Data Analysis Of Clinical Depression

In recent years,with the rapid development of medical technology,advanced medical equipment has been used widely.Many medical data have been effectively saved.As a bridge between medical research and clinical diagnosis,clinical medicine has the characteristics of diversity,high dimensionality and redundancy.Reasonable medical data analysis methods are of great significance to analyze the causes of illness,assist doctors in diagnosis and prevent disease.Psychosomatic disease is an emerging clinical science.The diagnosis process not only requires the detection of precision instruments,but also requires doctors to consult with rich clinical experience.Due to the complexity of data collection,psychosomatic medicine is very special in clinical practice.In addition,as a common psychological disease,the cause of depression is not yet clear,so it has the specificity of disease research.This article focuses on the analysis of clinical depression data analysis to solve the following two problems: one is the research and development of the psychosomatic symptom scale,which is used to standardize the collection of clinical data;the second is to achieve data analysis of poststroke depression and late-onset depression to explore the pathogenesis.We surveyed 996 patients and 366 controls in 14 general hospitals in China using PSSS,PHQ-15 and SCL-90.Then,we use internal reliability test,structural validity test,correlation analysis,confirmatory factor analysis to verify PSSS.At the same time,we determine the reference threshold for men and women to establish a reliable and effective tool for measuring psychosomatic symptoms.In the data analysis of post-stroke depression,we use decision trees and decision curve methods to select features,thereby reducing the search space.Then,we deeply study the method of Bayesian network module,calculate the expectation of conditional probability and explore the interaction between 5-HTR1 A and the social psychology of Chinese PSD patients.Finally,in this paper,we combine the principles of magnetic resonance imaging on brain preprocessing data.Using graph theory as the model basis,we construct brain network functional connections and extract ROI regions.We fuse brain imaging data,genetic data and neurocognitive function data to study the interaction of susceptibility genes,brain functional connections,neurocognitive function and depression severity in patients with late-onset depression.