524 results about "Patient information" patented technology

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 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 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.

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 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 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.

The invention discloses a medical care data intelligent evaluation system based on multi-modal data fusion, and belongs to the technical field of medical care. The problems that subjective deviation exists in the prior art, evaluation results of different medical workers on the same patient may be different, the medical data source is single, and patient information cannot be comprehensively reflected are solved. Through real-time feedback of nursing personnel, the frequency of abnormal data in a feedback result within a period of time is analyzed, so that the updating frequency of the model is dynamically adjusted, and misjudgment caused by model deviation is reduced; according to the method, the abnormal data in the multi-modal data are identified by calculating Z-score, the time sequence data are set to be high priority, and the abnormal data at the high priority are preferentially fed back to the nursing personnel, so that the response speed of the nursing personnel to abnormal conditions is increased, the distribution of nursing resources is optimized, and the nursing efficiency is improved. Therefore, nursing personnel can deal with emergencies in time, and nursing quality and patient safety are improved.

The invention discloses a rehabilitation medical assessment management system based on dynamic multi-scale adaptation and automatic calculation, and particularly relates to the technical field of medical assessment management. The assessment management system comprises a patient information acquisition module, a dynamic scale adaptation module, an automatic data acquisition and calculation module, a data analysis and report generation module, a rehabilitation management module and a database storage center, and the patient information acquisition module is used for acquiring patient information and storing the acquired information in the database storage center. According to the rehabilitation medical assessment management system based on dynamic multi-scale adaptation and automatic calculation, through dynamic multi-scale adaptation, the most suitable assessment scale combination can be selected according to individual differences and rehabilitation stages of patients, the rehabilitation conditions of the patients are comprehensively and accurately reflected, and the accuracy of assessment results is improved.

The invention discloses a reasoning and interaction system based on multiple dimensions. According to the inference system based on multiple dimensions, the complete process from multi-source data acquisition to inference result output is realized. The acquisition module is responsible for collecting out-hospital examination information and in-hospital examination information of a patient. And the pruning module predicts a targeted set by utilizing the interactively collected patient information and a preset diagnosis knowledge base, dynamically prunes the full-amount question-examination thinking sub-trees according to the targeted set, and optimizes the pruned question-examination thinking sub-trees. And the feature extraction module extracts key features from the interactively collected out-of-hospital and in-hospital question examination information based on the optimized question examination thinking sub-tree. And finally, the inference module inputs the key features into a pre-trained diagnosis model, and the model is combined with a Bayesian algorithm, a deep learning algorithm and a clinical decision consensus to output a final inference result. The problem that dynamic adaptation and efficient and accurate reasoning of multi-dimensional data cannot be achieved on a task set with specific requirements in the prior art is effectively solved.

The invention relates to the technical field of online medical treatment, in particular to an online inquiry intelligent diagnosis system based on a deep learning model, which comprises a patient interaction end, an intelligent diagnosis end and a hospital database end. The patient interaction terminal collects patient information and generates an inquiry request; the intelligent diagnosis terminal performs disease derivation and diagnosis and treatment scheme generation in combination with historical medical records and a medical knowledge base; and the hospital database end provides medical record storage and knowledge graph updating support. The system optimizes the diagnosis accuracy through a deep learning model processing platform, and has the functions of aging optimization and automatic registration. The problem of resource shortage of doctors can be relieved, the diagnosis efficiency and the medical record data utilization rate are improved, and efficient and accurate online inquiry service is provided for patients.

The invention discloses a transfer information management system in a patient hospital. The transfer information management system comprises a patient information management module, a transfer evaluation and grading module, a transfer plan generation module, an Internet of Things real-time monitoring module, a multi-department cooperation module and a quality control management module. The invention belongs to the technical field of medical informatization, and particularly provides an in-hospital transfer information management system for patients, which realizes transfer information linkage sharing and transfer quality control management through digital and intelligent means and ensures the safety of the patients.

The invention belongs to the field of medical image processing and artificial intelligence, and provides a colon cancer CT image segmentation method and system fusing individualized features, and the method comprises the steps: obtaining the abdomen CT images of a historical patient and a target patient; obtaining structured individual feature information related to the patient, wherein the individual feature information comprises age, gender, colon cancer family history and intestinal medical history information; preprocessing the abdomen CT image; and training an image segmentation model by using the preprocessed abdominal CT image of the historical patient, segmenting the preprocessed abdominal CT image of the target patient by using the trained image segmentation model meeting the segmentation precision requirement, and generating a colon cancer focus segmentation image conforming to the individual feature difference of the target patient. According to the method, the structured patient information is utilized to guide the model network to adjust the feature response, so that the segmentation precision and individual adaptability of the model are improved, and the segmentation accuracy is further improved.

The invention provides an electronic medical record data patient information desensitization and inspection data standardization method, and relates to the technical field of medical big data processing and privacy protection. The method comprises the following steps: collecting structured and unstructured electronic medical record data from a hospital information system; carrying out automatic identification and desensitization processing on the sensitive information of the patient; performing time alignment, missing value filling and normalization processing on laboratory inspection data; key features are extracted, a prediction model is constructed, and the adaptive capacity of the model in different medical policy environments is improved in combination with policy stage division and feature importance analysis. And the interpretability and prediction performance of the model are enhanced through multi-model fusion modeling and an SHAP interpretation mechanism. According to the method, efficient and compliant data desensitization is realized, the consistency of test data and modeling quality are improved, dynamic response policy changes are supported, and the method has good practicability and popularization value.

The invention discloses an intelligent outpatient service guidance and triage management method and system based on artificial intelligence. The method comprises the following steps that patient information, symptoms and outpatient service data are collected to generate triage input; constructing a triage optimization model based on the input, setting waiting time, path cost, load rate and priority satisfaction degree as targets, and setting a time window, resources and dependency constraints; an improved squirrel search algorithm is initialized, a population code is adopted to represent a distribution scheme, and a fitness function is established; updating the candidate scheme according to a fitness function in iteration; repairing and locally optimizing the updating scheme; when the operation data fluctuates, self-adaptive adjustment is triggered, and updating is continued; and when the fitness function converges or the iteration upper limit is reached, outputting an optimal triage scheme, including departments, time windows and path allocation, and executing incremental optimization when the patient arrives or the shift arrangement is changed. The triage efficiency is improved, the load is balanced, and the requirements of priority patients are met.

The invention discloses an intelligent sorting engine system for multiple examination tasks in a hospital treatment scene, which comprises a data acquisition module, an intelligent analysis module, a sorting module, a task pushing module, a feedback module, an emergency adjustment module and a module connected with a hospital reservation system, and efficient and reasonable checking task sorting and management are realized. Through a multi-source data fusion technology, data of each information system in a hospital can be integrated, and part of examination result data of other medical institutions can be obtained through a data interface with external medical equipment. Therefore, a doctor is ensured to have comprehensive and accurate patient information during diagnosis, repeated examination is avoided, and the utilization efficiency of medical resources is improved. A dynamic planning algorithm is adopted to quickly adjust the sorting of the examination tasks according to the real-time changing doctor seeing condition and resource condition, and a personalized examination task execution sequence is generated by considering factors such as the psychological state and the comfort level of a patient.

The invention relates to the technical field of medical management informatization, in particular to a visual multi-dimensional patient flow adjustment system and method, and the system comprises a patient sensing module, an intelligent analysis gateway, an adjustment strategy generation module, a resource dynamic scheduling engine, and a visual positioning platform. Wherein the patient sensing module is used for collecting basic information, doctor seeing states, department resource use conditions and environment data of patients in all areas of a hospital; the intelligent analysis gateway is used for fusing and processing data and predicting the patient flow risk through the gating circulation unit; the adjustment strategy generation module is used for carrying out treatment priority fine classification on the patients and generating a personalized flow adjustment strategy; the resource dynamic scheduling engine is used for performing accurate scheduling through an intelligent allocation algorithm according to the adjustment strategy; the visual positioning platform is used for calibrating patient positions and rendering a hospital-wide patient distribution thermodynamic diagram and a resource occupancy rate in real time. Therefore, the problems of long waiting time, unreasonable resource allocation, single management strategy and the like in the prior art are solved.

The invention relates to the technical field of first-aid patient information processing, in particular to a severe trauma early evaluation and processing system based on virtual-real combination interaction, which comprises a scene risk sensing module, a dynamic physiological extraction module, an image feature analysis module, an abnormal grade judgment module and a priority scheduling grading module. According to the invention, through abnormal time period positioning, multi-terminal data synchronization and fusion of multi-source data, feature analysis is carried out under a unified time sequence, continuous triggering of risk early warning and hierarchical response is realized, a wound index is automatically generated, geographical location information is linked, seamless information circulation and task pushing among multi-terminal platforms are realized, and the efficiency of risk early warning and hierarchical response is improved. Automatic synchronization, intelligent grading and scheduling response of data from a site to a hospital are promoted, system advantages are established in the aspects of multi-link cooperation, information closed loop and early trauma judgment, physiological process visual feedback, grading task closed loop and real-time resource matching are further supported, and the early recognition and processing efficiency of critical patients is improved.

The invention provides a distributed rehabilitation data analysis method based on federal learning, and the method comprises the steps: obtaining an encrypted rehabilitation medical data abstract, and obtaining an anonymized patient feature data set suitable for multi-party cooperation; performing learning model training on the anonymized patient feature data set in the local environment to obtain a shared global model parameter set; simulating distribution characteristics of rare disease cases based on parameters, performing adversarial data generation processing on the global model parameter set to obtain a patient information supplementary data set, and fusing real patient rehabilitation data to obtain a comprehensive rare disease case characteristic representation set; according to the comprehensive rare disease case feature representation set, a patient data distribution equilibrium index is calculated, a final distributed patient rehabilitation medical data analysis result is obtained, the problem of data insufficiency under privacy protection is effectively solved, the comprehensiveness and accuracy of rare disease case feature representation are improved, and the patient rehabilitation medical data analysis efficiency is improved. And comprehensive medical data analysis of multi-mechanism safety cooperation is realized.

The invention relates to the technical field of emergency treatment information management, and discloses an emergency treatment-oriented multi-disease integrated information management system, which comprises a patient information management module used for configuring multi-disease inspection items and recording standardized time nodes; the inter-hospital referral cooperation module is used for dynamically selecting a referral doctor and recording referral key time; the first-aid time axis module is used for automatically generating a visual record of a treatment process based on a time sequence; and the quality control analysis platform module quantitatively evaluates the treatment efficiency through medical indexes. Through real-time data transmission between the patient information module and the data interaction module, instant sharing of key medical data before and in a hospital is realized, information incompleteness or inaccuracy caused by telephone communication is avoided, time waste of repeated examination after a patient arrives at the hospital is reduced, and treatment delay is effectively shortened.

The invention relates to a remote electrocardio telemetering method and system, a terminal and a medium, and belongs to the technical field of electrocardio monitoring. The remote electrocardio telemetering method comprises the steps that a cloud end distributes monitoring equipment and initial parameters according to patient information; the edge end receives real-time electrocardiogram data, obtains environment data in combination with the position of a patient, collects physiological data through a wearable sensor, constructs a spatio-temporal context sensing map, fuses the data to generate situation enhanced electrocardiogram signal representation, inputs a comprehensive risk prediction model to obtain a dynamically updated prediction risk value and uploads the prediction risk value; the cloud judges the predicted risk value, if the predicted risk value is larger than a threshold value, a matched disease type is called, and differential prompts are generated and pushed to the patient and the doctor in combination with the co-disease relation network; if the electrocardiogram data does not exceed the threshold value, carrying out deep analysis on the electrocardiogram data, identifying abnormity, determining an abnormity level, generating a personalized report in combination with treatment history, pushing the personalized report, and triggering a clinical response protocol. The method has the beneficial effect of feeding back the abnormal electrocardiogram condition of the patient in time.

The invention discloses a perineal pelvic floor color ultrasound examination system and a use method thereof, and relates to the technical field of color ultrasound examination.An image optimization module automatically adjusts gain, contrast, filtering and tissue harmonic imaging modes, and a three-dimensional imaging module is used for capturing changes of pelvic floor muscles in resting and moving states; the data analysis module combines the optimized ultrasonic image data and muscle dynamic information to intelligently evaluate the image quality, automatically calculates the bladder neck activity, the urethra rotation angle and the pelvic floor muscle thickness in a high-quality image, and provides an abnormal marking prompt; the user interaction module realizes visual display of ultrasonic images and analysis results, supports a doctor to input patient information, and improves operation convenience and diagnosis accuracy; the whole system optimizes the visualization ability of an anatomical structure, reduces artifact interference, improves the evaluation accuracy of pelvic floor dysfunction, provides more reliable data support for a personalized treatment scheme, and finally improves the clinical diagnosis and treatment efficiency.

The invention discloses a radiodiagnosis report intelligent distribution system and method based on a multi-dimensional rule engine, and belongs to the technical field of medical image information processing. In order to solve the problems that existing radiology department reports depend on manual distribution, the efficiency is low, emergency reports are prone to missing detection, and resource distribution is uneven, a data input module is in butt joint with a PACS / RIS system to collect data and preprocess the data; the dynamic rule engine integrates multi-dimensional parameters such as patient information and examination types, and dynamically calculates distribution priorities according to a weight algorithm; the intelligent matching module is combined with NLP keyword extraction, load balancing and historical efficiency prediction to realize accurate matching of a report and a doctor; the exception handling module provides a three-level rollback strategy; and the security convergence block ensures data security. The system can dynamically adapt to hospital requirements, improves report distribution efficiency and accuracy, meets medical compliance requirements, and is suitable for hospitals and third-party image centers.

The invention discloses an intelligent diagnosis guide system for oral health, relates to the technical field of intelligent medical treatment, and solves the problems that in existing oral diagnosis guide, the description voice of a patient is not clear due to partial pathological changes of the oral cavity; the diagnosis guide analysis result error is large, and the selection accuracy of the diagnosis guide department is reduced. Comprising a data acquisition module for acquiring initial patient information, and a hospital guide analysis module for extracting test audio information in voice information and acquiring test keyword data; generating a test audio state based on the test audio information and the test keyword data; extracting description audio information in the voice information, and generating description text information from the description audio information based on the test audio state; generating a first hospital guide keyword based on the description text information and the text information, and inputting the image information into an image recognition model to obtain a second hospital guide keyword; generating a hospital guide department based on the first hospital guide keyword and the second hospital guide keyword; and the accuracy of hospital guide is improved.