4 results about "Patient classification" patented technology

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 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.

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.