Predicting Depression Trajectories: A Novel AI Approach for Personalized Mental Health Treatment

Abstract

Depression is a widespread mental illness that affects millions of people worldwide and is a problem for health care. In contrast with modern approaches, traditional treatment strategies do not rely on individuality, specifically about the course of the disease. In this paper, a new AI-based method to identify individual-specific depression trajectories for improving the outcomes of depression treatments is proposed. This, we believe, makes our strategy more effective for predicting the outcomes of specific treatment techniques for each patient since it employs up-to-date machine learning techniques and integrates a vast volume of patient information into modeling patient response. The paper shows how using customized estimations can increase the effectiveness of a treatment plan as opposed to relying on trial and error as it is today. AI in mental health care not only has potential to increase the quality of treated condition, it also has the ability to provide efficient solution to increase demand of depressants all over the world. This paper supports the view that AI can revolutionize mental health treatment since it makes it targeted, time and effectiveness oriented.