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digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives

World Journal of Psychiatry 2022年第3期12卷 393-409页

作者： Jayesh Kamath Roberto Leon Barriera Neha Jain Efraim Keisari Bing Wang Department of Psychiatry and Immunology University of Connecticut School of MedicineUniversity of Connecticut Health CenterFarmingtonCT 06030United States Department of Psychiatry University of Connecticut School of MedicineUniversity of Connecticut Health CenterFarmingtonCT 06032United States Department of Computer Science and Engineering University of ConnecticutStorrsCT 06269United States

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

关键词： digital phenotyping Depression Ecological momentary assessment Telepsychiatry Passive sensing Smart phone

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INPHOVIS:Interactive visual analytics for smartphone-based digital phenotyping

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Visual Informatics 2023年第2期7卷 13-29页

作者： Hamid Mansoor Walter Gerych Abdulaziz Alajaji Luke Buquicchio Kavin Chandrasekaran Emmanuel Agu Elke Rundensteiner Angela Incollingo Rodriguez Worcester Polytechnic Institute USA

digital phenotyping is the characterization of human behavior patterns based on data from digital devices such as smartphones in order to gain insights into the users’state and especially to identify *** support supervised machine learning,digital phenotyping requires gathering data from study participants’smartphones as they live their ***,participants are then asked to provide ground truth labels about their health *** such complex data is challenging due to limited contextual information and imperfect health/wellness *** propose INteractive PHOne-o-typing VISualization(INPHOVIS),an interactive visual framework for exploratory analysis of smartphone health data to study *** visualization work has focused on mobile health data with clear semantics such as steps or heart rate data collected using dedicated health devices and wearables such as ***,unlike smartphones which are owned by over 85 percent of the US population,wearable devices are less prevalent thus reducing the number of people from whom such data can be *** contrast,the‘‘low-level"sensor data(e.g.,accelerometer or GPS data)supported by INPHOVIS can be easily collected using *** visualizations are designed to provide the essential contextualization of such data and thus help analysts discover complex relationships between observed sensor values and health-predictive *** guide the design of INPHOVIS,we performed a hierarchical task analysis of phone-o-typing requirements with health domain *** then designed and implemented multiple innovative visualizations integral to INPHOVIS including stacked bar charts to show diurnal behavioral patterns,calendar views to visualize day-level data along with bar charts,and correlation views to visualize important wellness predictive *** demonstrate the usefulness of INPHOVIS with walk-throughs of use *** also evaluated INPHOVIS with expert feedb

关键词： Interactive visual analytics Smartphone-sensed data digital phenotyping

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