555 results about "Patient data" patented technology

Systems and Methods are disclosed for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand / supply related cardiac ischemia. The system may have both implanted and external components that communicate with a Physicians's programmer, and smart-devices for monitoring and alerting to detected medically relevant events or states. At least one processor provides event detection using statistical threshold criteria calculated upon at least a portion of a patient's data / distributions and set for a patient or based upon what a doctor determines as abnormal for a patient. Cardiovascular condition is tracked using histogram, trend, and summary information related to heart rate and / or cardiac features such as S-T segment measures of heartbeats. Heartbeats with elevated rates, and below a “high” range, provide medically relevant detections including medication non-compliance. Novel methods of power management and patient monitoring are disclosed.

The invention discloses an interpretability analysis and decision sharing verification method and system for a rectal cancer prognosis model, and relates to the field of medical artificial intelligence interpretability. The method comprises the following steps: carrying out gradient weighting class activation mapping analysis on a prognosis model to generate an image thermodynamic diagram; calculating the contribution degree of the multi-modal features by using an SHAP interpreter; an integrated visual interface is constructed, and patient data, model prediction and the explanation result are presented to a doctor together; the doctor performs independent risk assessment based on the interface information; finally, decisions of doctors and the model are compared, and model auxiliary efficiency is evaluated. Through a doctor-model decision sharing verification mechanism which is explained and innovated in a multi-level mode, the transparency and clinical credibility of the complex AI prognosis model are remarkably improved, the value of time sequence data in dynamic risk assessment can be verified, and clinical landing application of the AI model is powerfully promoted.

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 discloses a venous thrombosis risk assessment method based on a large language model, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: collecting thoracic surgery diagnosis and treatment data of a patient, carrying out the space-time alignment, generating a standard diagnosis and treatment data flow, and carrying out the homomorphic encryption of the standard diagnosis and treatment data flow, and forming an encrypted patient data package; inputting the encrypted patient data packet into a multi-task large language model, performing feature extraction and semantic coding by a feature coding layer, performing time sequence modeling and risk probability calculation by a risk quantification layer, and outputting a venous thromboembolism risk level of a patient; and performing feature decoupling and potential space mapping on the encrypted patient data packet to obtain thrombus semantic potential features. Through the multi-task large language model, the dual machine learning algorithm and the homomorphic encryption, the accuracy of venous thrombosis risk early warning is improved, and the safety of the risk assessment process is enhanced.

The invention relates to the technical field of medical artificial intelligence, and discloses a non-small cell lung cancer immunotherapy risk assessment method and system and a storage medium, and the method comprises the steps: constructing an optimal strategy tree model capable of dividing a patient into a plurality of subgroups based on a historical patient data set, and determining an initial optimal treatment strategy for each subgroup; determining a subgroup to which a new patient belongs and an initial treatment strategy thereof according to the model; in the treatment process, the deviation degree between the individual response trajectory of the patient and the average response trajectory of the subgroup to which the individual response trajectory belongs is calculated; and when the deviation degree exceeds a preset threshold value of the subgroup, triggering an adaptive adjustment mechanism, and outputting an updated treatment suggestion. By establishing a dynamic monitoring and closed-loop feedback mechanism based on the subgroup benchmark, the technical problem that an existing scheme is difficult to carry out prospective identification and dynamic intervention on individualized risks is solved, so that the accuracy, safety and individualized level of non-small cell lung cancer immunotherapy are improved.

Systems and methods for designing and implementing patient-specific surgical procedures and / or medical devices are disclosed. In some embodiments, a method includes receiving a patient data set of a patient. The patient data set is compared to a plurality of reference patient data sets, wherein each of the plurality of reference patient data sets is associated with a corresponding reference patient. A subset of the plurality of reference patient data sets is selected based, at least partly, on similarity to the patient data set and treatment outcome of the corresponding reference patient. Based on the selected subset, at least one surgical procedure or medical device design for treating the patient is generated.

The invention discloses a literature metrology and clinical data fusion lung cancer knowledge graph construction method and device, which can solve the problems of research and clinical disjunction, data fragmentation and AI'black box ', and realize systematic mining, verification and interpretable application of lung cancer diagnosis factors. The method comprises the following steps: (1) mining high-frequency and emerging diagnosis factors from scientific research literatures through a multi-dimensional algorithm, and generating academic prior weights; (2) enabling the literature candidate factors to correspond to real patient data, and performing structured matching to obtain an aligned clinical candidate factor set; (3) screening out a core factor having a reliable causal relationship with the lung cancer outcome from the clinical candidate factors, and providing a causal-driven skeleton for the knowledge graph; (4) establishing an initial knowledge graph based on D3 and G, and dynamically updating an edge weight based on newly added data; and (5) outputting a clinical interpretable report which comprises a puncture score Pbiopsy, driving factor contribution degree ranking, an evidence chain and a recommended path.

The present invention relates to an AI-based system and method for generating enhanced radiology reports. The system comprises a database for storing multimodal patient data, a natural language processing (NLP) module for extracting clinical information, and a machine learning module for correlating the clinical information with radiology images to identify diagnostic insights. An AI-based report generation module analyzes the images and clinical information to generate a preliminary report, which is refined based on radiologist input. The generated report is then integrated into the patient's electronic health record. The system employs techniques such as multimodal deep learning, active learning, explainable AI, and federated learning to enhance diagnostic accuracy, capture expert feedback, provide transparency, and enable multi-institutional collaboration. The invention aims to improve the accuracy, efficiency, and value of radiology reporting in patient care.

Systems and methods for de-identifying patient data are disclosed herein. In some embodiments, a method for de-identifying patient data includes receiving a patient record including one or more identifiers. The method can include generating a first de-identified record from the patient record using a first de-identification process. The first de-identification process can be configured to produce a first re-identification risk score. The method can further include receiving a request from a data recipient to access the first de-identified record. The method can also include generating a second de-identified record from the first de-identified record by using a second de-identification process. The second de-identification process can be configured to produce a second re-identification risk score lower than the first re-identification risk score.

The invention relates to the technical field of intelligent medical treatment and clinical nutrition, and discloses a multi-dimensional sign sensing personalized enteral nutrition infusion regulation and control device which comprises a multi-parameter sign sensing module, an intelligent analysis control module, an adjustable infusion execution module and a man-machine interaction and early warning module. The intelligent nutrition management platform is in communication connection with the modules; the intelligent nutrition management platform is composed of a nutrition screening and evaluation unit and a statistical analysis unit. According to the method, a full link from nutrition risk automatic screening, individualized scheme intelligent generation, bedside safety execution and self-adaptive regulation and control to whole-process data statistical analysis is formed; information integration and initial decision making are automatically completed, manual operation links and cognitive loads are reduced, scientificity and normalization of a treatment scheme based on newest clinical guidelines and patient data are ensured, and medical workers are liberated from tedious transactional work and focus on higher-value clinical judgment.

Various embodiments include methods for generating patient-specific heart and thorax models using anatomical landmarks and segmentation data, optimized through the application of trained neural network models. Three-dimensional medical imaging data may be processed by a trained neural network to automatically segment and isolate the heart, blood cavities, and thorax to extract feature maps from the segmented images. Anatomical landmarks, such as the heart apex and valve centers, are identified and the alignment of heart axes is verified. Reference heart and thorax models are selected and adapted to fit the patient-specific landmarks through scaling, translating, and rotating. Best adapted heart and thorax models may then be used for conducting one or more medical procedures. Neural network models, trained on historical patient data sets, may be refined through machine learning from new patient data, thereby improving accuracy.

The invention relates to the technical field of chronic disease health management, and discloses a chronic disease health management method based on data analysis, and the method comprises the following steps: S1, obtaining medical records, wearable device data, laboratory results, self-reporting information and environment data of a patient through a multi-source collection gateway; and S2, performing desensitization processing and feature extraction on the data under a federated learning framework, and generating a multi-modal patient portrait containing time sequence, spatial features and biomarkers. According to the chronic disease health management method and system based on data analysis, through desensitization processing under a federated learning framework, on the premise of protecting patient data privacy, medical records, wearable device data, environment data and other multi-source information are effectively integrated, a comprehensive multi-modal patient portrait is formed, and the patient experience is improved. The problem that data are mutually separated in a traditional management mode is solved, and comprehensive data support is provided for subsequent risk prediction and intervention decision making.

A surgical computing system may receive measurement data from at least one sensing system. The measurement data may be associated with a set of patient biomarkers of a patient. A surgical computing system may obtain a set of patient parameters associated with the patient. A surgical computing system may generate vectorized patient data based on at least the measurement data and a set of patient-specific parameters. A surgical computing system may use a predictive model to predict an occurrence of a prolonged air leak (PAL) based at least on the vectorized patient data. A surgical computing system may generate a set of recommendations for preventing the PAL.

The invention discloses an AI-based atrial fibrillation patient health risk prediction method, and belongs to the technical field of medical information, and the method specifically comprises the steps: receiving a physiological parameter record and an intervention treatment record of an atrial fibrillation patient, carrying out the time sequence alignment operation, and forming structured patient data; extracting risk-related features from the structured patient data, wherein the risk-related features are divided into basic risk features and intervention response features; inputting the basic risk features into a basic risk prediction network, and outputting a basic risk score; inputting the intervention response characteristics and the basic risk score into an intervention effect separation network together to generate a post-intervention risk score; calculating a net effect value of treatment intervention according to the difference between the basic risk score and the post-intervention risk score; combining the basic risk score, the post-intervention risk score and the net effect value to generate a patient individualized long-term risk prediction trajectory; and accurate prediction of the long-term health risk of the atrial fibrillation patient is realized.

The invention relates to the technical field of computers, discloses a data sharing method and system for patients suffering from metabolic acidosis, and aims to solve the problem of out-of-control safety caused by extensive authority, bare transmission and traceless behaviors in cross-institution medical data circulation. A dynamic authority strategy based on roles and scenes is constructed by extracting and standardizing patient diagnosis and treatment data from a hospital system; end-to-end encrypted transmission is realized through bidirectional authentication and a temporary session key; establishing a three-layer block chain type audit log tracking access, a secret key and a data flow direction; dynamic desensitization, quality verification, cache acceleration and intelligent contract extension mechanisms are integrated, and a visual authority topology monitoring and credit scoring system is supplemented. According to the technical scheme, data field-level accurate authorization, encryption and anti-theft in the whole transmission process, traceability in the whole operation period and use compliance self-feedback are achieved, and the scientific research value of clinical data is released to the maximum extent while the privacy safety of a patient is effectively guaranteed.

The invention relates to the technical field of gastrointestinal tumor chemotherapy risk assessment, and particularly discloses a gastrointestinal tumor chemotherapy risk scoring model and a construction method thereof, and the method comprises the steps: obtaining the individualized feature data and basic physiological data of a historical patient, and constructing an individualized difference library with a unique ID, and a core influence library; the method comprises the following steps: standardizing and coding double-library data, synchronizing clinical data containing treatment effect codes, forming a data dictionary, acquiring and coding current gastrointestinal tumor patient data, and matching the data dictionary to judge individual difference abnormity; when the first data set is abnormal, constructing a first data set, screening core independent variables through Logistic regression, and constructing a scoring model by using an LSTM (Long Short Term Memory) model; when no abnormity exists, redundant codes are removed to obtain a second data set, and modeling is conducted through the same method. Through double-library linkage and scene-divided modeling, the three-level toxicity risk prediction precision is improved, data support is provided for clinical chemotherapy dose adjustment and toxicity prevention, and the risk of excessive treatment or insufficient treatment is reduced.

A system for real-time prediction of heart disease and to support clinical decisions; the system includes: a patient data acquisition module configured to receive multimodal inputs, including physiological signals selected from electrocardiographic waveforms, blood pressure readings, heart rate readings, and oxygen saturation readings, as well as demographic and lifestyle information, including age, gender, cholesterol levels, smoking habits, and family medical history; a preprocessing engine configured to perform data cleansing, imputation of missing values, categorical coding, and feature scaling, so that the input data is normalized and converted into a format suitable for machine learning; a processing core comprising a multi-core CPU / GPU system-on-chip operationally coupled with a secure storage unit, wherein the processing core is configured to execute a variety of pre-trained machine learning models, including Naive Bayes, Random Forest, Logistic Regression and Decision Tree classifiers; a model evaluation unit configured to calculate validation metrics such as precision, recall, F1 score and area under the curve for each of the models and dynamically select the model with optimal performance to generate real-time predictions for the risk of heart disease; a display interface configured to present predictive results in the form of risk probability values, confidence indices, and actionable recommendations that are mapped to clinical treatment guidelines; and a communication interface configured to transmit emergency alerts based on high-risk predictions to remote caregivers, hospitals, and emergency response systems via wireless communication protocols such as WLAN, Bluetooth, and 4G / 5G cellular connections.

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 system brings together patient data both clinical records and imaging studies from disparate sources to the user workstation or mobile device in real-time and on-demand. In order to do so, the system needs to establish application layer connectivity utilizing HL7 or FHIR and DICOM for imaging. Once a secure connection is established, the system is able to search and retrieve records and present it to end user.

According to an embodiment, disclosed is a system comprising a processor configured to receive an image of a breast of a patient and patient data comprising genetic data; extract features from the image and the patient data, using one or more machine learning models, wherein the features comprise a presence of a calcification and a calcification pattern to generate a breast calcification vector; augment the breast calcification vector with the genetic data; determine, using the machine learning models, a first risk for a breast cancer; a second risk to one or more organs of the patient, wherein the organs comprises one or more of heart, kidney, lungs, pancreas, and brain; predict, a third risk based on one or more of a healing response, a tumor flow and growth, inflammation and degeneration, a disease relapse, an adverse event, and a clinical response; and determine, an overall risk to the patient.

According to the M proteinemia diagnosis and prediction method and device based on multi-modal data fusion, the problems that effective adaptation cannot be achieved, the feature difference of heterogeneous multi-modal data is large, and no-label data is difficult to apply can be solved, and the data utilization rate and the accuracy of M proteinemia diagnosis and illness state prediction are improved. The method comprises the following steps: (1) acquiring M proteinemia patient data, and combining other label-free IFE images to form a sample set; (2) performing data preprocessing on the sample set; (3) constructing an auxiliary task to pre-train an IFE image feature extractor by using the preprocessed labeled and unlabeled IFE image samples; meanwhile, feature engineering is designed for the clinical examination index values preprocessed in the step (2) according to the correlation between the examination indexes; (4) constructing a negative and positive 2 classifier; (5) constructing a positive feature classifier; and (6) constructing a patient condition predictor.

The invention discloses a recruitment strategy generation method and device and a related product. Acquiring a patient recruitment condition, a plurality of to-be-recruited patients and patient data sets corresponding to the plurality of to-be-recruited patients; based on the patient recruitment condition, screening the plurality of to-be-recruitment patients and the patient data sets corresponding to the plurality of to-be-recruitment patients to obtain a candidate patient set meeting the patient recruitment condition in the plurality of to-be-recruitment patients; for each candidate patient in the candidate patient set, performing calculation processing based on the patient data set corresponding to the candidate patient to obtain a patient recruitment score corresponding to the candidate patient; performing calculation processing based on the plurality of patient data sets corresponding to the candidate patient set to obtain a patient set recruitment score corresponding to the candidate patient set; and generating a patient recruitment strategy based on the patient set recruitment score and the patient recruitment scores corresponding to the candidate patients, the patient recruitment strategy being used for determining recruited patients. In this way, recruitment accuracy is improved.

The invention discloses an intelligent patient data management method based on the general surgery department, and relates to the technical field of medical information processing, and the method comprises the steps: a multi-source patient data comprehensive collection step: collecting full-process multi-type data through four modes, and dynamically adjusting the collection frequency according to the data type; a data standardization preprocessing step: processing the data by adopting a cleaning algorithm; a structured storage and intelligent index construction step, wherein classified storage is carried out based on a mixed storage architecture; an intelligent data analysis step based on machine learning: constructing a multi-task model; a personalized medical intervention scheme generation step: generating a scheme in combination with an analysis result and a clinical guide; a data dynamic updating and real-time monitoring step: updating data in real time and monitoring key indexes; and a hierarchical authority management and data security guarantee step: distributing authorities according to roles. The method improves the integrity and efficiency of general surgery department patient data management, guarantees the data security and privacy, reduces the medical risk, and improves the rehabilitation effect of the patient.

One or more instruments generate test result data. The test result data include patient data and QC data. The test result data is provided to a QC data flow system via a local network, which filters the test result data (e.g., using a set of rules) to extract the QC data. The QC data is provided to a cloud-based QC data management platform via an external network. The cloud-based QC data management platform analyzes the QC data and provides a result back to the QC data flow system. The QC data flow system forwards the result to middleware or the instrument, which triggers a corrective action based on the result as appropriate.WO

The application discloses an evaluation method for predicting the safety of EGFR TKIs monotherapy for NSCLC. The method comprises the following steps: obtaining the data of a plurality of patients receiving EGFR TKIs treatment; taking MDRAE (maximum grade of drug-related adverse event) as a safety index, constructing a safety evaluation final model, and the final model is an ordered logistic regression model, the covariates of the final model include normalized steady-state trough concentration, EGFR TKIs treatment history, gender, baseline glutamyltransferase, baseline uric acid, baseline creatine kinase, baseline platelets and baseline lymphocytes; obtaining the data of a target patient, inputting the numerical values of each covariate in the data of the target patient into the final model; determining the first probability of MDRAE of the target patient being grade 3 and above, the second probability of MDRAE being grade 2 and the third probability of MDRAE being grade 1 and below according to the final model, and taking the grade corresponding to the maximum probability in the three probabilities as the predicted grade of MDRAE of the target patient. The application improves the accuracy of predicting the safety of EGFR TKIs monotherapy for NSCLC.

The invention discloses a genetic disease pathogenic site sorting and diagnosis auxiliary method and system based on multi-modal artificial intelligence. The method comprises the following steps: firstly, collecting genetic disease data to construct a heterogeneous knowledge graph; the method comprises the following steps: acquiring patient data, and executing an analysis process: generating multi-modal feature representation of candidate pathogenic sites from four dimensions of variation features based on rules, a tissue specificity mechanism, a protein three-dimensional structure and real-time literature evidence; carrying out fusion sorting on the features by using a sorting model, and generating an interpretable report and a clinical examination suggestion based on the uncertainty of a sorting result; and after the doctor executes examination according to the suggestion and feeds back newly added data, the analysis process is repeated until a preset iteration target is achieved. According to the method, the limitation of a static analysis model is broken through, multi-dimensional evidence fusion and clinical workflow embedding are realized, and the interpretation accuracy and diagnosis efficiency of the pathogenic site of the genetic disease are remarkably improved.

Optimized bio-synchronous drug delivery begins with establishing a bio-synchronous treatment protocol that incorporates individual temporal and innate biological characteristics into a pharmacological treatment plan. The bio-synchronous treatment protocol is thereafter initiated using bioactive agent delivery device. Bio-synchronous drug delivery includes continual collection of patient data such as physical, psychological, temporal and environmental characteristics. This data is analyzed so to not only determine an initial treatment protocol but to also determining whether modification to the ongoing bio-synchronous treatment protocol is required. And, responsive to determining a modification is required the system modifies the bio-synchronous treatment protocol and use of delivery device. These modifications and treatment protocols can include reactive and proactive psychological support supplied to the patient in a variety of formats.

The invention relates to the technical field of intelligent medical treatment, and discloses a pancreatic cancer evolution simulation and dynamic intervention system based on multi-model driving, and the system comprises a multi-source data fusion module which is used for accessing and processing patient data, and forming a multi-dimensional feature set; the model fusion construction module is integrated with a model coupler and can construct a patient-specific integrated calculation model; the twinborn calibration generation module is used for generating pancreatic cancer digital twinborn matched with the initial state of the patient; and the evolution simulation and intervention module is used for running the pancreatic cancer digital twins, simulating the natural evolution trajectory of the tumor and carrying out quantitative simulation on the input virtual treatment scheme effect. According to the method, by constructing the patient-specific digital twins, normal form transformation from static diagnosis to dynamic prediction is achieved, and a quantitative evaluation basis can be provided for selection of clinical treatment schemes.

The disclosure provides a computer-implemented method of determining a medical treatment for a patient. The method comprises: obtaining a patient data set; applying a set of eligibility criteria to patient values to determine whether the patient is eligible for the medical treatment; obtaining further patient values and reapplying the eligibility criteria; obtaining, in response to a determination that a status of diagnosis data in the medical records system has changed, an indication of a diagnosis of a medical condition for the patient; and, in response to the indication that the patient has been diagnosed with the medical condition, and that the patient is eligible for the medical treatment, providing, through a user interface layer or through a network interface connection, an output signal to direct a clinician to provide the patient with the medical treatment.

Systems, methods, and computer-readable media for providing a decision support solution to medical professionals to optimize medical care through data monitoring and feedback treatment are provided herein. In another embodiment, a computer-implemented method for modeling patient outcomes resulting from treatment in a specific medical area includes receiving patient-specific data associated with a patient, determining a plurality of possible patient states under which the patient can be categorized, a current patient state under which the patient can be categorized and determining probabilities of the patient transitioning from any of the possible patient states to every other possible patient state.