770 results about "Patient information" patented technology

The invention discloses an intelligent dynamic triage system and method for emergency patients. The triage system comprises a data acquisition module used for acquiring basic information, physiological parameters and historical records of patients; the artificial intelligence analysis module generates a preliminary triage result; the Internet of Things connection module collects hospital resource data in real time; the central control processing unit is connected with each module to receive data; the dynamic grading decision-making module divides the patients into critical symptoms, severe symptoms, subcritical symptoms and common symptoms; the resource allocation module dynamically generates a resource allocation scheme according to the grading result; the diagnosis and treatment process management module tracks the whole process of the patient and updates state data; when the state of the patient changes, the system transmits data to the artificial intelligence analysis module again for evaluation, triggers the grading decision and resource allocation module, dynamically updates the triage priority and the resource allocation scheme, and achieves closed-loop feedback and continuous optimization.

The invention discloses a semantic enhancement auxiliary inquiry system and method based on a medical knowledge graph, and the system comprises a patient information receiving module, a theoretical knowledge graph storage library, an evidence-based knowledge graph storage library, a double-track diagnosis path construction module, a medical knowledge conflict judgment module, and a semantic enhancement report generation module. The double-track diagnosis path construction module inquires the received patient information in a theoretical knowledge graph storage library and an evidence-based knowledge graph storage library which are independent from each other in parallel, and a theoretical diagnosis path and an evidence-based diagnosis path are generated respectively; the medical knowledge conflict judgment module dynamically compares the two paths in real-time interaction, and performs priority judgment according to a preset medical judgment rule; finally, the semantic enhancement report generation module presents the two paths and the conflict judgment result to the user at the same time. Transparency and interpretability of the whole interrogation process are assisted, and reliable semantic enhancement decision support is provided for doctors when the doctors face complex medical knowledge conflicts.

The invention provides a patient information collection and medical record construction system and method based on multiple rounds of dialogues, and the system comprises an intelligent guide interaction module which receives the natural language input of a patient; the context-aware question and answer engine adopts a dialogue state representation method based on a graph structure to construct entities, relationships and attributes of each round of dialogue into knowledge sub-graphs; the medical record information dynamic builder monitors an updating event of the dialogue state diagram in real time; the abnormal information detection module adopts a mixed conflict detection method combining rules and learning; and the medical knowledge graph support system maps the oral expression of the patient to a standard medical term system in real time through a multi-level semantic matching strategy. According to the invention, the doctor does not need to distract the record in the inquiry process, and can pay more attention to patient observation and clinical thinking. The quality of the medical record first draft automatically generated by the system is high, a doctor only needs to perform a small amount of auditing and supplementing, and the medical record writing time is greatly shortened.

The invention relates to an advanced gastric cancer survival prediction system based on Lasso regression, Cox regression and an interpretable machine learning technology, and belongs to the technical field of medical artificial intelligence and intelligent decision support. According to the system, by collecting multi-modal clinical data (including demographic information, TNM staging, treatment modes, tumor grading and the like) of a patient, survival-related variables are screened by adopting Lasso regression and a Cox proportional risk model, and an optimized feature set is constructed. Based on the feature set, the system integrates various mainstream machine learning algorithms (such as XGBoost, Random Forest, SVM, Logistic regression and the like) to construct a prediction model, compares the performance of each model, and selects a model with an optimal effect as a main model. And hyper-parameter tuning is performed on the model through grid search and cross validation, so that the precision and generalization ability of the model are improved. An SHAP interpretability analysis method is introduced into the system, transparent interpretation is carried out on a model output result from the global level and the individual level, and the importance and directional effect of all variables in survival prediction are determined. Finally, the model is deployed on a terminal device, a doctor is supported to automatically output the survival probability and an explanation result after inputting patient information, and a reference basis is provided for clinical treatment decision and personalized management. The system has the advantages of high prediction precision, high interpretability, convenience in use, sustainable optimization and the like, is suitable for clinical aid decision-making scenes, and has good application prospects and popularization values.

The invention relates to an intensive care cross-institution collaborative data processing method, which comprises the following steps of: defining a hierarchical data model based on the intensive care field, and modeling various types of information including vital signs, clinical diagnosis and laboratory detection results of a patient according to a time sequence; a self-adaptive standardized interface is designed, and data formats from different hospitals or devices are identified and converted; real-time dynamic tags are introduced for patient data through a tagging technology; various health data, including vital signs, medical history and treatment feedback, of a critical patient are converted into a multi-dimensional health track, and sequential analysis is used for conducting continuous modeling on the condition of the patient; a deep learning and reinforcement learning method is combined to establish a disease evolution deduction engine; the disease development is predicted according to historical data, and the influence of different intervention measures on the disease is simulated to intelligently recommend a treatment strategy; and according to the dynamic deduction result, generating a plurality of treatment schemes, and dynamically adjusting the intervention strategy according to the real-time response of the patient.

The invention discloses a tumor chemoradiotherapy reaction automatic prediction system, and particularly relates to the technical field of medical automation, the tumor chemoradiotherapy reaction automatic prediction system comprises a data acquisition module, a data preprocessing module, a feature extraction and selection module, a model training module, a prediction analysis module, an early warning module and a feedback module; the multi-source data acquisition module comprehensively gathers patient information, the quality is improved through the preprocessing module, a foundation is built for the feature extraction and selection module to accurately screen key features, the data dimension is greatly reduced, and the model training efficiency is remarkably improved; the model training module applies a machine learning algorithm to deeply mine a data complex relationship, so that the prediction is more accurate and reliable; on the basis, the prediction analysis module outputs detailed chemoradiotherapy reaction prediction and provides confidence evaluation to help doctors to make decisions and select; and the early warning module is responsible for predicting, warning abnormity in time and helping doctors to plan intervention in advance.

The invention relates to the technical field of medical evaluation, and provides an evaluation system and method for potential complications of the department of cardiology, and the method comprises the following steps: S1, collecting information data of a patient, and carrying out the data verification and data preprocessing; s2, based on the obtained information, classifying types of complications which may occur on the patient; and S3, based on a complication type classification result, risk factors are extracted, and a risk factor network is constructed. By classifying the complication types, constructing the risk factor network and comprehensively considering multiple factors to calculate the risk index, compared with a traditional assessment mode, the risk of potential complication of a patient can be assessed more comprehensively and accurately, misdiagnosis and missed diagnosis caused by incomplete assessment are reduced, and the risk assessment efficiency is improved based on the risk score, the risk index and the risk type. The drug dosage or the measure intensity is adjusted through the patient specificity index, the prevention measures are dynamically adjusted in combination with the treatment time, and the pertinence and effectiveness of the prevention scheme are improved.

The invention discloses a multi-department medical sample collaborative management system and method, and relates to the technical field of medical information, and the system comprises an integrated information interaction platform which is used for achieving the real-time sharing of basic information, clinical diagnosis information and inspection reports of patients; the unique identification module is used for carrying out full-life-cycle tracking on a patient sample through a bar code or a radio frequency tag and dynamically associating the patient sample with patient information; the Internet of Things monitoring module is used for collecting and uploading temperature and humidity parameters of the sample storage environment in real time and triggering abnormal early warning; the automatic storage equipment executes sample classified storage and calling based on a platform instruction, and synchronously updates a sample state with the information system; and the data security module is used for realizing data encryption, dynamic authority management and operation traceability by adopting role-based access control and a block chain technology. According to the technical scheme, the core problems of data islands, low operation efficiency, weak safety and the like in traditional medical management can be systematically solved.

The invention discloses a lingual surface diagnostic information acquisition, diagnosis and analysis method and system, and belongs to the technical field of medical auxiliary diagnos.The method comprises the steps that an acquired lingual surface image is matched with patient information, a symptom associated lingual surface area is determined, the associated lingual surface area is subjected to priority division, and whether the acquired image meets a clear standard or not is judged; the method comprises the steps of constructing an image index evaluation model based on tongue vibration and image texture, performing tongue vibration, texture stability and water vapor fuzzy interference evaluation on an image needing to be processed, constructing a stable frame evaluation model, and importing vibration intensity, texture stability and a water vapor proportion into the stable frame evaluation model to evaluate image area stability. Image registration is carried out on the stable frame set, multi-frame registration and fusion are carried out based on an image stable region, region stability and processing information are recorded, a high-quality image for tongue picture analysis is generated, the image definition of a key diagnosis region is improved, and the accuracy and stability of tongue picture analysis are improved.

The invention relates to the technical field of neurosurgery patient follow-up visit management, and discloses a neurosurgery patient follow-up visit management system. The system comprises a patient information acquisition module, a follow-up plan generation module, a data cross validation module, a risk level judgment module and a follow-up structure output module. The system constructs an original information sequence template by extracting patient medical record text information elements; establishing an information directed path based on the template to form a follow-up visit plan path structure; extracting a path node sequence and screening cross data; matching risk labels according to the terminal node to obtain a risk affiliation label group; and finally generating a follow-up visit structure table. According to the system, structured processing, dynamic planning, data cross validation and accurate risk assessment of follow-up visit information of neurosurgical patients are realized, and scientificity and high efficiency of follow-up visit management are improved.

The invention provides an intelligent infusion system and an intelligent liquid control method based on database dynamic feedback regulation and control, and relates to the technical field of medical equipment intelligent control. The system comprises an infusion terminal execution module, a multi-parameter monitoring module, a data transmission module, a central control server module and a dynamic database module; patient information, an infusion scheme, a threshold range and multi-dimensional data of historical records are stored through the dynamic database module, the central control server module calls the data and compares the data with real-time physiological parameters and infusion state parameters collected by the multi-parameter monitoring module for analysis, and the target infusion speed is calculated. Early warning is carried out according to the target infusion speed and the real-time monitoring parameters, accurate monitoring of the regulation and control parameters and the individual condition of the patient is ensured, the infusion speed is always in the optimal range adaptive to the physiological state of the patient, and the infusion adverse reaction caused by parameter errors and exceeding the physiological range is avoided.

The invention discloses a medical monitoring method based on multi-modal prompt interaction, which comprises the following steps: acquiring vital sign data of a patient, performing scoring and sorting according to patient information and the vital sign data to obtain patient monitoring scores, and forming a patient monitoring sequence; obtaining the service capability data, the current workload and the position data of the medical personnel, carrying out the sorting and scoring according to the service capability data, the current workload and the position data of the medical personnel, obtaining the state score of the medical personnel, and forming a medical personnel distribution sequence; according to the priority order of the patients in the patient monitoring queue, selecting the optimal medical staff in the medical staff distribution queue; and according to the patient monitoring score, generating a multi-modal prompting strategy, and according to the multi-modal prompting strategy, carrying out multi-modal prompting on the selected optimal medical personnel.

The invention discloses a case analysis method and system based on a large language model, and belongs to the technical field of case analysis, and the method comprises the following steps: S1, patient information input, S2, examination item recommendation and determination, S3, preliminary diagnosis generation, S4, case screening and diagnosis and treatment suggestion generation, S5, diagnosis and treatment process monitoring and adjustment, and S6, case report generation and viewing. By means of the powerful knowledge integration and reasoning ability of a large language model, in combination with massive historical case data and professional medical database information, multi-dimensional diagnosis thought reference is provided for doctors, the misdiagnosis and missed diagnosis risks caused by personal experience limitation are reduced, the health indexes of patients can be monitored, and the diagnosis accuracy is improved. Personalized adjustment is performed on the diagnosis and treatment plan according to the disease change and the diagnosis and treatment condition of the patient; the medical database can dynamically expand and correct contents; diagnosis and treatment plans and suggestions can be given according to diagnosis and treatment passes of previously cured patients with similar conditions.

The invention belongs to the technical field of man-machine interaction, and discloses a diagnosis and treatment auxiliary system and method based on a voice gesture interaction technology, and the system comprises a voice recognition module which is used for collecting voice information; converting the voice information into text information; inputting the text information into the large language model to obtain a voice system instruction, and sending the voice system instruction to the data processing and interaction module; the gesture recognition module is used for collecting diagnosis and treatment scene information; identifying a preset operation gesture in the diagnosis and treatment scene information; mapping a preset operation gesture into a gesture system instruction and sending the gesture system instruction to the data processing and interaction module; the data processing and interaction module is used for receiving and executing a voice system instruction or a gesture system instruction; generating a calling instruction according to the voice system instruction or the gesture system instruction and sending the calling instruction to the network communication module; converting the received medical record data into diagnosis and treatment data in a display format; the network communication module is connected with a hospital information system; the calling module is used for calling medical record data after receiving a calling instruction of the data processing and interaction module and sending the medical record data to the data processing and interaction module; the display module is used for displaying the diagnosis and treatment data. The problem that a doctor cannot conveniently operate a computer to check patient data due to the sterility principle in the diagnosis and treatment process is solved, the diagnosis and treatment efficiency is improved, infection control is facilitated, and the sterility principle is met.

The invention discloses a rehabilitation exercise system and method based on intelligent interaction, and relates to the technical field of medical rehabilitation, intelligent management of basic information, medical history information, treatment information and medication information of patients is realized through a patient information management module; personalized exercise plans and real-time action guidance are provided for the patient through the rehabilitation exercise module; dynamically evaluating the rehabilitation state of the patient through a rehabilitation evaluation module; a friend communication platform is established through the community interaction module, and science popularization education is provided; and real-time monitoring and early warning of the doctor on the rehabilitation process of the patient are realized through the doctor supervision module. The artificial intelligence technology is adopted, the rehabilitation actions of the patient are monitored and corrected in real time through the camera, the rehabilitation state of the patient is evaluated through the electronic scale and the intelligent scoring system, the exercise plan is dynamically adjusted according to the rehabilitation condition of the patient, the rehabilitation exercise accuracy and the patient compliance are effectively improved, and the rehabilitation effect of the patient is improved. And the intelligent and electronic management of the whole rehabilitation exercise process can be realized.

The invention discloses an emergency patient information data matching method and system based on the Internet, and the method comprises the steps: obtaining multi-source heterogeneous data in real time according to an electronic medical record, image data and real-time vital sign data of an emergency patient, and generating a time-synchronized multi-source data set through a self-adaptive data format conversion algorithm; for the multi-source data set, using a feature extraction network to extract illness state features of patients, and generating patient priority scores; adjusting a resource allocation scheme in real time by adopting a medical resource matching algorithm based on a graph neural network according to the patient priority score and the illness state feature, and generating a preliminary medical resource matching result; and for a medical resource matching result, generating a final emergency patient information data matching scheme through a differential privacy protection mechanism by adopting a data security transmission protocol based on homomorphic encryption. According to the embodiment of the invention, the method can meet the requirements of quick, safe and effective processing of data in an emergency scene, and improves the quality and efficiency of emergency medical service.

The invention belongs to the technical field of rehabilitation assessment, and provides a cerebral apoplexy rehabilitation assessment method and system based on multi-mode electroencephalogram and myoelectricity fusion. According to the method, 59-channel electroencephalogram signals and 14-channel electromyographic signals are preprocessed, mutual information between channels is calculated to construct a correlation matrix, spatial-temporal features are extracted in combination with a convolutional neural network (CNN) and a Transform self-attention mechanism, and rehabilitation level classification is achieved. According to the method, coherence between electroencephalogram / myoelectricity channels is quantified by adopting mutual information, and 59 * 59 and 14 * 14 dimensional feature matrixes are constructed; a CNN-Transform hybrid model is designed to optimize feature fusion, and the classification precision is improved; gradient weighted class activation mapping (Grad-CAM) is introduced to analyze feature saliency, and correlation between an electroencephalogram channel and a focus is revealed; and developing a visual evaluation system, and displaying the multi-modal data and the historical trend in real time. The system stores patient information and evaluation results through a MySQL database, and supports doctor-patient online interaction. Compared with traditional scale evaluation, the method has the advantages that the evaluation objectivity is improved by using multi-modal objective data, the model credibility is enhanced by combining interpretability analysis, and technical support is provided for formulating a personalized rehabilitation scheme.

The invention belongs to the technical field of application of an artificial intelligence technology to medical rehabilitation instruments, and relates to a method for designing an orthosis based on an AI artificial intelligence technology, and the method comprises the steps: S1, data collection and preprocessing: converting patient data into structured data; s2, feature extraction: extracting geometric and biomechanical features related to orthosis design, and constructing a feature set; s3, processing the features by using a deep learning model, and predicting personalized parameters; s4, designing and optimizing an orthopedic module, and optimizing pressure distribution; s5, a three-dimensional orthopedic CAD system is developed and deployed, and patient information and orthopedic device design are managed; and S6, evaluation and optimization: fusing clinical data, and optimizing the system according to an evaluation result. According to the method, the AI technology is utilized to improve the design efficiency and the adaptation accuracy, the personalized requirements are met, the rehabilitation effect is optimized, digital management of data and iteration of an AI model are achieved, a treatment basis is provided for rehabilitation medical employees such as a brace teacher and a rehabilitation therapist, and the clinical effectiveness of a rehabilitation treatment scheme is improved.

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

The invention discloses an emergency pre-examination grading system and method based on collaborative decision of a large language model and a tree model. The method comprises the following steps: constructing a data set according to patient information and screening data; training an initial random forest model based on the data set, generating a basic decision rule, mining and extracting high-frequency features based on association rules, and combining to obtain a candidate rule pool; constructing a cue word structure adaptive to the field, driving LLM to complete rule correction, and forming a correction rule set; performing multiple rounds of rule random division and rule combination generation based on the correction rule set, and then screening out an optimal rule; and on the basis of a specific scene, the expert rule and the corrected optimal rule are fused, matching is carried out on patients, and emergency pre-examination grading is realized. According to the method, the interpretability is improved while the grading accuracy is improved, and the problem of poor cross-courtyard generalization of a machine learning model is effectively solved. The generalization ability of the rule is remarkably improved, and the method is adaptive to a multi-center combined diagnosis and treatment scene.

The invention discloses a machine learning-based intra-operative bleeding risk prediction method and device for spinal surgery. The machine learning-based intraoperative bleeding risk prediction method for spinal surgery comprises the following steps: acquiring information of a patient to be predicted; obtaining a trained hemorrhage risk prediction model; and inputting the information of the patient to be predicted into the trained massive hemorrhage risk prediction model so as to obtain a prediction result. According to the method, the high-precision prediction model is trained through large-scale patient data (including basic information, operation parameters, blood indexes and the like), so that the accuracy and the stability of intraoperative SBL risk prediction are improved. And a Cell Saver use suggestion based on a risk threshold is provided, and blood resource allocation is optimized. Clinical decision-making efficiency is improved through an automatic tool, blood transfusion related complications (such as infection and immune response) are reduced, and patient prognosis is improved.

The invention provides a bone tumor fine-grained classification model training and classification method and device, and the method comprises the steps: constructing a multi-modal positive sample containing global / local positive lateral X-ray images, lesion attributes and patient information, and removing a false negative part in combination with text semantic similarity to construct a high-quality negative sample; global / local image features are extracted through double image encoders, lesion attribute keywords are converted into'entity-translation-existence 'triples based on a medical knowledge base, and basic information of a patient and global / local semantic features of lesion attributes are extracted through a text encoder; infoNCE contrast loss is constructed for global images and global semantics based on contrast learning, global image-text feature alignment and local image-text feature alignment are realized in combination with a local mutual information loss and classification loss training model calculated based on a DV variational formula, medical term semantics are deeply combined, the training stability is improved, and the training efficiency is improved. The accuracy and robustness of bone tumor subtype classification are remarkably improved, and reliable support is provided for clinical precise diagnosis.

The invention relates to the technical field of medical report generation, in particular to an intelligent anesthesia record sheet automatic generation system. According to the system, patient information is automatically recognized through the OCR unit, and real-time collection and structured storage of physical sign data, medication records and surgical operation are achieved in combination with a medical equipment interface and a voice recognition technology; time sequence data analysis and multi-source data fusion modeling are adopted to construct a medical event causal relationship graph so as to improve the accuracy of anomaly recognition and patient sign evaluation; based on a time axis construction unit and a structured template engine, an anesthesia record list is automatically analyzed and filled, and the manual input workload is greatly reduced; and finally, in combination with block chain evidence storage and a multi-level encryption technology, the security and non-tampering property of the anesthesia record list are ensured, the reliability of medical data management is improved, the recording, monitoring and storage efficiency of the anesthesia process is comprehensively optimized, and the medical quality and the patient safety are improved.

The invention discloses a semantic enhancement auxiliary inquiry method and system based on a medical knowledge graph. According to the method, natural language processing, knowledge graph construction and reasoning and other means are fused, and by means of hierarchical entanglement state tracking, dynamic prompt template design, undirected isomeric graph construction and other methods, patient information is accurately obtained through multiple rounds of dialogues, the semantic understanding and reasoning ability is enhanced, an optimization scheme is provided for intelligent inquiry, and the intelligent inquiry efficiency is improved. And disease classification and treatment schemes can be determined in an assisted manner.

The invention relates to the technical field of data processing, and discloses an emergency department patient information processing method and system based on big data, and the method comprises the steps: carrying out the standardization processing of an unstructured text and heterogeneous data through natural language processing and medical ontology mapping; the method comprises the following steps: dynamically monitoring vital sign data of a patient, extracting trend change and fluctuation amplitude, and mining a frequent pattern of illness state evolution from historical medical record data based on an FP-Growth algorithm; through a Transform-GNN-XGBoost model, the disease deterioration risk of the patient is predicted, and in combination with historical medical record data and the current state of the patient, the hospitalization duration and the treatment cost of the patient are predicted; dynamically generating an optimal triage strategy and a resource scheduling scheme through a genetic algorithm; the prediction result, the optimal triage strategy and the resource scheduling scheme are visually displayed to medical staff; the diagnosis and treatment efficiency and the resource utilization rate of the emergency department are remarkably improved.

The invention relates to the field of intelligent control, and particularly discloses an anesthetic injection intelligent control system which predicts and generates personalized initial injection parameters by deeply integrating multi-source heterogeneous data such as patient individual information, surgical sites and anesthetic types. Through high-frequency collection of physiological and process signals such as tissue pressure and injection displacement, the system can accurately calculate the current actual pressure, the pressure change trend, the real-time flow rate and the total injected amount. The real-time monitoring data is continuously compared and evaluated with the generated personalized threshold value and the preset target so as to intelligently judge whether the current injection is smooth or not, whether abnormal resistance exists or not or whether the current injection is close to the target or not, and a mark reflecting the current injection state is generated according to the judgment result; and finally, the system dynamically adjusts the output of the injection pump according to the state flag in combination with a preset control logic and an optimization algorithm, and adaptively corrects the current target flow velocity.

The embodiment of the invention relates to a method and a device for generating a medical report based on evidence-based medicine. The method comprises the following steps: acquiring patient information and an original clinical problem; inputting the patient information, the original clinical question and the first cue word into a target model to generate a structured clinical question; the target model is a medical field large language model; retrieving a medical literature library according to the clinical problem to obtain a plurality of medical literatures; inputting the target medical literature and the second cue word into a target model, so that the target model performs bias risk assessment on the target literature, and a structured initial assessment result of the target literature is generated; inputting the clinical problem, each medical literature, the initial evaluation result of each medical literature and the third cue word into a target model, and enabling the target model to carry out thinking chain gradual reasoning based on a GRADE evaluation process in the third cue word to generate a medical report; the medical report includes medical suggestions for clinical issues, and recommendation intensities and evidence quality levels of the medical suggestions.

An AI-based system and method for supporting differential diagnosis and standard of care in healthcare. The method involves receiving patient information from various sources, including patient-reported symptoms, physician notes, and sensor data from medical devices. The patient information is preprocessed and analyzed using deep learning models to generate a ranked list of potential diagnoses, each associated with likelihood scores and key contributing factors. The potential diagnoses are provided to physicians via an interactive interface, and physician feedback is collected to fine-tune the AI models using reinforcement learning. The method aims to enhance physician decision-making, improve diagnostic efficiency, and ensure adherence to the standard of care by leveraging AI's ability to analyze vast amounts of data more effectively than human physicians.

According to the clinical nursing patient information monitoring and inputting method and system, the data accuracy, the data inputting efficiency and the privacy protection safety are improved; the method comprises the following steps: firstly, collecting and preprocessing vital sign data of a patient in real time through various physiological sensors; then, acquiring a nursing record image, acquiring nursing record data through an OCR model, acquiring nursing information voice, transwriting the nursing record data and the nursing information voice into nursing information data through a medical scene voice transwriting model, and performing unified standardization processing on the three groups of data to generate patient information data; and finally, when patient information data is input, symmetric encryption is carried out by adopting a national cryptographic SM4 algorithm, encrypted data is obtained, a dynamic encryption key is generated through an ECDH key distribution technology, and the dynamic encryption key can be called to carry out decryption operation after authentication of a dual protection mechanism of Hash value pre-verification and identity authentication is passed.

A computer-implemented method for creating medical reports, in particular physician's letters, comprises the acquisition of patient information from various sources of electronic health records, with the latter being available in natural-language sampling points. Medical units are determined by computer-aided pattern recognition and classified according to their event type. Event groups that have temporal, thematic or treatment-related relationships are formed. Relationship information is derived and stored in a structured data representation. In addition, these data are enriched with an external medical knowledge base and converted into a format that can be processed by a generative language model in order to generate the continuous text for the medical report.