Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives
作者机构：Department of Psychiatry and ImmunologyUniversity of Connecticut School of MedicineUniversity of Connecticut Health CenterFarmingtonCT 06030United States Department of PsychiatryUniversity of Connecticut School of MedicineUniversity of Connecticut Health CenterFarmingtonCT 06032United States Department of Computer Science and EngineeringUniversity of ConnecticutStorrsCT 06269United States
出 版 物：《World Journal of Psychiatry》 (世界精神病学杂志)
年 卷 期：2022年第12卷第3期
页 面：393-409页
核心收录：
学科分类：1002[医学-临床医学] 100205[医学-精神病与精神卫生学] 10[医学]
主 题：Digital phenotyping Depression Ecological momentary assessment Telepsychiatry Passive sensing Smart phone
摘 要：Depression is a serious medical condition and is a leading cause of disability *** depression diagnostics and assessment has significant limitations due to heterogeneity of clinical presentations,lack of objective assessments,and assessments that rely on patients perceptions,memory,and *** phenotyping(DP),especially assessments conducted using mobile health technologies,has the potential to greatly improve accuracy of depression diagnostics by generating objectively measurable *** includes two primary sources of digital data generated using ecological momentary assessments(EMA),assessments conducted in real-time,in subjects natural *** includes active EMA,data that require active input by the subject,and passive EMA or passive sensing,data passively and automatically collected from subjects personal digital *** raw data is then analyzed using machine learning algorithms to identify behavioral patterns that correlate with patients clinical *** investigations have also shown that linguistic and behavioral clues from social media data and data extracted from the electronic medical records can be used to predict depression *** other sources of data and recent advances in telepsychiatry can further enhance DP of the depressed *** of DP endeavors depends on critical contributions from both psychiatric and engineering *** current review integrates important perspectives from both disciplines and discusses parameters for successful interdisciplinary collaborations.A clinically-relevant model for incorporating DP in clinical setting is *** model,based on investigations conducted by our group,delineates development of a depression prediction system and its integration in clinical setting to enhance depression diagnostics and inform the clinical decision making ***,challenges,and opportunities pertaining to clinical integration of DP of depressio
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