Intelligent Analysis of Medical Big Data Based on Deep Learning

Abstract

With the wide application of computer technology, medical health data has also increased dramatically, and data-driven medical big data analysis methods have emerged as the times require, providing assistance for intelligent identification of medical health. However, due to the mixed medical big data format, many incomplete records, and a lot of noise, it is still difficult to analyze medical big data. Traditional machine learning methods can’t effectively mine the rich information contained in medical big data, while deep learning builds a hierarchical model by simulating the human brain. It has powerful automatic feature extraction, complex model construction and efficient feature expression, and more important. It is a deep learning method that extracts features from the bottom to the top level from the original medical image data. Therefore, this paper constructs a data analysis model based on deep learning for medical images and transcripts, and is used for intelligent identification and diagnosis of diseases. The model uses massive medical big data to select and optimize model parameters, and automatically learns the pathological analysis process of doctors or medical researchers through the model, and finally intelligently conducts disease judgment and effective decision based on the analysis results of medical big data. The experimental results show that the method can analyze the medical big data, and can realize the early diagnosis of the disease. At the same time, it can analyze the physical health status according to the patient’s physical examination records and predict the risk of a certain disease in the future. Greatly reduce the work pressure of doctors or medical researchers and improve their work efficiency.