Comprehensive Summary
This study, presented by Lee et. al, examines the application of AI system DeepGAM in diagnosing depression and alleviating the black box nature of traditional diagnostic methods thereby improving interpretability. Using baseline data from The Heart and Soul Study, research following patients with Coronary Heart Disease and related outcomes of depression, a Generalized Additive Model (GAM) was implemented to add all possible contributing factors (age, heart rate, etc.) and select the top five variables by Straight-Through Estimator (STE). This deep learning algorithm achieved a higher score in determining whether a patient was depressed or not (AUC 0.600) and better accuracy in diagnosing correctly (F-1 score 0.387) compared to traditional machine learning models. DeepGAM performance was just as effective only analyzing the five top features selected by STE compared to traditional systems Lasso or Botura which usually study 99+ factors to achieve a diagnosis. This advanced neural network system outcompeted multiple artificial intelligence methods in various sectors and provided better substantiated interoperability on the depression diagnosis.
Outcomes and Implications
This new machine learning method presents an innovative perspective on depression diagnosis by allowing physicians to systematically locate the contributing factors of the condition and interpret the results of a patient’s medical record with deep learning intelligence. By implementing DeepGAM software in medicine, physicians across various specialties may be able to diagnose faster, more effectively, and with greater accuracy thereby improving quality of care.