The Medical Testing Equipment Management System based on Artificial Intelligence

Abstract

This study focuses on developing a medical testing equipment management system based on artificial intelligence. The system integrates advanced sensor technology to monitor patient's physiological characteristics data in real-time, such as heart rate, blood pressure, body temperature, etc. and processes the data through a differential entropy analysis algorithm to extract key health indicators. Then, this study constructed a deep learning neural network model to predict the changing trend of patient health status and optimized the configuration and use of medical detection equipment accordingly. This paper proposes a feature extraction method based on neural network model, which can effectively identify abnormal patterns in physiological signals and provide high-quality input data for subsequent prediction models. Simulation results show that the proposed neural network model has high accuracy and practicability in predicting patients' health status. The model can assist healthcare workers in identifying potential health risks in time to improve treatment results and patients' quality of life.