A review on assessment, early warning and auxiliary diagnosis of depression based on different modal data

Depression is a widespread mental health disorder. At present, the clinical diagnosis of depression mainly depends on the interview between doctors and patients and the self-report of patients, so the diagnosis is subjective to a certain extent. This paper summarizes the scales of self-assessment for depression and the methods of relevant health data collection. This paper discusses the research status of the auxiliary diagnosis of depression based on voice data and social network data, and summarizes the basic process of the auxiliary diagnosis using the above data. The application cases of some machine learning algorithms and their evaluation indexes are counted. The machine learning algorithm can objectively predict and assist the diagnosis of depression. The convenience and economy of collecting depression health information should be fully considered. In the actual analysis, data acquisition cost, difficulty, application purpose and application population should be taken into comprehensive consideration, and data used for auxiliary diagnosis should be designed. When necessary, multi-modal data combined with several data forms can be used. In this paper, different methods are proposed to evaluate depression in different populations, and at the same time, early warning and health intervention should be carried out on the basis of the assessment, so as to bring a new way of thinking for the economic and rapid objective evaluation of depression.