1730 results about "Patient Base" patented technology

A physiological patient monitor can include a front end conditioner that can receive analog sensor signal from a sensor responsive to a person's physiology. The patient monitor can further include an analog-to-digital converter that can convert analog sensor signal to a digital sensor information. The patient monitor can determine one or more patient specific physiological parameters. The patient monitor can further determine a physiological condition of a patient based on processing multiple physiological parameters. The patient monitor can generate a user interface that can included a visual representation of the patient's physiological condition.

Systems and methods are disclosed for evaluating a patient with vascular disease. One method includes receiving one or more vascular models associated with either the patient or with a plurality of individuals; receiving observed perfusion information associated with the patient; and estimating, using one or more computer processors, one or more blood flow characteristics or one or more pathological characteristics of the patient based on the observed perfusion information and the one or more vascular models.

The invention relates to the technical field of disease follow-up visit, in particular to a health state assessment system applied to disease follow-up visit, which comprises a data acquisition module, a data preprocessing module, a health state assessment module, a personalized health management suggestion module, a dynamic health risk prediction module and a visualization module, the data acquisition module acquires multi-dimensional data of a patient and performs data fusion and integration, the health state evaluation module evaluates the health state of the patient based on multi-level feature extraction and a deep learning model, and the personalized health management suggestion module combines a health state evaluation result and historical data to obtain a personalized health management suggestion result. Customized health management suggestions are generated through a rule engine, and the dynamic health risk prediction module predicts future health risks of patients and provides real-time early warning by using a time sequence analysis model. The visualization module displays health assessment and management suggestion results through a chart and an interactive interface, and the accuracy and individuation degree of health management are improved.

The invention discloses a whole course follow-up visit management system and method for gynaecology and obstetrics patients, and relates to the field of medical information management.The method comprises the steps that when a patient sees a doctor for the first time, basic information, medical related information, medical image data, gene detection data, sensor data and patient self-report data of the patient are input; preprocessing the input patient data, and integrating the preprocessed patient data into a personal health record; establishing a health assessment model of the patient based on the integrated personal health record, outputting a current health assessment score of the patient, and generating a follow-up visit plan according to the current health assessment score of the patient; and establishing an obstetrical complication prediction model based on the personal health record, and outputting a patient complication prediction value. Potential obstetrical complication risks are recognized in advance, especially when the health condition of the pregnant and lying-in woman changes, the prediction result can be dynamically updated, real-time monitoring and early warning of complication are achieved, and safety management of the lying-in woman is optimized.

The invention discloses a chronic disease patient information management system and method based on intelligent AI, and relates to the technical field of information management. The information management system comprises a patient information management module, a data acquisition module, a data management module, a data analysis and evaluation module, a risk early warning module, a health management module and a remote medical service module. According to the chronic disease patient information management system and method based on the intelligent AI, learning and analysis are carried out based on comprehensive health data of the patient, so that accurate assessment of the health risk of the patient is realized, the current health data of the patient is integrated, and multi-dimensional information of medical history and living habits of the patient is combined, so that the assessment result is more comprehensive and accurate, and meanwhile, the assessment efficiency is improved. The health risk possibly faced by the patient in the future is predicted, early warning is given out in advance, sufficient time is provided for medical institutions to formulate intervention measures, potential health problems can be found and handled in time, and disease deterioration is prevented.

Disclosed is a system for providing personalized self-medication, onboarding, training and improving medication adherence. The system includes a patient computer system comprising at least one processor and at least one memory component: a display, an optional input device, and an optional pointing device, a server connected to the patient computer system by a network link, the server comprising at least one processor and at least one computer readable medium wherein at least one computer readable medium comprises instructions that when executed by at least one processor, cause at least one processor to perform acts comprising: (i) responsive to patient input data, producing a patient personalized assessment; (ii) formulating a customized intervention for the patient based on the patient input data and / or personalized assessment; and (iii) presenting the patient personalized assessment and / or customized intervention on the display.

The invention relates to the field of traditional Chinese medicine rehabilitation scheme recommendation, in particular to a traditional Chinese medicine rehabilitation scheme recommendation method and system. The method comprises the following steps: firstly, acquiring examination reports of a to-be-tested patient and each historical patient, acquiring a rehabilitation scheme of each historical patient, and performing natural language processing on each examination report and each rehabilitation scheme to obtain word vectors of phrases in the examination reports and the rehabilitation schemes; the method comprises the following steps: obtaining symptom similarity of each historical patient according to word vector difference of phrases in an examination report between a to-be-tested patient and the historical patients, screening out similar patients of the to-be-tested patient, obtaining scheme difference indexes of the to-be-tested patient according to word vector difference of phrases in rehabilitation schemes among the similar patients, and determining the symptom similarity of each historical patient based on the scheme difference indexes. And recommending traditional Chinese medicine rehabilitation schemes to the to-be-tested patient in different modes. Accurate traditional Chinese medicine rehabilitation scheme recommendation can be performed for patients, and the accuracy of the traditional Chinese medicine rehabilitation scheme recommendation system is improved.

The invention discloses a coronary artery patient exercise tolerance treatment management system based on a random control test, and relates to the technical field of coronary artery patient exercise tolerance treatment management, and the coronary artery patient exercise tolerance treatment management system comprises a scheme recommendation module and an effect feedback module, the scheme recommendation module comprises a data acquisition unit, a scheme recommendation unit and an archive management unit; the data acquisition unit comprises a physiological index monitoring device. According to the coronary artery patient exercise tolerance treatment management system based on the random control test, the exercise tolerance improvement condition of a patient in the treatment process is evaluated through the definite physiological indexes, exercise performance indexes and subjective feeling indexes of the effect feedback module; multi-dimensional evaluation is achieved, and the movement effect and the body reaction of the patient can be comprehensively known; the exercise scheme of the patient can be optimized and adjusted according to the evaluation result and the health file of the patient, and a preferred exercise scheme is generated.

The invention discloses an intelligent remote follow-up visit method and system for tumor patients, and relates to the technical field of medical intellectualization, and the method comprises the steps: collecting follow-up visit data through a remote medical terminal, carrying out the processing of the follow-up visit data, obtaining a physical sign index sequence and a symptom feature vector, and generating a dynamic follow-up visit evaluation score through a time sequence prediction model; performing semantic analysis on the electronic medical record data of the patient based on the medical knowledge map, extracting tumor stage classification data, chemoradiotherapy measurement data, weight nutrition indexes and laboratory inspection data, determining a basic risk coefficient, a cumulative dose ratio and a body state score, and generating a baseline follow-up assessment score through a prediction model; and according to the dynamic follow-up evaluation score and the baseline follow-up evaluation score, calculating a comprehensive follow-up grade and generating a next follow-up plan. According to the invention, the accuracy of patient state evaluation is improved, accurate configuration of medical resources is realized, and the quality and efficiency of remote follow-up visit are effectively improved.

The invention discloses a traditional Chinese medicine nursing management system and method based on a cloud follow-up visit management platform, and belongs to the technical field of intelligent nursing. The management system comprises the following modules: a dynamic data acquisition module for acquiring physiological parameters, living habits and environmental data of a patient in real time and performing primary processing; the dynamic health archive construction module is used for constructing and continuously updating an individualized health archive of the patient based on the data collected in real time; the traditional Chinese medicine knowledge map and reasoning module depends on the traditional Chinese medicine nursing knowledge map and combines an intelligent reasoning technology to realize accurate traditional Chinese medicine syndrome differentiation and treatment analysis and automatically generate a personalized nursing scheme; the multi-dimensional patient feedback module is used for constructing a feedback mechanism fusing objective data, patient subjective feeling and medical care evaluation so as to extract key nursing effect information; and the nursing scheme iterative optimization module dynamically adjusts and optimizes the traditional Chinese medicine nursing scheme based on the feedback data, and realizes personalized and self-adaptive continuous improved nursing.

The invention discloses an apoplexy patient management method and system based on a multi-mode RAG and LLM model, and the method comprises the steps: obtaining the standardized electronic medical record data of a patient based on an FHIR standard electronic medical record; performing feature extraction on the electronic medical record data to obtain multi-modal data; integrating and representing a time-space relationship among the multi-modal data, and obtaining a dynamically updated dynamic knowledge graph; analyzing and integrating the electronic medical record data to obtain integrated information; constructing a stroke risk causal network based on an LLM model in combination with a causal reasoning mechanism; obtaining a personalized health report based on the dynamic knowledge graph and a stroke risk causal network; and based on a personalized report result and feedback data of the patient, a treatment scheme and a management strategy of the patient are optimized in real time. According to the application, the causal reasoning mechanism is embedded into the Transform architecture of the LLM, so that the modeling and prediction capabilities of the stroke risk causal relationship are enhanced.

The present invention relates to the technical field of medical triage, and discloses an emergency patient triage system based on artificial intelligence, including: a data acquisition module: used to obtain the patient's identity and basic information by performing face recognition and fingerprint recognition on the patient, and receive health data uploaded by a wearable device, and transmit it to the patient's corresponding historical pathology library; a medical interaction module: used to provide an interactive platform for patients to remotely input pathological conditions based on 5G wireless communication technology; a multimodal data fusion module; a pathological feature matching module; a triage matching module; and a system integration and display module. Through the convolutional neural network model combined with pathological feature matching, based on the patient's question reply content, the condition and the judgment of the severity, a rapid judgment of the possibility of the disease, the generation of consultation content and the optimization of triage results are achieved, thereby improving the accuracy and efficiency of triage and reducing misdiagnosis and missed diagnosis.

The invention relates to a data fusion processing method and system for an intelligent sensor based on MEMS, and the method comprises the following steps: carrying out the non-stationary signal decomposition of a multi-modal physiological parameter time sequence data stream, and obtaining a key vital sign feature sequence; performing phase coupling degree calculation on the key vital sign feature sequence to obtain an inter-organ function correlation intensity matrix; performing high-dimensional feature space projection on the critical patient based on the inter-organ function association intensity matrix to obtain a pathological state feature map of the patient; performing nonlinear kinetic analysis on the pathological state characteristic spectrum of the patient to obtain a disease progress dynamic trajectory; and performing clinical decision support reasoning based on the disease progress dynamic trajectory to obtain personalized treatment suggestions, thereby solving the technical problem that the existing analysis method is often limited to observation of a single physiological parameter or simple statistical analysis and lacks the ability to understand the disease mechanism from the overall perspective.

The invention discloses an exercise rehabilitation assessment method and system based on limb posture and emotion recognition, and the method comprises the steps: collecting a motion video, recording the age and gender information of a patient, constructing a self-made data set, defining a candidate region containing the rehabilitation motion of the patient, constructing a basic motion posture data set, and carrying out the recognition of the rehabilitation motion of the patient based on a posture estimation algorithm. Personalized limb skeleton key points are extracted, and emotion features are obtained through face key point detection and face action unit analysis; analyzing the motion trail of the knee joint based on the personalized limb skeleton key points, and extracting limb posture information; and carrying out feature fusion on the limb posture information and the emotional features to form a quantitative rehabilitation evaluation result. According to the invention, high-precision capture of the key points of the human skeleton is realized through computer vision and pattern recognition technologies. In addition, in combination with analysis of the emotional state of the patient, the evaluation accuracy is enhanced, and the rehabilitation training effect evaluation is more comprehensive and accurate.

Novel tools and techniques are provided for implementing intelligent assistance (“IA”) ecosystem, and, in some cases, for implementing extended reality (“XR”) for cardiac arrhythmia procedures. In various embodiments, a computing system might receive device data associated with a device(s) configured to perform a cardiac arrhythmia procedure to provide effective heart rhythm, might receive imaging data associated with an imaging device(s) configured to generate images of a portion(s) of a patient. The computing system might analyze the received device data and imaging data (collectively “received data”), might map the received data to a 3D or 4D representation of the portion(s) of the patient based on the analysis, and might generate and present (using a user experience (“UX”) device) one or more XR images or experiences based on the mapping.

The invention discloses a neurology patient rehabilitation nursing method based on multi-modal data analysis, and relates to the technical field of medical health, and the method comprises the steps: extracting neural function features through a multi-modal data fusion algorithm, and carrying out the calculation through a weighted fusion and statistical analysis method, and obtaining a neuroplasticity index vector; combining the neuroplasticity index vector with the unified multi-modal feature representation, carrying out multi-modal abnormal mode recognition analysis, obtaining a rehabilitation risk early warning signal and a personalized intervention suggestion, and generating a personalized rehabilitation scheme; performing dynamic optimization and self-adaptive adjustment on the individualized rehabilitation scheme by using a feedback loop mechanism to generate an optimized individualized rehabilitation scheme; physiological signals in individualized rehabilitation training based on the optimized individualized rehabilitation scheme are collected in real time and preprocessed, and a standardized physiological response data sequence is generated. According to the invention, the scientificity, timeliness and individual adaptability of regulation and control are improved, so that nerve function remodeling is accelerated and the rehabilitation risk is reduced.

Example solutions for real-world evidence generation using artificial intelligence models and performing trial simulations include: training a large language model (LLM) to receive medical documents that include medical text associated with a patient output predicted values for medical attributes of the patient based on the medical text; performing attribute extraction from structured medical documents, including extracting values for a first plurality of attributes associated with the plurality of patients; performing attribute extraction from a plurality of unstructured medical documents of the plurality of patients using the LLM, including extracting predicted values for a second plurality of attributes associated with the plurality of patients; and performing a survival model simulation that computes estimations of hazard ratio (HR) between cases and controls using real-world data of the plurality of patients extracted in the first attribute extraction and second attribute extraction.

A digital healthcare architecture including a cloud-based virtual clinic platform configured to facilitate remote therapy of one or more patients based on biometric authentication systems and methods. In an example arrangement, one or more biometric indicia of a user (e.g., a clinician and / or a patient) as well as one or more non-biometric input factors may be used in authenticating the user prior to granting access to a protected resource or application (e.g., a therapy application executing on a UE device) configured for effectuating remote programming of an implantable medical device (IMD) operative to provide therapy to the patient.

The invention belongs to the technical field of hospital triage systems, and provides an outpatient and emergency triage system, comprising: an information collection module collecting basic information and symptoms of a patient as original data, and preprocessing the data; the intelligent triage module classifies and predicts symptom data input by a patient through a medical knowledge graph and a machine learning algorithm; the triage result output module allocates departments to the patients according to the classification and prediction results of the intelligent triage module, and predicts the waiting time of the patients based on historical data; the interaction module displays and informs the patient according to the distribution information of the triage result output module and the predicted waiting time; through the intelligent triage module, the severity of the illness state of a patient can be rapidly judged, critical patients can be processed preferentially, so that distribution of medical resources is optimized, the waiting time of the patient in a hospital can be shortened through prediction and scheduling of waiting time of the patient, and the treatment efficiency is improved.

The invention discloses a medical intelligent dialogue method based on deep learning, relates to the technical field of intelligent medical treatment, constructs a rare disease knowledge graph, and deduces implicit relations between diseases and symptoms through association relations in the graph even under the condition of insufficient training data. The ability of processing complex cases such as rare diseases is greatly enhanced, a more accurate rare disease list can be provided through a map reasoning mechanism, related diagnosis schemes are reasonably and preferentially displayed to a user, the misjudgment risk is reduced, and multi-source data such as medical record texts, medical images and laboratory detection results are effectively integrated through a multi-mode Transform model, so that the accuracy of diagnosis is improved. According to the method, a unified comprehensive feature representation is generated, the ability of comprehensively analyzing the health condition of the patient from different angles is deeply fused, the diagnosis accuracy is improved, the inquiry sequence and the problem depth can be adjusted according to the specific illness conditions of different patients based on the preliminary diagnosis result, and more accurate and personalized inquiry experience is provided.

The invention provides a dynamic myopia management lens prescription customization method and system, and relates to the technical field of lens design and customization. According to the dynamic myopia management lens prescription customization technology provided by the invention, ophthalmic basic data of a patient is acquired; then periodically follow-up the patient to obtain ophthalmology measurement follow-up data of the patient; the joint evaluation model dynamically updates a myopia risk evaluation grade of the patient based on ophthalmology basic data and ophthalmology measurement follow-up visit data; and finally, dynamically updating the lens prescription based on the myopia risk assessment level of each time by the joint assessment model. According to the technical scheme, the combined evaluation model can perform dynamic lens prescription adjustment according to follow-up visit ocular axis progress and choroid change conditions, dynamic myopia management lens prescription customization, whole-course tracking of a myopia cycle and individualized prevention and control management are realized, and thus the effectiveness and continuity of myopia prevention and control are improved.

The invention relates to a personalized medical decision-making method based on artificial intelligence, and relates to the field of medical health and artificial intelligence, and the personalized medical decision-making method comprises the following steps: S1, inputting medical data of a patient in a patient information input module, including historical cases, examination reports, genomic sequences and real-time health monitoring data; and S2, based on the patient information, the patient health comprehensive evaluation module evaluates the health state of the patient, and the medical decision module outputs a preliminary treatment scheme according to the evaluation result. By collecting comprehensive medical data of a patient and comprehensively evaluating the health state of the patient based on the data, the illness state of the patient is more accurately grasped, a more accurate treatment scheme is made, individual differences, including age, physiological indexes, psychological conditions and disease duration, of the patient are fully considered, and by calculating the comprehensive evaluation score of the patient, the patient health state is comprehensively evaluated. The timeliness and effectiveness of a treatment scheme are ensured, and the rehabilitation speed of a patient is increased.

The invention discloses a senile weakness and Parkinson's disease multi-dimensional evaluation system based on an intelligent single-chip microcomputer, and belongs to the technical field of disease evaluation. The senile weakness and Parkinson's disease multi-dimensional evaluation system comprises a data acquisition module used for acquiring patient multi-dimensional real-time data based on artificial intelligence; the data transmission module is used for uploading the multi-dimensional real-time data of the patient based on artificial intelligence to the multi-dimensional evaluation terminal; and the multi-dimensional evaluation terminal is used for processing, analyzing and evaluating the multi-dimensional real-time data of the patient based on artificial intelligence, and determining a multi-dimensional evaluation result of senile weakness and Parkinson's disease based on the intelligent single-chip microcomputer. The problems that real-time monitoring and multi-dimensional evaluation cannot be carried out on senile weakness and Parkinson's disease based on an intelligent single-chip microcomputer in the prior art, and disease deterioration cannot be effectively prevented are solved. Real-time monitoring and multi-dimensional evaluation can be performed on senile weakness and Parkinson's disease on the basis of the intelligent single chip microcomputer, the illness state of a patient can be mastered in time, the patient can be tracked and treated in time, disease progression is slowed down, and deterioration of the illness state can be effectively prevented.

The invention discloses a coordination system and method for intelligent appointment treatment of daytime chemotherapy, and relates to the technical field of medical information, and the system comprises an information processing unit which is used for obtaining the medical information of a target patient, constructing a comprehensive diagnosis and treatment information model comprising the diagnosis and treatment parameters of the patient, and generating an initial diagnosis and treatment node sequence based on the comprehensive diagnosis and treatment information model; the optimization unit is used for receiving the initial diagnosis and treatment node sequence from the information processing unit, adjusting the priority of each node in the initial diagnosis and treatment node sequence according to a first influence factor, and generating a diagnosis and treatment path after reordering; and the management unit is used for generating a daily chemotherapy appointment strategy and a follow-up visit plan of the target patient based on the diagnosis and treatment path sent by the optimization unit, and sending reminding content containing appointment key information through a patient terminal. Through multi-dimensional priority comprehensive evaluation and a dynamic adjustment mechanism, resource utilization and diagnosis and treatment paths of daytime chemotherapy are optimized, intelligent appointment and real-time response are realized, and treatment efficiency and patient experience are improved.

The invention relates to a pressure injury intelligent prediction and prevention method and system, and the method comprises the steps: determining target comprehensive monitoring data of a patient, the target comprehensive monitoring data comprising microenvironment data indicating body surface temperature, body surface humidity and pressure on a pressed part, and individual feature data indicating the gender, age and whether the patient suffers from diabetes; based on the comprehensive monitoring data, a machine learning model based on ensemble learning is adopted for analysis to obtain a risk score, the machine learning model comprises a plurality of basic learners, and each basic learner performs performance evaluation in advance based on a method of combining cross validation and feature importance evaluation; corresponding weights are distributed according to scores of the performance evaluation indexes, weighted average combination is carried out based on prediction results of all the basic learners, and a prediction result of the integrated model can be obtained; and performing quantitative evaluation on the pressure injury occurrence risk of the pressed part of the patient based on the risk score, and generating a risk report.

The invention discloses a tumor needle biopsy robot and an image real-time navigation system thereof, and relates to the technical field of medical instruments, and the method comprises the following steps: extracting historical data, and constructing a segmentation model according to the historical data; acquiring a plurality of medical images of the patient, and constructing a three-dimensional model of the patient according to the plurality of medical images; constructing a visual puncture path according to the three-dimensional model; the method comprises the following steps: acquiring optical tracking data, acquiring a needle point position according to the optical tracking data, correcting a puncture path according to the needle point position, acquiring a path point sequence according to a puncture starting point, a target point, a cost function and a graph structure when an optimal path is planned, and continuously adjusting the path through a path optimization algorithm. The path is far away from key tissues such as important blood vessels and nerves, the path is prevented from excessively approaching or directly penetrating through a dangerous area, so that the rationality of optimal path planning is ensured, the precision controllability of the puncture process is enhanced, the puncture path is corrected in real time through the position of the needle tip, and the possibility of the scheme is further improved.

The invention discloses an examination item scheduling system and method based on patient examination demand optimization, and relates to the technical field of medical first aid, and the system comprises an information obtaining module which is used for obtaining the basic information and the emergency examination demand of a patient; the priority evaluation module is used for evaluating the priority of the patient based on the vital signs and the disease severity of the patient; the scheduling generation module is used for generating an optimized examination item scheduling table so as to ensure that high-priority patients are examined preferentially; the state monitoring module is used for monitoring the state of the patient in real time and dynamically adjusting the examination item scheduling table according to the state change, and the result sending module is used for sending the final examination item scheduling table to the corresponding medical equipment and the doctor terminal; according to the examination item scheduling system and method based on patient examination demand optimization, the problem that in the prior art, when critical patients in the emergency department are rapidly shunted, first-aid response is slow due to priority judgment errors is solved.

The embodiment of the invention discloses a new infectious disease monitoring and early warning method and device based on multi-point triggering. The method comprises the following steps: acquiring clinical data of a patient in HIS, LIS, PACS, operation and electronic medical records from a medical institution; setting a screening strategy of new infectious diseases; identifying suspected cases according to the clinical data of the patient; identifying abnormal diagnosis and treatment behaviors of the patient from clinical data of the patient based on big data analysis; determining a suspected case level according to the suspected case and the abnormal diagnosis and treatment behavior; obtaining relevant survey data of suspected cases; judging whether the suspected cases have aggregated cases or not according to the related survey data of the suspected cases; and if the suspected cases have aggregated cases, determining an infectious disease transmission route, and performing case management. By implementing the method provided by the embodiment of the invention, real-time monitoring and early warning of infectious disease epidemic situations and other public health events can be realized, and the processing efficiency and accuracy of new infectious diseases are improved.

Methods for training and using a machine learning model for predicting a likelihood of an event occurring in a patient having a malignant tumour are provided. The method of using the machine learning model includes inputting a pathology image of a portion of a malignant tumour of a patient and one or more categorical risk factors associated with the malignant tumour or the patient. The method also includes extracting, from the pathology image, one or more features relating to one or more properties of the malignant tumour. The method further comprises determining a predicted likelihood of an event occurring in the patient based on the extracted features and the one or more categorical risk factors. The method additionally includes outputting the predicted likelihood.

The invention discloses a medical image analysis method and system based on plain-scan CT data, and relates to the technical field of medical image analysis. According to the medical image analysis method based on plain-scan CT data, chest plain-scan CT data of a patient are obtained, and the chest plain-scan CT data comprise chest cross section image data of a plurality of scanning positions; establishing a three-dimensional model of the chest of the patient based on the chest plain-scan CT data of the patient; the three-dimensional model of the chest of the patient is segmented, and the three-dimensional model of the lung of the patient is obtained.According to the method, the three-dimensional model of the healthy lung is automatically reconstructed through the deep learning model and compared with the actual lung model of the patient, so that more accurate disease diagnosis can be provided, manual errors are reduced through automatic comparison, and the accuracy of disease diagnosis is improved. The analysis efficiency is improved, so that a doctor can quickly and accurately identify a lesion area and a disease degree, and a more effective treatment plan is made.