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Deep Digital Phenotyping and Digital Twins for Precision Health: Time to Dig Deeper

Deep digital phenotyping is the combination of deep phenotyping (defined for almost a decade now as the “precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described”) [7], with

Guy Fagherazzi

J Med Internet Res 2020;22(3):e16770

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Intelligent Sensing to Inform and Learn (InSTIL): A Scalable and Governance-Aware Platform for Universal, Smartphone-Based Digital Phenotyping for Research and Clinical Applications

This paper describes the design of a new platform for digital phenotyping intended to satisfy this requirement.

Scott Barnett, Kit Huckvale, Helen Christensen, Svetha Venkatesh, Kon Mouzakis, Rajesh Vasa

J Med Internet Res 2019;21(11):e16399

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Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors

The variable importance of the primary feature was also extracted from the higher-order ensemble, as well as from the lower order model to determine the single most influential digital biomarker of the predictions of SAD symptom severity.ResultsPercentage of

Nicholas C Jacobson, Berta Summers, Sabine Wilhelm

J Med Internet Res 2020;22(5):e16875

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Digital Phenotyping to Enhance Substance Use Treatment During the COVID-19 Pandemic

Digital phenotyping has shown initial success in relapse prediction for schizophrenia [12], bipolar disorder [13,14], and mood disorder detection [14].

Michael Hsu, David K Ahern, Joji Suzuki

JMIR Ment Health 2020;7(10):e21814

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Temporal Design Patterns for Digital Phenotype Cohort Selection in Critical Care: Systematic Literature Assessment and Qualitative Synthesis

The results can be used to inform the design of temporal abstraction-based digital phenotyping systems.MethodsData SourceWe conducted a systematic literature review of published articles in the critical care domain.

Daniel Capurro, Mario Barbe, Claudio Daza, Josefa Santa Maria, Javier Trincado

JMIR Med Inform 2020;8(11):e6924

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Psychiatry Outpatients’ Willingness to Share Social Media Posts and Smartphone Data for Research and Clinical Purposes: Survey Study

phenotyping.

Agnes Rieger, Averi Gaines, Ian Barnett, Claudia Frances Baldassano, Mary Beth Connolly Gibbons, Paul Crits-Christoph

JMIR Form Res 2019;3(3):e14329

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New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research

In this sense, digital phenotyping can be viewed as a variant of deep phenotyping.

John Torous, Mathew V Kiang, Jeanette Lorme, Jukka-Pekka Onnela

JMIR Ment Health 2016;3(2):e16

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Mapping and Modeling of Discussions Related to Gastrointestinal Discomfort in French-Speaking Online Forums: Results of a 15-Year Retrospective Infodemiology Study

Florent Schäfer, Carole Faviez, Paméla Voillot, Pierre Foulquié, Matthieu Najm, Jean-François Jeanne, Guy Fagherazzi, Stéphane Schück, Boris Le Nevé

J Med Internet Res 2020;22(11):e17247

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Toi Même, a Mobile Health Platform for Measuring Bipolar Illness Activity: Protocol for a Feasibility Study

step of this process.Objectives of This StudyIn order to tackle some of these questions as well as to contribute to the evidence on digital phenotyping in bipolar disorder, we developed the Toi Même mHealth platform, which comprises the Toi Même smartphone

Aroldo A Dargél, Elise Mosconi, Marc Masson, Marion Plaze, Fabien Taieb, Cassandra Von Platen, Tan Phuc Buivan, Guillaume Pouleriguen, Marie Sanchez, Stéphane Fournier, Pierre-Marie Lledo, Chantal Henry

JMIR Res Protoc 2020;9(8):e18818

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Digital Phenotyping Self-Monitoring Behaviors for Individuals With Type 2 Diabetes Mellitus: Observational Study Using Latent Class Growth Analysis

To date, digital phenotyping has been successfully used to track behavior and symptom data and to refine diagnosis and risk prediction for psychiatric disorders [12], dementia [13], and asthma [14].

Qing Yang, Daniel Hatch, Matthew J Crowley, Allison A Lewinski, Jacqueline Vaughn, Dori Steinberg, Allison Vorderstrasse, Meilin Jiang, Ryan J Shaw

JMIR Mhealth Uhealth 2020;8(6):e17730

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