek and a time sequence causal knowledge graph, and the method comprises the following steps: 1, constructing an initial static medical knowledge graph, and generating a dynamic time sequence causal knowledge graph; 2, finely adjusting and training the DeepSeek model to enable the DeepSeek model to adapt to the medical field; and step 3, receiving and analyzing the text uploaded by the patient, performing intelligent triage and disease risk prediction, and realizing accurate matching of patient symptoms and target departments and intelligent prediction and early warning of potential diseases. The method aims at providing accurate triage and disease risk prediction for patients, constructing a scientific and efficient medical intelligent decision-making mechanism and optimizing medical resource allocation.

The invention discloses an AI-based traditional Chinese medicine dialectical auxiliary diagnosis method and system and a medium thereof, and relates to the technical field of intelligent auxiliary diagnosis, and the method comprises the steps: collecting physiological monitoring data, tongue condition data and pulse condition signals of a patient, obtaining patient symptom information, mapping the patient symptom information to a preset traditional Chinese medicine pathogenesis classification model, and generating an initial symptom feature vector; calling a knowledge base containing traditional Chinese medicine prescription rules and compatibility medication taboo, performing data space-time alignment processing and dynamic combination calculation on the initial symptom feature vector, and generating a personalized candidate prescription set conforming to the traditional Chinese medicine compatibility taboo; according to dynamic combination calculation, a solution algorithm based on a constraint satisfaction problem is adopted, and multi-dimensional association rule matching is carried out in combination with the mapping relation between syndromes and modern medical symptoms; and outputting a prescription medication recommendation scheme containing traditional Chinese medicine compatibility, dosage and decoction methods based on the candidate prescription set. The method supports the formulation of an accurate personalized diagnosis and treatment scheme, and meets the requirements of high-quality traditional Chinese medicine personalized auxiliary diagnosis and treatment.

The invention discloses a traditional Chinese medicine famous prescription and operation multi-agent collaborative consultation method and system. The system comprises a medical case knowledge base module, an expert knowledge base module, a medicine knowledge base module, a pre-differentiation module, a multi-agent diagnosis module and the like. The method comprises the following steps: constructing structured experience medical case data, an expert experience knowledge graph and a traditional Chinese medicine knowledge graph knowledge base, carrying out mixed retrieval to carry out pre-differentiation, calling multi-agent consultation according to preliminary disease differentiation and syndrome types, and obtaining an expert diagnosis scheme for risk verification and prescription adjustment optimization. According to the invention, a multi-agent collaborative diagnosis technology is utilized, and a multi-agent architecture for simulating real traditional Chinese medicine consultation is constructed. A plurality of expert agents perform independent diagnosis and complement each other, so that the limitation of a single agent is avoided, and comprehensive and accurate diagnosis is provided. And in combination with expert experience knowledge graph and big language model reasoning, the system can flexibly differentiate symptoms according to the symptoms of the patient, accurately judge disease types and syndrome types, and improve the differentiation accuracy.

The invention discloses a multi-agent large-model disease diagnosis knowledge reasoning system based on data dual drive, and relates to the technical field of artificial intelligence assisted medical diagnosis. The system collects patient symptom follow-up records, laboratory test results, observation diagnosis probabilities and expert diagnosis recommendation results in a multi-source manner; time sequence evolution characteristics are extracted, a time sequence diagnosis sensitivity coefficient is calculated, and early recognition of disease risks is achieved; in combination with anti-fact simulation and statistical reasoning, a causal consistency coefficient is obtained and is used for verifying causal reasonability of observation diagnosis and contrast results; based on agent group consensus analysis, calculating a game consistency coefficient for judging the credibility of a diagnosis conclusion; positioning and multi-level verification are carried out on abnormal reasoning steps and knowledge fragments, so that the reliability and safety of a result are guaranteed; continuous optimization of the diagnosis model is realized through a log analysis and knowledge backflow mechanism; according to the invention, the accuracy, interpretability and safety of disease diagnosis can be obviously improved.

The invention provides a medical agent middleware-based full-specialized collaborative diagnosis and treatment method and system, and relates to the technical field of medical agents, and the method comprises the steps: building a basic medical knowledge graph through reinforcement learning, converting expert diagnosis and treatment experience into diagnosis and treatment probability distribution through a personalized treatment decision model, and forming an enhanced medical knowledge graph; and calculating a matching probability with a disease based on the symptom feature vector of the patient to generate a preliminary diagnosis decision, and selecting and adjusting a treatment scheme through reinforcement learning. According to the invention, general and specialized collaborative diagnosis and treatment can be realized, and medical decision accuracy and diagnosis and treatment efficiency are improved.

The invention relates to the field of traditional Chinese medicine clinical auxiliary diagnosis, and discloses an acupuncture point recommendation method, an acupuncture point recommendation model obtaining method, an acupuncture point recommendation model obtaining device, acupuncture point recommendation equipment and a medium, and the acupuncture point recommendation model obtaining method comprises the following steps: collecting patient medical record data to form a medical record data sample set; preprocessing the medical record data sample set to obtain a standard medical record data sample set; inputting the standard medical record data sample set into a preset language model, performing preliminary training on the language model based on the standard medical record data sample set, so that the language model establishes a semantic mapping relationship between the patient symptom information and the acupuncture points, and performing parameter fine tuning and strategy optimization on the preliminarily trained language model in sequence, obtaining an acupuncture point recommendation model; according to the acupuncture point recommendation model obtaining method, the accuracy and individual adaptability of acupuncture clinical treatment are improved, the diagnosis and treatment efficiency is optimized, and a scientific and systematic treatment scheme is provided for a patient.

The invention discloses a multi-source confidence perception insomnia intelligent prescription recommendation method, a medium and equipment, and aims to solve the problem of low recommendation accuracy caused by insufficient multi-source heterogeneous data fusion, single feature representation and missing prediction confidence in the prior art. The method comprises the following steps: coding patient symptoms into a sparse binary matrix; extracting a symptom-traditional Chinese medicine prescription matrix and a symptom-symptom type matrix from the traditional Chinese medicine knowledge map; generating symptom-guided traditional Chinese medicine features and symptom type features through a double-layer graph convolutional neural network; calculating the prediction distribution probability of the traditional Chinese medicine prescriptions and syndromes; based on an energy function, quantifying uncertainty scores of the two; the multi-source probability is dynamically weighted and fused to improve the weight of a high-confidence result; and finally, outputting recommended prescriptions and syndrome type sequences. The accuracy and interpretability of insomnia prescription recommendation are remarkably improved through isomeric map modeling and multi-source confidence fusion, and the method is suitable for a traditional Chinese medicine clinical auxiliary decision making system.

The invention relates to the technical field of medical information, in particular to a tumor patient adverse drug reaction intelligent follow-up visit system based on an internet hospital, which comprises a patient file module for acquiring medical data, identifying bad data and recording the bad data in a medical record file to form an abnormal symptom report; the intelligent analysis module is used for comprehensively analyzing the abnormal symptom report by utilizing a trained machine learning model and outputting analysis data; according to the system and the method, continuous monitoring and active early warning of extrahospital adverse reactions of tumor patients are realized, the timeliness of finding the adverse reactions is remarkably improved, intervention measures can be taken before symptoms of the patients deteriorate, and the treatment safety of the patients is improved. And secondly, through intelligent analysis and grading, the burden of manually screening the patient condition by medical staff is greatly reduced, the system automatically finds abnormity from massive follow-up visit data, and the efficiency and accuracy of follow-up visit management are improved.

The invention discloses a tear total IGE detection method and system based on multi-modal data fusion. The method comprises the following steps: acquiring ocular surface images, physiological parameters and environmental allergen data, and acquiring historical medical data from a hospital information system; preprocessing and feature extraction are performed, patient symptom description is analyzed in combination with a natural language processing technology, and related image block features are recognized; adjusting a space-time attention weight based on frequency domain analysis and transfer learning, inputting a lightweight Transform model, and predicting a tear total IgE concentration and an allergy risk score; whether a trace tear sampling program is started or not is judged, and the actually measured concentration of total tear IgE is detected through a disposable micro-fluidic chip; and integrating all information by using a Bayesian adaptive filtering algorithm to determine the total tear IgE level. By implementing the method provided by the invention, the sampling amount is extremely small, the detection time is short, the environmental interference can be dynamically corrected, and the absolute concentration value is output, so that the dual requirements of clinical precise diagnosis and treatment and home monitoring are met.

The invention relates to the technical field of diagnosis and treatment, in particular to a traditional Chinese and western medicine combined intelligent decision diagnosis and treatment method, system and equipment. According to the method, firstly, the symptoms of a patient are coded and converted into feature vectors, then vector matching is carried out in a pre-constructed traditional Chinese and western medicine combined knowledge graph, applicable diagnosis and treatment information is screened out by calculating the similarity and setting a threshold value, and a preliminary scheme is formed; reading individual information of the patient, and analyzing in combination with the medication taboo rule base to obtain personalized treatment parameters; and finally, performing optimization adjustment based on the initial scheme and the personalized parameters to generate a final diagnosis and treatment suggestion. Deep fusion of traditional Chinese and western medicine knowledge is realized, a diagnosis and treatment scheme can be dynamically adjusted according to individual features of a patient, and the accuracy and effectiveness of diagnosis and treatment are improved.

By providing a nursing care plan creation support system that enables even new nurses to easily create complete nursing care plans, searches for nursing care plans for similar cases from accumulated nursing care plan records, and enables automatic or semi-automatic creation of nursing care plans using additional learning in a large-scale language model, it becomes easier to create high-quality nursing care plans in a short amount of time. [Solution] The nursing care plan creation support system comprises a nursing diagnosis classification recording means, a nursing outcome classification recording means, a nursing intervention classification recording means, a nursing outcome classification feature vector recording means, a nursing intervention classification feature vector recording means, a nursing diagnosis item selecting means, a nursing outcome item selecting means, and a nursing intervention item selecting means. The nursing diagnosis classification feature vector recording means converts any portion of each classification record document into a feature vector and records it, and the nursing diagnosis item selecting means inputs the patient's symptoms and findings and a group of items of the content of each selected classification into a large-scale language model and selects items appropriate to the patient's symptoms and findings.

The invention discloses an allergic rhinitis diagnosis and allergen traceability system based on dynamic text guidance, and belongs to the technical field of intelligent medical treatment. According to the method, the problems that in the prior art, patient description is inaccurate, allergens are various in variety and have differences, and allergens are difficult to accurately determine and trace are solved, the initial situational sub-graph is verified by introducing periodic features of the patient, the causal contribution degree of the initial situational sub-graph is evaluated through a Bayesian reasoning algorithm, and a mode association result library is formed; according to the method, the crossing of the allergic rhinitis diagnosis and tracing from the traditional static judgment to the dynamic accurate inference is realized, and a personalized and scientific allergen avoidance scheme is provided for patients; through similarity retrieval, environmental information injection verification and calculation of a mode goodness-of-fit score, under the condition that patient symptoms are in multi-factor mixing, a mixed causal graph is created through a graph fusion technology, and the mode goodness-of-fit score is calculated again, so that the accuracy of obtaining an allergen traceability result in practical application is ensured.

The invention discloses a dementia intervention scheme recommendation method and system based on a knowledge graph, and relates to the technical field of intelligent medical treatment and artificial intelligence. And constructing a dementia intervention knowledge graph containing patients, symptoms, intervention measures and environmental elements. And performing intelligent semantic reasoning on the short-term state, the long-term behavior mode and the nursing environment of the patient by fusing multi-level reasoning and situational reasoning, generating a candidate intervention scheme matched with the current state of the patient, and outputting a personalized recommendation result. In the intervention implementation process, the knowledge graph association relation is dynamically updated based on patient behavior feedback and symptom changes, and continuous optimization and self-adaptive adjustment of an intervention scheme are achieved. According to the method, the individuation level, long-term adaptability and safety of dementia intervention recommendation are improved, and the method is suitable for clinical and long-term nursing scenes.

The invention discloses a liver, gall and pancreas patient symptom evaluation system based on multi-dimensional data, and relates to the technical field of liver, gall and pancreas symptom evaluation. The visual management platform is in communication connection with a data acquisition module, an abnormal symptom classification module, a symptom risk analysis module, a symptom risk classification module and a report generation module, and all the modules are in electric signal connection; the data acquisition module is used for collecting and preprocessing multi-dimensional index data of a liver function index, a biliary system index and a pancreas index of a patient. By integrating the multi-dimensional data of the liver function index, the biliary system index and the pancreas index, the health condition of the liver, gall and pancreas patient can be more comprehensively reflected, compared with a traditional single-dimensional evaluation method, the abnormal symptoms of the patient can be more accurately recognized, the possibility of misdiagnosis and missed diagnosis is reduced, and the diagnosis accuracy is improved. And personalized symptom risk assessment can be carried out according to specific data of each patient, so that the medical decision is more accurate.

This invention provides a method and system for predicting diabetes by integrating knowledge graphs and deep learning. The method constructs an entity vector library of physical examination data, and obtains vector representations of patient physical examination data based on this library, resulting in physical examination data vectors. It then constructs a diabetes domain knowledge graph; based on this knowledge graph, it extracts entities and relationships from patient symptom descriptions and represents them as vectors, obtaining symptom description vectors, and thus a matrix representing the patient's condition; finally, it integrates a self-attention mechanism and a convolutional neural network to construct a classification prediction model, which yields the prediction results. Compared with existing methods, this invention, by simultaneously considering patient physical examination data and symptom descriptions and based on a dual-driven approach of knowledge and data, achieves higher accuracy and interpretability.

The invention relates to the technical field of medical instruments, in particular to a multi-mode biological feedback ear vagus nerve self-adaptive stimulation device and method.The device comprises a host, a stimulation electrode, a signal collection bracelet and an electroencephalogram collection module, and the host comprises core hardware configuration and a power management system; the signal acquisition bracelet comprises an ECG module, a PPG module, a GSR module, a six-axis acceleration sensor and the like. The ear vagus nerve stimulation parameter can be dynamically adjusted through real-time analysis of multi-mode data such as electrocardio, electroencephalogram, heart rate variability, respiration-heart rate synchronism and emotional state, personalized precise treatment is achieved, meanwhile, the wearing comfort and the durability of the device are improved, and the application range is wide. The traditional Chinese medicine composition can be used for auxiliary treatment of various diseases such as inflammatory bowel disease, epilepsy, chronic insomnia, depression, chronic pain, anxiety disorder, post-traumatic stress disorder and autonomic nerve dysfunction, and the functions of a nervous system are adjusted and the symptoms of a patient are improved by accurately stimulating the vagus nerve in the auricular concha area.

The invention discloses a traditional Chinese medicine prescription diagnosis decision method and system, and relates to the technical field of decision diagnosis, and the method comprises the steps: collecting the symptom description data, geographic position data and seasonal climate data of a patient, carrying out the standardization processing of the symptom description data, and generating a symptom feature vector; performing time sequence analysis on the symptom feature vector by using a recurrent neural network, extracting a symptom evolution feature matrix, calculating an environmental influence weight based on geographic position data and seasonal climate data, and performing feature fusion on the symptom evolution feature matrix and an environmental influence weight vector to obtain a comprehensive feature vector; inputting the comprehensive feature vector into a pre-trained BERT model to obtain a semantic feature vector, and calculating a syndrome matching degree; and calculating the prescription probability according to the symptom evolution vector, the environmental influence weight and the syndrome matching degree, and outputting the probability distribution of each prescription to obtain a prescription decision result. The accuracy and objectivity of diagnosis decision are improved, and standardization and intelligence of the traditional Chinese medicine diagnosis and treatment process are further achieved.

The invention belongs to the technical field of outpatient service management, and discloses a generative AI-based outpatient service management method and system, and the method comprises the steps: obtaining the basic information, symptom description and historical medical record data of a patient when the patient sees a doctor through an AI outpatient service management system, and carrying out the semantic understanding and preliminary triage prediction of the symptom of the patient through a generative AI model, and generating possible disease types and corresponding department suggestions. According to the invention, semantic understanding and triage prediction are carried out on the symptoms of the patient through the generative AI, and intelligentization of the appointment process is realized. Patients do not need to manually select cumbersomely, and the system automatically generates department suggestions, so that the appointment time is greatly shortened, and the convenience and efficiency of appointment of the patients are improved. The constructed patient priority evaluation model and the department resource state model enable the calculation of the reservation time to be more scientific and reasonable, coordinate the time arrangement of the reservation and the preliminary diagnosis stage, effectively avoid reservation conflicts and long-time waiting, and improve the overall efficiency and accuracy of outpatient reservation.

The invention relates to the technical field of multi-source medical data integration, in particular to an integration method and system for integrating multi-source medical data sets. Comprising the following steps: S1, collecting multi-source medical data into an LIMS system, and associating the multi-source medical data according to a patient identifier; s2, extracting historical patient diagnosis symptoms according to the patient identifier to establish a patient symptom model, inputting associated medical data after association according to the historical patient diagnosis symptoms, and outputting a symptom related report through the patient symptom model; the medical knowledge graph is extracted from the multi-source medical data, accuracy analysis is carried out on disease related reports, correction is carried out according to different types of disease related reports, the accuracy of diagnosis reports is greatly improved, then the corrected reports are integrated into a disease diagnosis model, training and optimization are carried out through a machine learning algorithm, and the accuracy of the diagnosis reports is improved. The model can learn a large amount of medical knowledge and diagnosis modes.

The invention relates to the technical field of tumor treatment, in particular to a tumor patient symptom group collaborative management system, comprising a data acquisition module used for acquiring patient information and environment information through an intelligent device; the feature extraction module is used for data acquisition of the data acquisition module, and the feature extraction module performs feature extraction on the patient information and the environment information to obtain time domain features, frequency domain features and nonlinear features corresponding to symptoms; the evaluation platform identifies the core symptom group of the patient according to the time domain feature, the frequency domain feature and the nonlinear feature and calculates a total load index; the evaluation platform also performs symptom deterioration early warning; the collaboration platform is used for storing a disease knowledge base and identifying and obtaining a corresponding multi-mode intervention scheme according to the condition of a patient; and the patient terminal is used for pushing the multi-mode intervention scheme to the patient. According to the invention, the symptom group of the tumor patient can be managed as a whole, a personalized and real-time intervention scheme is provided for the patient, and the treatment effect is improved.

The invention relates to the technical field of esophageal cancer treatment, in particular to an esophageal cancer radiotherapy patient symptom evaluation scale development and verification method, which comprises the following steps: constructing an initial entry pool; entries are screened to form a preliminary scale; performing preliminary simulation evaluation on the scale; cognizing and explaining scale entries and giving feedback; carrying out characteristic verification based on psychology; performing association degree verification based on the items and the core symptoms; and scale determination and application are realized based on a verification result. According to the esophageal cancer radiotherapy patient symptom assessment scale and the development and verification method thereof, the development process of the assessment scale is ensured through multi-dimensional entry pool construction, multi-round Delphi method screening and a system verification mechanism, and entries with low association degree with core symptoms are systematically eliminated, so that each entry is ensured to have clinical significance and reference value, and the development and verification of the esophageal cancer radiotherapy patient symptom assessment scale is facilitated. The entry design is optimized through the patient interviews and the cognitive interviews, so that the scale is ensured to fit the real feelings of the patient, and evaluation deviation caused by obscure terms or irrelevant entries is avoided.

The invention discloses multi-mode medical demand interactive retrieval terminal equipment, belongs to the technical field of medical equipment, and solves the problems that an existing device only sprays disinfectant to disinfect equipment, dust, fingerprints and other traces attached to the surface of a machine cannot be cleaned, and stains are likely to exist on the surface of the equipment. The cleaning assembly comprises a gear located in the sliding groove and engaged with the tooth block, the gear is fixedly installed at the output end of a micro motor, a connecting rod is fixedly installed on the side face of the micro motor, a sweeping plate is fixedly installed on the cambered surface of the connecting rod, and cleaning cloth is fixedly installed on the side, away from the connecting rod, of the sweeping plate. A treatment scheme can be provided according to patient symptom analysis, medical resources are effectively saved, the diagnosis and treatment efficiency and the treatment success rate are improved, non-emergency patients can be left in primary hospitals for graded diagnosis and treatment, and national medical policies are met.

The invention discloses movable traditional Chinese medicine inquiry equipment, relates to the technical field of inquiry machines, and provides the following scheme that the movable traditional Chinese medicine inquiry equipment comprises a base assembly, a movable base is embedded in the base assembly, and an inquiry panel assembly is arranged on the top surface of the base assembly; the base assembly comprises a bottom supporting plate, four supporting legs fixed to the four corners of the upper surface of the bottom supporting plate, and a platform fixed to the surfaces of the tops of the supporting legs. The traditional Chinese medicine inquiry equipment helps a clinician to carry out preliminary inquiry according to symptoms and signs of a patient, and is combined with physical characteristics provided by the patient and treatment based on syndrome differentiation of the traditional Chinese medicine theory, so that prescription selection and medication are more accurate, disease recovery of the patient is facilitated, and the influence of neglect of natural climate on human bodies due to clinical medication is avoided; and meanwhile, brain power and calculation time are saved, and waiting time of the patient is shortened.

The present invention discloses a method and device for obtaining patient symptoms, a storage medium, and a computer device, which belong to the field of digital medical technology. The method mainly aims to solve the problem in the prior art that the patient symptoms extracted from the doctor-side dialogue content generated based on the trained language model are quite different from the patient symptoms that the doctor actually wants to know. The method comprises performing entity extraction processing on the current patient-side speech with the intention of asking a diagnosis to obtain at least one patient symptom entity corresponding to the current patient-side speech; performing a query in a pre-configured traditional Chinese medicine symptom knowledge graph based on the patient symptom entity to determine the main symptoms of the target patient; determining multiple relationship entities related to the target patient's main symptoms based on the traditional Chinese medicine symptom knowledge graph, and determining the sub-symptoms to be asked based on the priorities of the multiple relationship entities; generating a doctor-side speech based on the sub-symptom to be asked, so that the patient side can give a symptom reply based on the doctor-side speech.

This information assistance server comprises: an aggregation processing unit that aggregates the time required to treat a patient for each symptom, on the basis of medical treatment information indicating the content of medical treatment performed at a medical institution; and a prediction processing unit that, if an inquiry is received about whether emergency transportation can be accepted, predicts the time available for the emergency transportation to the medical institution and the number of patients who can be admitted to the medical institution, such prediction being made on the basis of the results of the aggregation by the aggregation processing unit, the circumstances the medical workers are in at the medical institution, and the symptoms of a patient subject to the emergency transportation.

The application relates to the technical field of diagnosis and treatment, in particular to an intelligent decision-making diagnosis and treatment method, system and equipment combining traditional Chinese medicine and western medicine. First, the symptoms of a patient are encoded into a feature vector, then vector matching is performed in a pre-constructed knowledge graph combining traditional Chinese medicine and western medicine, applicable diagnosis and treatment information is screened out by calculating the similarity and setting a threshold value, and a preliminary scheme is formed; then the individual information of the patient is read, and analysis is performed in combination with a medication contraindication rule library, so that personalized treatment parameters are obtained; finally, the preliminary scheme and the personalized parameters are optimized and adjusted, and the final diagnosis and treatment suggestion is generated; not only the deep integration of traditional Chinese medicine and western medicine knowledge is realized, but also the diagnosis and treatment scheme can be dynamically adjusted according to the individual characteristics of the patient, and the accuracy and effectiveness of the diagnosis and treatment are improved.

The invention provides a patient risk dynamic assessment method and device, relates to the field of medical health, and solves the technical problems of high assessment result subjectivity and low assessment efficiency caused by manual assessment when a critical patient is assessed in the prior art. The method comprises the steps that clinical index information of a patient is obtained, the clinical index information comprises subjective symptom information and objective index information, and the subjective symptom information comprises subjective evaluation information of a doctor on patient symptoms, subjective self-description information of the patient and / or patient treatment information; the objective index information comprises index information obtained by detecting multiple physiological indexes of the patient; the clinical index information is input into the four-level risk dynamic model, and the risk level of the patient is determined; the output result of the fourth-level risk dynamic model comprises a first-level risk, a second-level risk, a third-level risk or a fourth-level risk; different levels of risks correspond to different rehabilitation treatment schemes. The method is used in a patient risk dynamic assessment process.

This invention discloses a medical intelligent decision-making method based on DeepSeek and temporal causal knowledge graphs, comprising the following steps: Step 1: Constructing an initial static medical knowledge graph and generating a dynamic temporal causal knowledge graph; Step 2: Fine-tuning the DeepSeek model to adapt it to the medical field; Step 3: Receiving and parsing text uploaded by patients, performing intelligent triage and disease risk prediction, achieving accurate matching of patient symptoms with target departments, and intelligent prediction and early warning of potential diseases. The method of this invention aims to provide patients with accurate triage and disease risk prediction, construct a scientific and efficient medical intelligent decision-making mechanism, and optimize the allocation of medical resources.

To provide a AI inquiry device, a AI inquiry method, and a program capable of reducing a burden on a patient during AI inquiry.SOLUTION: The AI inquiry device includes an image inputting part, a disease candidate estimating part, and a disease estimating part. The image input unit receives an input of a face image of a patient. The disease candidate estimation unit extracts symptoms of the patient from the face image of the patient to estimate disease candidates of the patient based on the symptoms of the patient by using the image recognizing AI model. The disease estimation unit uses the interactive AI model to output a question to the subject, receive an input of a response of the subject to the question, and estimate a disease of the subject from candidates for the disease of the subject based on the response of the subject.SELECTED DRAWING: Figure 2

The invention belongs to the technical field of smart medical treatment, and discloses a patient data acquisition method and system based on a mental disorder questionnaire and a mapping manual, and the acquisition method comprises the steps: obtaining individualized information, and constructing a personal graph in combination with a constructed mental disorder knowledge graph; according to principal component analysis, constructing a mental disorder assessment questionnaire for assessing symptom dimensions and a corresponding symptom dimension mapping manual; outputting a personalized questionnaire according to the personal map, the mental disorder assessment questionnaire and the symptom dimension mapping manual in combination with machine learning; and obtaining a questionnaire result, and obtaining an evaluation result according to the questionnaire result in combination with the conversion rule, thereby solving the problem that the existing mental questionnaire problem is not standard and cannot effectively interpret the symptoms of the patient.