The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods in psychiatry detection applications, specifically depression disorder: A Brief Review

11 November 2023

Papers citing "The Pros and Cons of Using Machine Learning and Interpretable Machine Learning Methods in psychiatry detection applications, specifically depression disorder: A Brief Review"

7 / 7 papers shown

Title
Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis Across Six Depression Treatment Studies D. Benrimoh Akiva Kleinerman T. Furukawa C. Reynolds E. Lenze ... S. Qassim A. Anacleto A. Kapelner Ariel Rosenfeld G. Turecki 6 7 0 24 Mar 2023
A Picture May Be Worth a Thousand Lives: An Interpretable Artificial Intelligence Strategy for Predictions of Suicide Risk from Social Media Images Yael Badian Yaakov Ophir Refael Tikochinski Nitay Calderon A. Klomek Roi Reichart 47 4 0 19 Feb 2023
Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT Jiaheng Xie Xiaohang Zhao Xiang Liu Xiao Fang OOD 111 2 0 08 Nov 2022
Modern Views of Machine Learning for Precision Psychiatry Z. Chen Prathamesh Kulkarni Kulkarni I. Galatzer-Levy Benedetta Bigio C. Nasca Yu Zhang 59 98 0 04 Apr 2022
RobIn: A Robust Interpretable Deep Network for Schizophrenia Diagnosis Daniel Organisciak Hubert P. H. Shum E. Nwoye Wai Lok Woo OOD 50 19 0 31 Mar 2022
Predicting Mood Disorder Symptoms with Remotely Collected Videos Using an Interpretable Multimodal Dynamic Attention Fusion Network Tathagat Banerjee Matthew Kollada Pablo Gersberg Oscar Rodriguez J. Tiller A. Jaffe J. Reynders 21 5 0 07 Sep 2021
RuleMatrix: Visualizing and Understanding Classifiers with Rules Yao Ming Huamin Qu E. Bertini FAtt 62 215 0 17 Jul 2018