Research And Implementation Of Auxiliary Diagnosis Algorithm Of Depression Based On Text Information Processing

With the acceleration of the pace of life and the increasingly fierce competition in society,the incidence of depression is rising everyday and depression has become a major disease in the world.Depression can make patients feel depressed,pessimistic and even suicidal,which makes the depression diagnosis particularly important.If the patient is not diagnosed and treated in time,it might cause serious consequences.However,the number of people with rich experience and strong professionalism in the field of depression diagnosis is limited,thus resulting in a lot of inaccurate diagnosis results and laying down hidden dangers.In view of those problems,people try using AI and NLP technology to analyze the text information of patients when they meet a doctor and extract the semantic features to predict depression,thereby assisting doctors in making a diagnosis.Although methods such as using shallow statistical features to construct classifiers and using deep learning methods to extract semantic features from patient text information for depression detection have gained much research attention and have made continuous progress,the effects are very limited.On one hand,it is difficult for the existing models to directly predict depression based on semantic features from texts or shallow statistical features extracted manually.On the other hand,due to the limitation of privacy protection policies,it is hard to obtain depression data,which greatly restricts the training effect and makes it difficult to make accurate predictions.In view of these problems,this paper designs and implements a depression detection algorithm that combines semantic features and language style features based on text similarity model and completes depression diagnosis task through recall-and-sort mode.This paper mainly carried out the following work:1.A depression detection algorithm based on text similarity model is proposed.The algorithm transforms the learning objective of the model from learning the mapping relationship between the semantic features of patients and their depression to learning the semantic difference between patients with different depression levels,which greatly reduces the learning difficulty of the algorithm and improves the effect.At the same time,using combination method to construct training data reduces the algorithm’s reliance on the amount of data;2.Based on the above algorithm,a depression detection algorithm which can integrate semantic features and language style features is further proposed.Through the joint learning of two features,the learning ability of the algorithm is improved and so is the effect of the algorithm.Then this paper carried out a lot of experiments to verify the performance of the model from multiple perspectives;3.In order to reduce the operation difficulty of the algorithm so that users can focus on depression diagnosis and improve the diagnosis quality,this paper designs and implements an auxiliary diagnosis system which provides users with a graphical operation interface,including functions such as model introduction,depression diagnosis and data display.