425 results about "Physical examination" patented technology

The invention discloses a fireman occupational health risk dynamic prediction method based on multi-modal data fusion, relates to the technical field of occupational health risk assessment, and aims to solve the problems of information loss, difficulty in capturing cross-modal complex dependence, lack of interpretability of prediction results and the like when unstructured data is processed in the prior art. The method comprises the following steps: intelligently analyzing a multi-modal document, and analyzing unstructured physical examination information into structured data; performing semantic standardization and knowledge graph dual verification of domain knowledge enhancement; the time-space-static cross-modal attention deep fusion network is used for modeling physiological, dynamic and situational data; and carrying out interpretable risk prediction and attribution. By adopting the technical scheme, the method can realize early, accurate, dynamic and high-credibility prediction of the occupational health risk of the firefighter, provides quantitative attribution, and improves the transparency and application value of the system.

The invention relates to the technical field of report optimization, in particular to a user medical history-based physical examination report conclusion priority adjustment method, which comprises the following steps of: acquiring multi-source data, and preprocessing the multi-source data to obtain a structured multi-source data set; based on the structured multi-source data set, mining an association relationship between health data and diseases through association analysis, constructing a health trend prediction model in combination with a deep Q network, and obtaining a multi-dimensional diagnosis analysis result; determining a diagnosis conclusion priority in combination with a multi-dimensional diagnosis analysis result and a priority calculation formula; calculating a comprehensive risk score through the data fusion score, and dividing risk levels according to the comprehensive risk score to match corresponding stage intervention strategies; and inputting a multi-dimensional diagnosis analysis result, intervention strategy execution feedback data and user health preference into a comprehensive diagnosis decision model, and obtaining a differentiated health association strategy in combination with a diagnosis conclusion priority. According to the scheme, the severity of the disease is accurately identified by dynamically adjusting the priority.

The invention relates to the technical field of medical health monitoring, and particularly discloses a chronic disease patient physical examination data and complication nursing monitoring system based on the Internet of Things, multi-dimensional physical examination data such as vital signs and blood biochemical indexes of a patient are collected in real time through a multi-source sensing equipment network, and an individualized data stream is constructed; extracting parameter collaborative change characteristics by adopting multi-time scale analysis, and constructing a dynamic physiological characteristic spectrum; identifying the abnormal offset by calculating the deviation degree of the feature vector distribution and the reference distribution; based on intelligent matching of the spatial distribution features of the feature vectors and a clinical case library, generating structured early warning information including risk assessment and personalized nursing schemes; and finally, visual display and preventive nursing intervention of the complication risk evolution process are realized.

The invention discloses a clinical intelligent decision-making method based on proxy workflow and a storage medium. Comprising the following steps: acquiring and preprocessing clinical information of a patient, and constructing a candidate disease set; and constructing a proxy directed workflow. In the retrieval stage, the diagnosis criteria corresponding to the candidate diseases are retrieved and aggregated from the diagnosis criteria library rechecked by the experts to form working memory. The preliminary diagnosis stage model node generates a preliminary candidate diagnosis set in combination with work memory and patient medical history and physical examination. And the final diagnosis stage generates a final diagnosis result based on the preliminary candidate diagnosis and the complete clinical information. And when the global confidence is lower than a threshold value, the model node pointedly checks an information source and updates reasoning and confidence. A successful reasoning track forms a demonstration set after manual auditing, the demonstration set is used for supervising a fine tuning model to obtain an initial strategy, multiple structured outputs are generated through grouping sampling, relative strategy updating is carried out in combination with reward signals and reference strategy regularization constraints, and optimization and stable improvement of the model diagnosis capability are achieved.

The invention provides a physical examination data exception association analysis method and system based on a knowledge graph, and relates to the technical field of exception association analysis, and the method comprises the steps: determining all physical examination items of a target physical examination center; aiming at a plurality of selectable physical examination indexes, executing abnormal generality association and generality confidence degree analysis among the indexes, and establishing an abnormal generality association map; receiving a physical examination report; executing abnormal generality association verification based on the abnormal generality association graph; performing abnormal distribution characteristic analysis in a preset period for the plurality of selectable physical examination indexes, and performing distribution abnormality verification on the physical examination report; and performing joint abnormity verification according to the distribution abnormity verification result and the generality verification result, then performing abnormal physical examination item positioning, and sending the abnormal physical examination item positioning to a target physical examination center for reminding. The technical problem that potential health risks are difficult to recognize in time and the accuracy of physical examination results is affected in the prior art can be solved, and the technical effect of improving the accuracy of physical examination anomaly detection is achieved.

According to the multi-mode speech recognition and structured electronic medical record generation system based on environmental perception, the dialogue content between a doctor and a physical examination person and the oral instruction / result of the doctor are accurately captured in a non-inductive mode and are intelligently filled into the predefined structured electronic medical record template of the corresponding department, so that the manual input time of the doctor is shortened, and the efficiency is improved. The system liberates both hands and eyes, focuses on inquiry and examination, improves the physical examination quality, and improves the satisfaction degree of physical examinees.

A mine earthquake adaptive positioning method and system based on multi-index fusion discrimination belong to the field of mine earthquake positioning, and the system comprises a data acquisition module, a feature extraction and quality evaluation module, a comprehensive index calculation module and an adaptive decision module. The method comprises the following steps: firstly, carrying out multi-dimensional physical examination on input waveform data, station layout and a speed model by the system, and generating a quantized physical examination report comprising a signal-to-noise ratio, a pickup error, station geometric coverage, model credibility and the like, namely a multi-dimensional feature vector; and then, a built-in rule engine automatically screens out an optimal single method or combined strategy under the current condition from multiple positioning methods according to the report and a demand target set by a user, and outputs a decision result with a clear basis. Through the system and the method, full-process automation from data input to strategy output is realized, and efficient and reliable technical support is provided for mine safety monitoring and disaster early warning.

The invention discloses a health early warning management system and method based on physical examination information, and particularly relates to the field of skeleton structure recognition, and the method comprises the steps: constructing a structural posture imaging data set based on a physical examination posture image of a student, extracting three-dimensional structure coordinates of parts, such as a scapula, a spine and a pelvis; and uniformly mapping to a skeleton reference system to calculate a body inclination index, a spine offset rate and a pelvic inclination index. Then, constructing a time sequence of posture structure parameters, and generating a trend feature vector sequence according to academic difference and gradient extraction; and further constructing a posture structure evolution trajectory diagram by taking the structure parameters as nodes and the trend vectors as edge attributes, and capturing a cross-year posture change path of the students. On the basis, all paths are aligned according to ages, the sudden change trend is extracted, the posture deviation high-incidence risk of a specific age group is recognized, and accurate student posture early warning management is achieved.

The invention discloses a health analysis report generation method based on a large model. The report generation method comprises the following steps: inputting a physical examination report; preprocessing the physical examination report to obtain a preprocessing result; performing visual language model analysis according to the preprocessing result; carrying out structured processing; compressing the cue word; generating an HTML (Hypertext Markup Language): generating webpage contents by utilizing And rendering and exporting. By introducing a large model with natural language understanding, data reasoning and code generation capabilities, an intelligent report generation mechanism is constructed, while the system development workload is reduced and the development period is shortened, specialized, precise and personalized output of health reports is realized, and the scientificity, readability and practical value of report contents are improved.

The invention relates to the technical field of medical data processing, and discloses a disease risk assessment method based on a disease risk knowledge graph, the disease risk knowledge graph comprises a disease risk rule and a disease knowledge entity, and the disease knowledge entity comprises a focus position entity; performing organ positioning according to the physical examination data of the target object, and determining a target organ; performing organ matching and index data matching in a disease risk rule based on the target organ and the physical examination data to obtain a risk matching rule corresponding to the target organ; performing disease risk level analysis on the target organ according to the risk matching rule to obtain a disease risk level of the target object about the target organ; generating a physical examination report of the target object according to the physical examination data, the disease risk level and the target focus position; wherein the physical examination report comprises a three-dimensional model used for displaying the target focus position. The method has the beneficial effects that the pertinence and efficiency of disease risk assessment are improved, and the target object is helped to understand the health state of the target object.

The invention relates to the technical field of data analysis, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a human body state characteristic value analysis method, device and equipment and a medium. Performing system state analysis on an endocrine system, a circulatory system and a respiratory system of the user according to the physical examination data to obtain a system state characteristic value set, constructing a physiological index vector and a disease course characteristic vector according to the physical examination data, and constructing a complication incidence matrix according to case data; performing complication association analysis on the user according to the physiological index vector, the disease course feature vector and the complication association matrix to obtain a complication feature value, analyzing a criticality feature value of the user according to a system state feature value set and the complication feature value to obtain a hazard feature value set, and performing weighted summation on the hazard feature value set to obtain a complication feature value set; and obtaining a human body state characteristic value. And the accuracy of human body state characteristic value analysis is improved.

The invention discloses a personalized physical examination period dynamic estimation system and method based on big data, and relates to the technical field of medical health data processing and intelligent evaluation. The estimation method comprises the step of constructing an integrated implementation framework including a multi-source data integration link, a multi-party cooperative processing link, a three-step modeling evaluation link and a quantitative feedback adjustment link. According to the personalized physical examination cycle dynamic estimation system and method based on big data, a medical institution, wearable equipment, family genetic disease history and government affair medical treatment are integrated through a cross-domain data fusion platform, and a multi-dimensional evaluation basis covering physiology, behavior, heredity and resource collaboration is constructed; and then through a three-step modeling process of a hybrid model training engine, the problem of insufficient data is solved through similar group data adaptation, nonlinear correlation features are mined through multiple decision trees, a risk level is finally output, and a period is dynamically adjusted in combination with medical resource assessment.

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 discloses a standard information physical examination table intelligent verification method and system based on data automation, and the method comprises the steps: generating standard candidate data of a target field through a data collection step and data standardization processing, and carrying out the screening, and obtaining a standard data set; determining the dependency relationship between the fields of the physical examination table, mapping the standardized data values to the corresponding positions of the fields of the physical examination table through a field mapping function, and generating the physical examination table through data verification and automatic correction; analyzing the relationship between the fields of the physical examination table to predict and generate a filling suggestion value, and generating a filling suggestion list through regularization item correction and verification; inputting the physical examination table data input and modification of the doctor, and generating a final physical examination table draft; and performing consistency verification and medical compliance verification on the final physical examination table draft to generate a formal physical examination table. Through data integration, structured mapping and context reasoning filling, the filling efficiency, data integrity and medical compliance of the physical examination table are improved, and the workload of doctors is relieved.

Provided is an intelligent grading method and system for pulmonary nodules based on multi-modal feature fusion, including: obtaining ROI and VOI of pulmonary nodules based on chest CT examination images and examination reports by utilizing clinical multi-modal data from physical examination population, designing a multi-task feature extraction network based on attention mechanism, to obtain radiomics features and deep image features from the ROI and VOI; designing a cross-modal feature fusion method based on graph representation learning, designing a multi-modal information extraction method, obtaining specific feature representations and graph structures of modalities, and then fusing the feature representations and the graph structures; and proposing an optimization and clinical verification method of pulmonary nodule grading GCN model based on self-supervised learning, to realize fine grading of pulmonary nodule malignancy with slight differences, thereby providing a new approach to design of fine-grained classification algorithms.

The invention discloses a cognitive function screening system and method based on an MMSE (Minimum Mean Square Error) prediction model, and relates to the technical field of data analysis, the method comprises the following steps: collecting physical examination index data, removing missing records, adopting a multiple interpolation method for interpolation, and obtaining multiple sets of complete data sets; a continuous prediction model is constructed, MMSE continuous prediction values are obtained, and a sensitivity analysis report is generated; constructing a first-stage classification model, adaptively dividing an optimal threshold combination, and dividing a sample into a high-confidence region, a to-be-discriminated region and a low-confidence region; if the sample size of the to-be-discriminated region is higher than a preset training threshold value, constructing an enhanced feature set, and constructing a second-stage classification model; when the prediction probability reaches the optimal re-discrimination threshold value, the classification result in the first stage is corrected, and otherwise, the classification result is maintained; if not, maintaining the classification result; and integrating the classification results to obtain a final classification result of all the samples.

The invention discloses a physical examination suggestion intelligent adaptation and recommendation system based on a personalized health portrait. The method aims to solve the problems of static portraits, general suggestions, lack of continuous tracking, insufficient safety and the like in existing health management services. Through real-time acquisition and fusion of multi-source heterogeneous data, the incremental learning algorithm is constructed and adopted to dynamically update the health portrait of the user, and the timeliness of the portrait is ensured. Based on the dynamic portrait, the system carries out deep traceability and quantitative attribution on abnormal indexes through a multi-path reasoning decision tree, and carries out comprehensive health risk assessment. In a recommendation stage, the system comprehensively considers a user portrait, a risk assessment result, cost and feasibility preference, and performs strict medical suggestion conflict resolution by using a medical knowledge graph based on an OWL ontology and an SWRL rule, so that a highly personalized, safe and feasible physical examination suggestion and health management scheme is generated.

The invention discloses a fusion mode health physical examination psychological test system, which belongs to the technical field of psychological test and comprises a physical examination data acquisition module, a psychological test module, a multi-modal data fusion module, a result analysis module and a user interaction module. The physical examination data acquisition module is used for acquiring physiological health data of a user, including physical sign data, laboratory examination data and image examination data; the psychological test module provides a plurality of standardized psychological assessment scales; the multi-modal data fusion module is used for performing feature alignment and information fusion on the physiological health data acquired by the physical examination data acquisition module and the psychological test original data generated by the psychological test module, performing fusion analysis on the physiological health data and the psychological test data through the multi-modal data fusion module, and mining the relevance between the physiological health data and the psychological test data; the problems that traditional physical examination and psychological testing are independent and evaluation is not comprehensive are solved, and more complete health condition cognition is provided for the user.

The invention provides a personalized physical examination report generation method and system based on separate examination, and relates to the technical field of digital processing, and the method comprises the steps: building a general digital expression model of a corresponding physical examination item according to the physical examination item of a corresponding department and based on a digital expression technology; based on the physical examination data of the physical examination items matched with different patients, constructing an AI feature auditing algorithm; associating the AI feature auditing algorithm with a corresponding general digital expression model; according to the AI feature auditing algorithm, performing intelligent diagnosis on the physical examination data of different physical examination items of the patient to obtain corresponding physical examination diagnosis features; importing the physical examination diagnosis features into a general digital expression model to obtain an identified virtual diagnosis model; according to the method, the virtual diagnosis models of different physical examination items of the same patient are obtained, all the virtual diagnosis models are subjected to set analysis, then the personalized physical examination report of the corresponding patient is output, and the experience feeling of medical staff and the patient is improved.

Systems and methods for automated healthcare services include the use of medical facilitators. A purpose of the facilitators is to serve as a human link between patients and electronic medical applications which utilize medical AI. In some embodiments, medical facilitators assist patients with history and physical examination following the guide of an automated and at least partially AI-based medical computing system or application. The medical facilitator may be trained to use the electronic application and to have sufficient basic medical knowledge to follow directives generated by the application, but he or she is not required to have the medical knowledge of a physician. Medical facilitators can provide the history and physical tasks at the same time, and the physician(s) need only to review and approve the results.

The invention discloses an intelligent oral health management system, and relates to the technical field of oral health of old people. The system comprises a central processing module, the output end and the input end of the central processing module are both electrically connected with an AI non-contact inspection module, and the output end and the input end of the central processing module are both electrically connected with a digital physical examination report module. By means of the AI non-contact examination module, efficient, accurate and safe oral examination is achieved, the limitation of a traditional examination mode is effectively solved, the digital physical examination report module presents oral health data in a visual and multi-dimensional mode, the health condition is clear at a glance, a clear basis is provided for subsequent diagnosis and treatment and management, and the examination efficiency is improved. The grading diagnosis and treatment engine module and the four-stage prevention system module are matched with each other, and a set of scientific and complete oral health management process is formed from risk assessment to prevention and intervention to diagnosis and treatment arrangement and rehabilitation.

The invention discloses a health management scheme generation method and system based on deep learning. The method comprises the steps of obtaining a user physical examination report and portrait information; extracting structured and unstructured data in the report through preprocessing and an OCR (Optical Character Recognition) technology; encoding the extracted data into a vector by using a pre-training language model, and obtaining associated medical background information from a medical knowledge base through vector retrieval; in combination with the user portrait, the original data and the retrieval information, analysis is performed based on a pre-trained large language model, key abnormal indexes are identified, abnormal levels are judged, and associated risks are analyzed; and generating a personalized health management scheme containing a specific quantitative execution standard, and carrying out visual display on the personalized health management scheme. The system comprises corresponding function modules. According to the method and the system, end-to-end automatic generation from a non-standardized physical examination report to a personalized and executable health management scheme is realized, and report analysis precision, health risk understanding depth and operability and practicability of the scheme are remarkably improved.

The application discloses a kind of high-precision online weight detection equipment with self-checking function, including detection assembly, mutual detection component, weighing top seat, mounting plate, second balance cylinder, first push material cylinder, first push material plate, first material baffle, second material guide plate, second level, self-checking component, third support, third conveyor belt, third motor, fifth fixed plate, second push material cylinder, second push material plate, self-checking block, second material baffle, third material guide plate;The application is designed with self-checking function, and the preset self-checking block is automatically pushed by self-checking component regularly to carry out detection operation, this process is not only a calibration to the weighing accuracy of detection equipment, but also a comprehensive physical examination to the overall operation state of equipment, this self-checking mechanism ensures that equipment maintains high-precision and high-efficiency working state, effectively avoids the problem of inaccurate weighing caused by equipment failure or error accumulation, thereby greatly improving the quality control level of production line.

The invention provides a data index intelligent physical examination system and method, and the system comprises a data obtaining module which obtains index related data in a plurality of data sources, and the index related data comprises index basic data and external event data; the index prediction module is used for obtaining index prediction data and a confidence interval in future preset time according to the index basic data and the external event data; the anomaly detection module is used for determining a first anomaly judgment result based on the index prediction data and the confidence interval, and determining a second anomaly judgment result based on an unsupervised anomaly detection algorithm and the index prediction data; the attribution analysis module is used for positioning the exception and determining an exception reason list when the first exception judgment result and / or the second exception judgment result are / is that the exception exists; and the report generation module generates a physical examination report based on the abnormal reason list. According to the invention, accurate anomaly detection of data indexes is realized, and more accurate data-driven operation and decision are provided for enterprises.

The invention discloses a humanoid robot state monitoring method and device. The method comprises the following steps: acquiring multi-modal information data of a humanoid robot; determining physical examination result information according to the multi-modal information data, wherein the physical examination result information is used for representing the anthropomorphic health state of the humanoid robot; when a state inquiry instruction is detected, a state updating strategy is determined according to the physical examination result information and the state inquiry instruction, and the state updating strategy comprises interaction confirmation operation, state updating operation and health maintenance operation; and executing the state updating strategy to update the current running state of the humanoid robot. Therefore, the physical examination result information representing the anthropomorphic health state of the humanoid robot is determined according to the multi-modal information data, so that when the state inquiry instruction is detected, the state updating strategy is determined according to the physical examination result information to perform interaction with higher anthropomorphic degree and more intelligent updating and maintenance; therefore, the state monitoring of the humanoid robot is more intelligent, and the user experience is improved.

1. The name of the design product: physical examination all-in-one machine. 2. The use of the design product: for measuring and managing blood pressure, heart rate, body temperature, blood oxygen, exhaled gas alcohol concentration, etc., and printing measurement results. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The utility model belongs to the technical field of blood sampling devices, and particularly relates to a physical examination nursing blood sampling device which comprises a first connecting part and a blood sampling container which are movably connected in an inserted mode through a third connecting part. The end part of the first connecting part is provided with an insertion end, the other end of the first connecting part is provided with a blood sampling input end, and the blood sampling input end is communicated with the interior of the first connecting part; a second connecting part is arranged at the top end of the blood sampling container, the third connecting part is in threaded connection with the second connecting part, the first connecting part and the third connecting part are movably connected in an inserted mode and are connected to the end of the second connecting part in a penetrating and inserted mode, a sealing part is arranged in the second connecting part, and the outer side of the sealing part is sleeved with an elastic covering film. And the insertion end is vertically inserted into the surface of the sealing part through threads of the third connecting part and penetrates through the sealing part and the elastic covering film. According to the utility model, the structure of the blood collection tube can be simplified, and the use requirements of sanitation and convenience in operation are met.

The present application relates to a kind of dynamic health risk assessment methods based on multi-source heterogeneous data, belong to data processing field.The method is applied to physical examination system, and the method comprises: physical examination system obtains the portrait data of user and M candidate physical examination schemes selected by user, any two candidate physical examination schemes in M candidate physical examination schemes contain different examination items, and M is integer greater than 1;Physical examination system is based on the portrait data of user, deduces the multi-source heterogeneous data of each candidate physical examination scheme in M candidate physical examination schemes adopted by user, and a total of M multi-source heterogeneous data is obtained.

The application discloses a drug recommendation method and system based on drug mapping and diagnosis enhancement, the natural language processing technical field and the recommendation system field, aiming at the problems of implicit drug and disease association, single diagnosis information and poor interpretability in the existing drug recommendation technology, and realizes accurate recommendation through four core modules: a diagnosis enhancement module utilizes a large language model to associate diagnosis and treatment methods, physical examination, and generates enhanced diagnosis information; a drug mapping module establishes an explicit association of drugs, diagnosis and treatment methods, a drug recommender module combines a cross-attention mechanism and a graph neural network, and respectively models enhanced diagnosis representation and drug external knowledge; and a joint training module balances single-disease and global recommendation loss to optimize model performance. The application is superior to existing mainstream models in various indicators, can significantly improve the accuracy, clinical interpretability and safety of drug recommendation, and is suitable for assisting doctors in formulating personalized medication schemes.

The invention discloses a GIS (Geographic Information System) visualization and intelligent auxiliary management method based on chronic disease patients. The method comprises the following steps: (1) carrying out GIS (Geographic Information System) visualization on chronic disease patient distribution, and generating a disease aggregation degree thermodynamic diagram; (2) a hybrid screening engine which combines a GIS interaction tool with a medical health label and displays data of different granularities; (3) binding the medical health intelligent robot with a verbal skill template, presetting a trigger condition, and generating a personalized reply; and (4) after the medical health intelligent robot or model completes health consultation on the patient, automatically extracting data related to health, physical examination and biochemical indexes, updating the data to a health file GIS visual platform of a corresponding user, reading the latest data update, and displaying dynamic monitoring of the chronic disease patient in the jurisdiction in real time. The beneficial effects of the invention are that the system supports the intelligent management service of multi-round dialogues and precise medical health for specific crowds in a target area, and achieves data continuity, precise service and efficient management.