111 results about "Electronic medical record" patented technology

The application discloses an electronic medical record and a cataloging and classifying method, system and device thereof. The method comprises the following steps: obtaining standardized data by preprocessing multi-format medical record original data through a medical OCR model, a VAE anomaly detection algorithm, a medical knowledge graph word segmentation tool and a Transformer term standardization model; checking semantic rationality, extracting multi-dimensional features, and obtaining a comprehensive feature vector after strengthening and fusing; constructing a transfer learning classification model and training the model, inputting the feature vector to generate cataloging information; and finally, evaluating the classification result through an active learning mechanism, combining artificial labeling data and a causal forest dynamic updating framework, and incrementally learning and optimizing the model performance. The application solves the problems of low multi-format data extraction accuracy, insufficient term standardization and poor model generalization in the prior art.

A medical device includes one or more modules configurable to connect to one or more sensors for capturing one or more physiological variable measurements and a display screen for displaying the physiological variable measurements. The medical device receives data identifying a patient, retrieves an electronic medical record of the patient acquired from a system responsible for maintaining the electronic medical record, and stores the electronic medical record on the at least one memory device. The medical device determines an interruption of a connection with the system responsible for maintaining the electronic medical record. The medical device updates the electronic medical record to include the physiological variable measurements captured by the one or more sensors while the interruption persists.

This disclosure provides a method, apparatus, device, and storage medium for generating multimedia surgical records. The method includes: responding to a surgical procedure configuration command, matching a multimedia surgical record template; entering first surgical record information based on the multimedia surgical record template; responding to an image entry operation command, determining an image to be matched and generating image-related information based on the image to be matched to obtain second surgical record information; responding to a video entry operation command, generating video-related information to obtain third surgical record information; supplementing the third surgical record information based on the multimedia surgical record template to obtain fourth surgical record information, and uploading it to an electronic medical record. This achieves automatic template matching and text structuring, reduces multimedia insertion steps through intelligent parsing, supports rapid video editing within the system without the need for third-party tools, adds identifiers to all referenced resources for easy tracking and tracing, and improves record integrity and operational efficiency.

A computational pupillometry system comprises an imaging device configured to capture video frames of a subject's eye and processors executing instructions to perform advanced pupillary assessment. The system employs multi-frame integration techniques, including super-resolution algorithms that utilize sub-pixel shifts between frames, temporal averaging for noise reduction, and parallax-based artifact mitigation to enhance measurement accuracy. Artificial intelligence models, including temporal neural networks, analyze the enhanced pupillary data to determine pupillary parameters and calculate a light-invariant Pupil Reactivity (PuRe) score. The system processes ambient lighting conditions through computational models that analyze video frames before and after controlled stimulation, enabling consistent scoring across varying environmental conditions. Quality assurance mechanisms provide pre-recording and post-recording validation with real-time feedback. The system integrates with electronic medical records through standardized healthcare protocols and supports synchronized, multi-device deployment across healthcare networks.

This invention provides an information processing device, information processing method, information processing system, and program that enable centralized management of patient information by linking an electronic medical record database with databases from multiple customer acquisition media, and that utilize this information to propose medical services. [Solution] The information processing system includes a reception unit that receives patient needs, a patient information acquisition unit that acquires patient information including basic patient information and medical visit history information, a medical information acquisition unit that acquires medical information including medical treatment information and prescription details from medical institutions, an information centralized management unit that centrally manages patient information and medical information, a prompt generation unit that outputs generation commands based on the centrally managed patient information and medical information, and a proposal information generation unit that generates proposal information including medical services, medical institutions, treatment details, etc., suitable for the patient based on the generation commands.

The present invention relates to a system and method for recommending health functional foods to elderly patients with geriatric chronic diseases. According to the present invention, a method for recommending health functional foods, performed by a system for recommending health functional foods, comprises the steps of: receiving, from a target patient with a geriatric chronic disease, response data obtained from responses to a medical inquiry form or questionnaire; performing natural language processing on the response data; inputting the natural-language-processed response data into a first learning model to classify a mental disorder and a severity level of the target patient; receiving personal information and EMR data of the target patient; and inputting the personal information, EMR data , and information on the mental disorder and severity level of the target patient into a second learning model to recommend one or more health functional foods to the target patient.

This invention discloses an intelligent health education method, device, equipment, and storage medium for gynecological tumor patients. The method acquires the patient's electronic medical record information and patient interaction data. Based on the electronic medical record information, it identifies the gynecological tumor disease type and treatment stage tags. Based on the patient interaction data, it specifically identifies psychological stress signals related to gynecological tumor treatment, generating a profile of the patient's current psychosomatic state. It matches and dynamically generates personalized health education content and psychological counseling plans from a structured knowledge base. Based on the patient's treatment data, it automatically generates reminders for medication and follow-up examinations. It structurally correlates the output records of personalized health education content and the patient's feedback data on the reminders to form a digital pathmap for patient education. This enables dynamic matching and personalized generation of health education content and psychological counseling plans, improving the patient's cognitive adaptability to health information and the effectiveness of psychological support, achieving closed-loop management throughout the entire hospital and inpatient cycle.

The application belongs to the field of medical prediction, and discloses an information mining method for heterogeneous time series data, comprising: acquiring electronic medical record data and constructing a hypergraph, analyzing and calculating the hypergraph to obtain embedding representation data, weighting the embedding representation data based on an attention mechanism to obtain embedding sequence data, constructing a sequence learning model and performing hidden state access to obtain hidden representation data and weight data thereof, weighting the embedding sequence data to obtain embedding sequence hidden data; training the sequence learning model through time training parameter data, weighting the embedding sequence hidden data through the trained sequence learning model to obtain time dimension hidden data, constructing a full connection network to analyze the time dimension hidden data to obtain medical event prediction data. The technical scheme disclosed by the application can learn complex information in the time dimension by using time step information, and can obtain accurate medical event prediction results.

This invention belongs to the field of acute lymphoblastic leukemia (ALL) diagnosis and treatment technology, and discloses an ALL scoring system. The feature library construction module establishes an associated feature system based on subtype molecular biological differences, uses LASSO regression and subtype stratified analysis to screen core features and determine weights, and excludes redundant cross-subtype information. The feature mining module targets the entire treatment cycle, dividing the collection nodes into induction, consolidation, and maintenance phases, capturing temporal correlation features such as the rate of MRD decline and gene expression change rate, breaking the limitation of relying solely on static data at the time of diagnosis. It not only fits the individual characteristics of patients with different subtypes, but also reflects the dynamic changes in the treatment process, improving the accuracy of prognostic assessment. A real-time corrected score is obtained by calculating the baseline score and the temporal feature adjustment score. Patient data is updated weekly through the electronic medical record interface, and appropriate solutions are output for different situations such as low-risk, intermediate-risk, and very high-risk.

The application discloses a kind of fusion multi-model intelligent reasoning's cause of death chain automatic generation and radical cause of death determination support method and equipment.The method comprises: obtaining and preprocessing electronic medical record data;Adopt four-stage series screening mechanism including semantic matching, context adaptation, timing verification and statistical optimization, map clinical text into candidate ICD coding;Adopt search enhancement generation framework and dynamic multi-round controllable reasoning engine to generate candidate cause of death chain;Adopt the mixed probability model of timing-cause dual constraint to evaluate candidate cause of death chain, calculate comprehensive probability to determine radical cause of death, and the result is visualized and presented.The application can realize the automation of cause of death chain generation and radical cause of death determination, significantly improve accuracy and efficiency, enhance the explainability of decision-making process, and have continuous learning ability.

The present application belongs to the technical field of medical data governance, and provides an electronic medical record data governance method and device for children with epilepsy. The method can acquire electronic medical record data from multiple heterogeneous data sources, construct multiple keyword dictionaries related to epilepsy for identifying and extracting information, thereby governing data from each data source through the keyword dictionaries, further generating structured data for clinical analysis of epilepsy, and displaying the epilepsy-related data of the patient in the form of a visualized graph based on the structured data. The scheme proposed in the present application converts the original electronic medical record data, which is scattered, chaotic and difficult to directly utilize, into structured data that is centralized, regularized and directly usable for clinical analysis and decision-making of children with epilepsy, solves the pain point of difficulty in integrating and converting medical data, improves the intuitiveness and efficiency of epilepsy diagnosis and treatment through visual means and provides strong data support, and can also provide analyzable and high-quality data for scientific research.

The application provides a kind of intelligent structured medical record generation method and system based on multimodal doctor-patient interaction, which comprises: real-time acquisition of dialogue voice and transcription into text sequence, while recognizing visual attention entity by listening to mouse operation in electronic medical record system.Based on the history of visual attention entity and text sequence, a logical demonstration track is constructed, and an implicit reward function is derived from it using a reverse reinforcement learning algorithm. Use the function to calculate the action reward value of each combination of visual attention entity and dialogue text, select the highest value combination as the optimal alignment strategy to determine the timing causal relationship. Map the entity and text to the medical knowledge graph, extract the shortest semantic path as the implicit clinical reasoning chain, and generate structured electronic medical record. The application deduces the diagnosis and treatment decision logic from the doctor's multimodal behavior through reverse reinforcement learning, solving the technical problem that traditional methods cannot establish the internal causal relationship between the doctor's visual attention focus and spoken content.

The application provides a liver cancer risk stratification and treatment recommendation information generation method and device, the method comprises the following steps: obtaining liver cancer related structured clinical data of a target object; based on a pre-set electronic medical record narrative template, converting the structured clinical data into an electronic medical record style narrative text containing clinical semantics, embedding key clinical variable markers corresponding to the structured clinical data into the electronic medical record style narrative text, and constructing a model input sequence; inputting the model input sequence into a pre-trained large language model for inference operation, outputting a structured text stream, and the large language model is obtained through decision tree constraint based on a liver cancer diagnosis and treatment guideline and multi-objective reinforcement learning strategy training; and extracting comprehensive decision assistance information from the structured text stream by using a pre-set analysis rule, realizing high-credibility clinical assistance decision, and significantly improving the accuracy and logical consistency of the treatment recommendation information.

The application discloses a method and a generating device for generating an electronic medical record based on a doctor-patient conversation, relates to the technical field of medical information, and is used for improving the accuracy of speech recognition when generating an electronic medical record based on a doctor-patient conversation.In the method, the generating device automatically distinguishes subjective self-reports of patients and empirical symptoms supported by objective evidence based on the doctor-patient conversation, and fills them into different columns of the medical record respectively, and meanwhile, the empirical symptoms are sorted according to the strength of the objective evidence, so that the accuracy of speech recognition is improved when generating an electronic medical record based on a doctor-patient conversation.

The application discloses a machine learning-based retrospective clinical data management method. The method comprises the following steps: obtaining an electronic medical record log file and parsing unstructured text to generate a word vector; calculating the Hamming distance between the word vector and the knowledge graph standard entity vector to extract standardized test indicators; establishing a two-dimensional physiological correlation mask matrix according to the pathology dependent edge; truncating the time series data into a sequence feature matrix and performing linear transformation to generate a query matrix, a key matrix and a value matrix; calculating the dot product value of the query matrix and the transpose of the key matrix, adding the scaling factor and the mask matrix after exponentiation, and then multiplying the value matrix to obtain the context feature representation; and generating a dense feature tensor data through a feedforward neural network to predict the interpolation value. The application can effectively curb the overfitting of the time series model under sparse data, improve the physiological authenticity of the feature array, and reduce the generalization error of the downstream prediction model.

This application relates to an intelligent voice control method, control system, device, and storage medium for an endoscope. The method includes: acquiring a first voice signal and comparing it with a standard voice signal; if the comparison is successful, the system starts; after the system is powered on, it performs image acquisition; during the acquisition process, it receives a second voice signal, recognizes the corresponding command, and executes the corresponding action; after completion, the data is synchronized and the system is shut down. A voiceprint authentication module simultaneously verifies the user's voiceprint characteristics and the power-on command upon power-on, ensuring only authorized personnel can start the device; a voice interaction module performs security classification of commands, requiring secondary voice confirmation for security-critical commands; a medical record processing module supports dictated medical records and automatically converts them into structured text, storing them in conjunction with an electronic medical record system. This achieves full-function voice control, voiceprint recognition-based secure power-on, and integrated voice medical record functionality, keeping doctors' hands sterile, avoiding cross-infection, improving operational convenience, safety, and diagnostic efficiency, and enhancing the comfort of the treatment environment.

The present application belongs to the technical field of medical artificial intelligence auxiliary decision-making, and particularly relates to a method for predicting postoperative delirium risk of elderly abdominal surgery patients based on perioperative clinical characteristics. The present application extracts the demographic characteristics, preoperative laboratory indicators and intraoperative anesthesia-related characteristics of elderly abdominal surgery patients based on an electronic medical record system, and constructs a full-feature data set for postoperative delirium risk prediction. Further, a machine learning algorithm is used to establish a postoperative delirium risk prediction model, and the target model is screened in combination with the model stability evaluation result. On this basis, the model is optimized by using SHAP feature contribution analysis and recursive feature elimination strategy, and a simplified prediction model with fewer variables and better generalization performance is obtained. The prediction model can realize early identification of high-risk groups of postoperative delirium of elderly abdominal surgery patients during the perioperative period, has high discrimination ability, stability and clinical applicability, and can provide a reliable tool for precise prevention and auxiliary decision-making during the perioperative period.

This invention belongs to the field of natural language processing technology. To address the problem of low input accuracy in existing technologies, it discloses a method and system for inputting traditional Chinese medicine (TCM) speech based on semantic perception and dynamic dialectical reasoning. This invention identifies TCM diagnostic elements in electronic medical records and dynamically constructs a diagnostic medicine pool by combining it with a TCM knowledge graph. This allows speech recognition to move beyond mechanical reliance on acoustic models and integrate with clinical diagnosis. Simultaneously, this invention expands the acoustic candidate word sequence with TCM-related confusing words, generating a more comprehensive set of TCM candidates. This set is then combined with the diagnostic medicine pool to generate candidate TCMs. Next, the semantic matching score between each candidate TCM and the diagnostic elements is calculated, and the corresponding real TCM name is determined based on this score. Therefore, this invention avoids mechanical recognition detached from syndromes and treatment principles and can effectively distinguish TCMs that are confused by homophones or near-homophones, thus reducing the input error rate.

There is a challenge in that doctors, hospitals, insurance companies, or patients cannot obtain the necessary medical information in a timely manner when they need it. Therefore, by providing patients with up-to-date digital medical records that can be securely accessed from anywhere, we can strengthen collaboration and communication among different healthcare providers and institutions. [Solution] This is solved by an AI system, method, and computer program product for patient diagnosis and treatment suggestion, which uses advanced AI algorithms to analyze all available data to diagnose diseases and suggest treatments.

A system and method for managing medication titration using real-time walking and balance data collected from mobile electronic devices are disclosed. Smartphone sensors, including accelerometers and gyroscopes, capture mobility metrics such as gait speed, stride characteristics, postural stability, and fall risk. The mobility data is integrated with additional physiological metrics and a patient's electronic medical records. Analytical algorithms, including machine-learning models, evaluate changes in mobility to identify correlations between medication usage and functional performance. Based on detected changes, the system generates alerts and recommendations to assist healthcare providers in adjusting medication dosages. The invention supports proactive and personalized medication management, particularly for medications that affect balance or fall risk, and improves patient safety by enabling data-driven clinical decision-making using real-world mobility insights.

The application provides an electronic medical record data encryption method and system based on browser fingerprints, which comprises the following steps: obtaining a browser fingerprint encryption public key of a user by using a front end; obtaining fingerprint data of a browser terminal by using the front end; encrypting the fingerprint data of the browser terminal by using the browser fingerprint encryption public key to obtain encrypted data; performing authorized registration processing on the encrypted data and a preset electronic medical record by using a back end, and generating an encryption public key of the preset electronic medical record; encrypting the preset electronic medical record by using the encryption public key of the preset electronic medical record by using the front end to obtain an encrypted preset electronic medical record; and transmitting the encrypted preset electronic medical record and the encrypted data to the back end, and storing the encrypted data and the encrypted preset electronic medical record by using the back end. The application improves the confidentiality of hospital electronic medical record data and solves the problem that one electronic medical record cannot be authorized by multiple devices.

This invention discloses a method for identifying mental illnesses combining electronic medical records and electroencephalogram (EEG) signals, comprising: acquiring electronic medical records and the corresponding patient's raw EEG signals; labeling patients based on disease type, and combining the labels, electronic medical records, and raw EEG signals into a dataset; constructing an initial model based on a large language model and a prior mental illness knowledge base, wherein the initial model includes an agent scheduling module, a data processing module, and a prediction module; and training the initial model using the dataset to obtain a multimodal predictive agent model for mental illnesses used to generate medical reports. The method provided by this invention enables multi-scale, multi-channel, and multi-feature exploratory analysis of EEG signals at any specified time slice, and jointly reasoning with medical record features and a mental illness knowledge base to generate structured diagnostic conclusions and interpretable reports.

A computer implemented method includes retrieving data from a patient's updated electronic medical record relevant to a diagnosis of a disease. The data includes medical images and at least one of the patient's demographic data, morbid symptoms, vital signs, medications, surgery history, family medical history, genetic data, laboratory test data, diseases records, allergies, and medical insurance information. The method includes generating available treatment path options including at least one surgical treatment or intervention, using at least one model to predict at least one implication for each of the available treatment path options based on the data and simulated adverse outcomes of the at least one surgical treatment or intervention simulated by predictive models including a biomechanical model based on the medical images. The method includes interactively updating and displaying a decision tree including the available treatment path options each with a corresponding at least one objective function.

A computerized system for personalized medical education, compliance verification, and clinical workflow automation uses sensor-based identification and multi-modal monitoring to verify user presence, engagement, and comprehension during delivery of educational modules and required disclosures. Artificial intelligence models interpret electronic medical record data to select and sequence patient-specific content, determine procedure-specific and jurisdiction-specific compliance requirements, and adapt presentation in real time through device-level control actions including pausing, prompting, repeating, and module recalibration when noncompliance is detected. The system further supports conducting interactive clinical encounters in lieu of or in conjunction with clinician visits, including structured intake, decision support, and generation of audit-ready, billable encounter documentation under clinician oversight and safety guardrails. The system generates time-stamped interaction logs, medical decision-making and time-on-task summaries, and electronic medical record-ready outputs with provenance and compliance verification, suitable for reimbursement and audit defensibility across multiple specialities.

This invention discloses an ophthalmic intelligent question-answering method, system, storage medium, and electronic device based on graph retrieval enhancement. The method first automatically constructs a structured knowledge graph from ophthalmic clinical guidelines, containing entities such as diseases, anatomical structures, and treatment methods, along with their causal, relational, or hierarchical relationships. Upon receiving a user's natural language query, it parses key entities and intents, and uses this as a starting point to perform multi-hop retrieval within the graph, generating a contextual subgraph. After pruning and refinement, enhanced prompts are formed, driving a large language model to generate accurate and interpretable responses. The system supports the integration of vision screening data or electronic medical records to achieve personalized question-answering and can optionally provide knowledge tracing paths. The graph supports dynamic incremental updates, and the retrieval depth and breadth can be adaptively adjusted according to query complexity. This invention significantly improves the clinical accuracy, safety, and interpretability of responses, and is suitable for scenarios such as doctor-patient communication, remote consultation, and medical education.

The application provides a multi-modal data fusion evaluation and prognosis prediction method and system for septic cardiomyopathy, which comprises the following steps: obtaining target data and electronic medical records of septic cardiomyopathy patients, combining the target data with a preset first database to construct a correlation matrix; constructing a second database corresponding to the septic cardiomyopathy patients according to the electronic medical records; generating a target interaction path based on the second database; obtaining a first signal according to the causal effect intensity of each node in the target interaction path, and generating a prognosis prediction scheme in combination with a target risk threshold. The application realizes real-time tracking and closed-loop intervention of the pathological process of septic cardiomyopathy, and fundamentally solves the prognosis prediction deviation caused by the time and space disconnection of the traditional database.

A system for managing liver disease, the system comprising an electronic medical record system comprising a plurality of patient records. The system comprising a patient management platform configured to access the electronic medical record (EMR) system to retrieve medical records of one or more patients, analyze the medical records to selectively identify data related to a liver condition, interface with one or more data analysis platforms configured to receive and analyze the data related to a liver condition. Based on analysis by the patient management interface and / or the data analysis platforms, a display is generated comprising: specific liver-related patient attributes and conditions, and at least one of: a liver health prognosis or a recommended treatment for addressing the liver condition corresponding to the one or more patients. The liver condition can comprise one or more of: cancer, cirrhosis, liver failure, fatty liver disease, steatosis, ischemia, and / or hepatitis.

The application provides a method for constructing a PCI postoperative patient all-cause death risk prediction model, which comprises the following steps: obtaining a training data set containing clinical electronic medical records, blood cell count and blood biochemical data of patients undergoing selective PCI, and at least 5 years of postoperative follow-up and all-cause death label; performing Z-score standardization on continuous variables and one-hot encoding on category variables for pretreatment; screening 43 core features through LASSO regression combined with ten-fold cross-validation; constructing a model based on a LightGBM algorithm, taking a binary logarithmic loss function as a measurement standard and optimizing hyperparameters through grid search; finally outputting a prediction model which can receive core feature data of a new patient and output the postoperative all-cause death probability of the new patient; and the application can realize accurate prediction of long-term death risk of PCI postoperative patients.

The application relates to the technical field of medical data analysis, and discloses a medical data analysis method and system based on a vertical domain intelligent agent, which comprises the following steps: acquiring image report texts, wearable data streams, electronic medical records, test results and other multi-source data, extracting structured entities and time sequence observation values and preprocessing, constructing a continuous multivariate trajectory of a unified time axis, fusing images and wearable information to generate an extended illness representation sequence, calculating local statistical features and combining previous data and a case library to form an association strength vector, weighting clustering a trend sequence to extract decision clues, searching for similar case trajectories and performing distribution matching to obtain a prognosis improvement probability, further correcting the trend sequence through reverse punishment, and finally completing risk stratification, abnormality screening and outputting a comprehensive evaluation report. The method can realize time sequence alignment and association analysis of heterogeneous medical data, and improves the accuracy and timeliness of illness evolution identification and clinical decision making.