Mood Emot 2024 Nov; 22(3): 63-68   https://doi.org/10.35986/me.2024.22.3.63
Predicting Treatment Response to Antidepressants in Patients with Major Depressive Disorder Based on Longitudinal Clinical Data Using Artificial Intelligence: A Pilot Study
Junhee Lee, MD, PhD1* , Seung-Hwan Baek, PhD2* , Min-Kyung Jang, RN, BA1 , Hyeon-Hee Sim, MA1 , In Young Choi, PhD3 , Dai-Jin Kim, MD, PhD1,3
1Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, 2UNiAI, Daejeon, 3Department of Medical Informatics, The Catholic University of Korea, Seoul, Korea
Correspondence to: Dai-Jin Kim, MD, PhD
Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul 06591, Korea
TEL +82-2-2258-6086 FAX +82-2-594-3870 E-mail kdj922@catholic.ac.kr ORCID https://orcid.org/0000-0001-9408-5639
*These authors have contributed equally to this work.
Received: October 31, 2024; Revised: November 14, 2024; Accepted: November 16, 2024; Published online: November 30, 2024.
© Korean Society for Affective Disorders. All rights reserved.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Background: The diagnosis of major depressive disorder (MDD) relies primarily on clinical interviews, which can be subjective and time consuming. Thus, there is a need for more objective diagnostic tools. The aim of this study was to develop an artificial intelligence (AI) application that predicts the antidepressant drug response of individual patients with MDD based on longitudinal data.
Methods: Longitudinal data from patient records, including sex, age, outpatient or inpatient status, medication type and dosage, and the Hamilton Depression Rating Scale (HAMD) scores, were used to train the Transformer model and the 1-dimensional convolutional neural network model. Individual patient records were allocated to training (80%), validation (10%), and testing (10%) datasets.
Results: The AI model demonstrated 88% sensitivity and 92% specificity for predicting the treatment response. Significant factors independently associated with the antidepressant response included age, sex, history of depression, and baseline HAMD scores.
Conclusion: This AI-driven software application provides a clinically valuable tool for predicting treatment response. While promising, further research is needed to incorporate voice data into the AI model using the voice recording feature to further improve diagnostic accuracy.
Keywords: Major depressive disorder; Treatment response; Prediction; Artificial intelligence

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