17 results about "Patient classification" patented technology

Provided is a process including: accessing clinical trial data of an ongoing clinical trial that is not yet complete, the clinical trial having a plurality of treatment groups and a plurality of patients in the treatment groups; detecting, with an anomaly detection model, an anomaly in the clinical trial data for a first patient, the anomaly corresponding to a first patient among the plurality of patients; in response to detecting the anomaly classifying the first patient as anomalous in the trial; generating, with the computer system, by an artificial intelligence model, a candidate explanation for the detected anomaly by designating data in a record for the first patient as potentially correlated with the anomaly; and causing the anomaly and the candidate explanation to be presented to a data safety monitoring board (“DSMB”) of the clinical trial.

The application discloses a patient classification method and device based on magnetic resonance parameters, equipment, medium, the method comprises the following steps: obtaining the target magnetic resonance parameter of the target patient; the target magnetic resonance parameter is selected from a plurality of preset magnetic resonance parameters based on the cardiac resynchronization label of the sample patient and the true result of the occurrence of the preset endpoint event; the target magnetic resonance parameter comprises a myocardial strain parameter, a gadolinium contrast agent delayed enhancement result and a left ventricular torsion parameter; the target classification tree is input into the target patient, and the target patient is classified by using the target classification tree, and the target classification result corresponding to the target patient is obtained; the target classification tree is obtained by extending the preset classification tree based on the cut-off value of each target magnetic resonance parameter and the influence of meeting each cut-off value on cardiac resynchronization; and the target classification result is output. The application can classify patients more accurately.

The vehicle first-aid medical method for guiding first-aid patient classification, treatment and hospital transfer according to the present application includes the following steps: a patient information registration unit receives necessary information about a patient in advance before getting on a vehicle or in the vehicle after getting on the vehicle; a patient symptom input unit receives the current symptoms of the patient in multiple ways; a first-aid treatment guideline providing unit provides a first-aid treatment method suitable for the patient based on the collected information; and a remote medical support unit supports real-time communication with a remote medical staff in the vehicle, so that the patient or a first-aid team member can obtain direct advice and guidance from a first-aid medical specialist.

The application provides an AI blood collection decompression grip ware system and an application method, and the system comprises a sensor matrix module, a main control processing module connected with the sensor matrix module, a voice interaction module connected with the main control processing module, and a feedback module connected with the main control processing module. The main control processing module constructs a three-dimensional grip curve surface model based on a pressure signal, calculates a blood vessel exposure index, dynamically adjusts a sensor weight according to patient classification, and forms a closed-loop control from signal collection to individualized intervention. The application reduces the workload of nurses through an automatic process, reduces the puncture failure rate through accurate grip evaluation, realizes accurate decompression guidance through clustering typing, and relieves psychological stress through multi-modal interaction. The standardized voice prompt and intelligent guidance reduce noise pollution in the blood collection area, improve the environmental comfort, and liberate nurses from repeated guidance, so that the nurses can pay more attention to core links such as sterile operation and improve the blood collection efficiency of the hospital.

Described herein are apparatuses and methods for classifying a patient as being asleep or awake. Such an apparatus can include an accelerometer and a processor. The accelerometer, alone or in combination with the processor, is used to determine an activity level of the patient and a posture of the patient. The processor is configured to classify the patient as being asleep in response to both (i) the posture of the patient being recumbent or reclined for at least a sleep latency duration, and (ii) the activity level of the patient not exceeding an activity threshold for at least the sleep latency duration; and classify the patient as being awake in response to at least one of (iii) the posture of the patient being upright for at least an awake latency duration, or (iv) the activity level of the patient exceeding the activity threshold for at least the awake latency duration.

Methods and systems to train and use an ensemble of artificial intelligence / machine learning (AI / ML) models to extract information from social determinants of health (SDoH), including training each of multiple dimensionality reduction models to reduce dimensionality of socio-demographic variables associated with a respective one of multiple SDoH categories, training a predictive model to predict a patient behavior for a geographic region (e.g., risk of non-adherence to treatment regimens) based on dimensionally reduced SDoH (alone or in combination with selected socio-demographic variables and / or other data), training a patient classification model to classify patients based on prescription transactions, and / or training a regional similarity model to determine a measure of similarity between geographic regions based on SDoH and / or dimensionally reduced SDoH. Also disclosed are techniques to visually represent outputs of the models on a user-interactive display.

This invention relates to the field of medical data management technology, specifically to a method for managing stressors in hypertensive patients based on meticulous nursing care. After identifying blood pressure stress points, this method quantifies the conformity of short-term stress stimuli to identify both short-term and chronic blood pressure stress points. The impact of short-term blood pressure stress on the patient is then obtained through the rate of change in systolic blood pressure. Complete chronic stress phases are determined through splitting and merging methods, and the similarity between different time periods is analyzed to obtain the chronic blood pressure stress impact on each patient. Patients can be classified based on these two stress characteristics. In the results of the first classification, the similarity of the complete chronic stress phases is further considered for further classification. This invention determines the short-term and long-term blood pressure stress characteristics of patients based on the trends in systolic and diastolic blood pressure, enabling meticulous patient classification and facilitating effective management and targeted nursing care for hypertensive patients.

There is provided a method of classifying a biological status of an individual. The method comprising: obtaining a biological sample from a patient; obtaining health-related information from the patient, said information including patient gender; analysing the sample to identify a quantity of each of 2 or more endogenous analytes in the sample; comparing the analyte quantities to reference data from healthy individuals to classify the patient as healthy, pre-diseased, at risk of disease or diseased for at least one health-related condition. The reference data includes data derived from a group of biological samples of individuals having the same gender as the patient and not having a need for medical treatment for a disease or illness, each biological sample of the group of biological samples having been analysed by the same process as used to analyse the patient sample, the process being monitored to maintain a predetermined level of consistency.

The invention relates to a diabetes adjuvant therapy cloud platform system based on an artificial intelligence model, and relates to the technical field of health-related information systems and cloud platforms, and the diabetes adjuvant therapy cloud platform system comprises a correlation analysis module which is used for determining a plurality of patient classification factors based on medical records of a plurality of historical diabetic patients, determining a plurality of diabetic patient types and the complication risk of each diabetic patient type; the feature extraction module is used for determining the type of the diabetic patient of the current patient according to the medical record of the current patient and the plurality of patient classification factors, and extracting complication risk features from the medical record of the current patient; the auxiliary treatment module is used for determining similar historical diabetic patients according to the complication risk characteristics of the current patient and determining an auxiliary treatment scheme of the current patient based on the auxiliary treatment scheme of the similar historical diabetic patients and the complication risk characteristics of the current patient, and the diabetes diagnosis and treatment efficiency and the treatment effect are improved.

The invention discloses a specific disease intelligent follow-up visit management system based on multi-model fusion and a patient grading method, and belongs to the technical field of medical informatization. The system comprises a data integration layer, an intelligent patient classification and grading module, a model fusion layer, a personalized follow-up visit content generation module, a follow-up visit state management module, a data synchronization and integration module and a statistical analysis layer, high / medium / low risk grading is achieved, and the model fusion layer comprises recurrence, re-admission, bleeding and compliance prediction models. The intelligent patient classifying and grading module is used for classifying and judging based on rules of diagnosis sets, operation markers, bedridden duration, tumor markers and heart and cerebral vessel markers, and calculating a total score by adopting a category adaptive weight and a multi-dimensional score; patient grading accuracy is improved by more than or equal to 90%, VTE recurrence prediction accuracy is improved by more than or equal to 85%, recurrence rate is remarkably reduced by 27%, and follow-up visit efficiency is improved by more than 20%.

The application discloses a kind of inpatient hierarchical management method and system, belong to medical risk management technical field.The method includes: obtaining the personal information of inpatient and determining its risk index;According to risk index, dynamic correction electronic fence;Through real-time acquisition patient position by wearable device, combine fence relationship to calculate risk index;While calculating vital sign index according to vital sign information;Comprehensive risk index and vital sign index determine early warning grade, and execute corresponding early warning prompt.The application realizes the change from passive patrol to active early warning through multi-dimensional risk fusion and dynamic fence adjustment, improves the timeliness and accuracy of high-risk patient monitoring, while reducing false positive rate and nursing interference.

The application discloses a patient large model classification method and system based on memory and retrieval enhancement, relates to the technical field of artificial intelligence, and inputs current patient information into a trained medical large model to output a classification result of the patient information; the training process of the medical large model is as follows: step one, different patient information is acquired to construct a training data set, and a corresponding knowledge instance of an i-th data in the training data set is obtained through a retrieval module; step two, each knowledge instance is subjected to a memory plug-in to obtain a knowledge instance representation; step three, the i-th data is input into a large language model, a gating mechanism is used to incorporate the knowledge instance representation corresponding to the i-th data into the information flow of the large language model, and thus the classification result of the i-th data is output; the patient large model classification method and system can dynamically adjust the used knowledge instance according to the specific condition of a patient, and thus the accuracy of patient classification is significantly improved.

The application discloses a personalized exoskeleton rehabilitation system and method based on consciousness and motor disorder patient grading, which comprises a comprehensive analysis platform, an electroencephalogram interface device, an electromyogram interface device and a modular exoskeleton combination. When applied, the patient wears the required device, connects the electroencephalogram signal collector through the electroencephalogram interface device, connects the electromyogram signal collector through the electromyogram interface device, sends the collected electroencephalogram signal and electromyogram signal to the comprehensive analysis platform for analysis, sends the control instruction to the controller according to the analysis result, and controls the modular exoskeleton combination to execute the corresponding personalized action combination. Through the integration of the comprehensive physiological signals of the patient and the dynamic evaluation mechanism, the accurate identification of the consciousness state and the motor ability of the patient is realized, the full-grade patient group from complete unconsciousness to basic autonomy can be covered, and the individual adaptability of the rehabilitation system is greatly improved. The application has good clinical popularization and productization potential.

Provided is a process including: accessing clinical trial data of an ongoing clinical trial that is not yet complete, the clinical trial having a plurality of treatment groups and a plurality of patients in the treatment groups; detecting, with an anomaly detection model, an anomaly in the clinical trial data for a first patient, the anomaly corresponding to a first patient among the plurality of patients; in response to detecting the anomaly classifying the first patient as anomalous in the trial; generating, with the computer system, by an artificial intelligence model, a candidate explanation for the detected anomaly by designating data in a record for the first patient as potentially correlated with the anomaly; and causing the anomaly and the candidate explanation to be presented to a data safety monitoring board ("DSMB") of the clinical trial.

This invention relates to a patient scheduling system for a hospital emergency room. An initial assessment scheduling module classifies admitted patients by urgency level. When medical resources are strained, it compares historical emergency records and simulates pre-treatment interventions based on similar cases. Simultaneously, it constructs a waiting queue containing patients at different treatment stages and predicts the impact of adding a patient to the waiting queue on the patient themselves and other patients in the queue. A secondary assessment scheduling module, after the patient receives formal intervention, schedules nurses to perform corresponding examinations and constructs a secondary examination result vector. A comprehensive assessment module combines the urgency level and the secondary examination result vector, using a pre-trained model to obtain the final patient classification, providing a basis for subsequent treatment scheduling. This invention, through pre-intervention and predictive dynamic ranking, effectively utilizes fragmented doctor time, optimizes emergency room resource scheduling, significantly shortens patient waiting time, and improves emergency efficiency and treatment success rate.

Systems and methods for pharmacotherapeutic intervention in obesity treatment are disclosed, targeting multiple neurophysiological mechanisms of appetite and weight regulation. The described technology utilizes patient phenotyping, including body mass index (BMI), medical history, and laboratory data, to classify patients and select individualized therapies. Solutions include administering non-GLP-1 agonist anti-obesity medications, dual therapies, or agents targeting GLP-1 and GIP receptors, combined with lifestyle modifications (diet, physical activity). The approach aims to achieve significant and durable weight loss while minimizing compensatory physiological responses, with periodic re-evaluation and treatment updates. An AI-driven decision support tool may be used for personalized recommendations. Principal uses include treating patients with varying BMI ranges and comorbidities, including type 2 diabetes mellitus, to improve metabolic health and quality of life. The described method is characterized by selecting and administering pharmacotherapy based on patient classification and ongoing assessment.

The invention relates to a method and a system for selecting and confirming informed objects of patients and a storage medium. The method comprises the following steps of: establishing an informed rule base containing a patient classification group and a corresponding informed object permission sorting system; receiving relation information between the informed notification object and the patient and a certification material, and verifying the consistency of the declared patient information and the existing patient information of the medical and health institution at the same time; verifying the correctness and effectiveness of the relationship between the informed object and the patient by adopting a relative relationship verification mode based on credibility; based on the informed informing rule base and the patient diagnosis and treatment information, generating an informed informing object suitable sorting table of the patient; and providing a data service based on the informed object suitable sorting table, wherein the data service comprises a relationship query service and an informed object verification service. The problem that in the prior art, it is difficult for medical staff to efficiently select an appropriate informing object is solved.