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      Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning

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          Abstract

          Sensors in everyday devices, such as our phones, wearables, and computers, leave a stream of digital traces. Personal sensing refers to collecting and analyzing data from sensors embedded in the context of daily life with the aim of identifying human behaviors, thoughts, feelings, and traits. This article provides a critical review of personal sensing research related to mental health, focused principally on smartphones, but also including studies of wearables, social media, and computers. We provide a layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health. Also discussed are research methods as well as challenges, including privacy and problems of dimensionality. Although personal sensing is still in its infancy, it holds great promise as a method for conducting mental health research and as a clinical tool for monitoring at-risk populations and providing the foundation for the next generation of mobile health (or mHealth) interventions.

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          Most cited references61

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          Is Open Access

          Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

          Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.
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            Is Open Access

            The Rise of Consumer Health Wearables: Promises and Barriers

            Lukasz Piwek and colleagues consider whether wearable technology can become a valuable asset for health care.
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              New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research

              Background A longstanding barrier to progress in psychiatry, both in clinical settings and research trials, has been the persistent difficulty of accurately and reliably quantifying disease phenotypes. Mobile phone technology combined with data science has the potential to offer medicine a wealth of additional information on disease phenotypes, but the large majority of existing smartphone apps are not intended for use as biomedical research platforms and, as such, do not generate research-quality data. Objective Our aim is not the creation of yet another app per se but rather the establishment of a platform to collect research-quality smartphone raw sensor and usage pattern data. Our ultimate goal is to develop statistical, mathematical, and computational methodology to enable us and others to extract biomedical and clinical insights from smartphone data. Methods We report on the development and early testing of Beiwe, a research platform featuring a study portal, smartphone app, database, and data modeling and analysis tools designed and developed specifically for transparent, customizable, and reproducible biomedical research use, in particular for the study of psychiatric and neurological disorders. We also outline a proposed study using the platform for patients with schizophrenia. Results We demonstrate the passive data capabilities of the Beiwe platform and early results of its analytical capabilities. Conclusions Smartphone sensors and phone usage patterns, when coupled with appropriate statistical learning tools, are able to capture various social and behavioral manifestations of illnesses, in naturalistic settings, as lived and experienced by patients. The ubiquity of smartphones makes this type of moment-by-moment quantification of disease phenotypes highly scalable and, when integrated within a transparent research platform, presents tremendous opportunities for research, discovery, and patient health.
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                Author and article information

                Journal
                101235325
                33704
                Annu Rev Clin Psychol
                Annu Rev Clin Psychol
                Annual review of clinical psychology
                1548-5943
                1548-5951
                30 November 2019
                17 March 2017
                08 May 2017
                10 December 2019
                : 13
                : 23-47
                Affiliations
                [1 ]Center for Behavioral Intervention Technologies and Department of Preventive Medicine, Northwestern University, Chicago, Illinois 60611;
                [2 ]Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824;
                Author notes
                Article
                PMC6902121 PMC6902121 6902121 nihpa1061609
                10.1146/annurev-clinpsy-032816-044949
                6902121
                28375728
                ea3ba346-a7d4-4862-ab88-f60bd21b5334
                History
                Categories
                Article

                wearables,pervasive health,machine learning,mHealth,mental health,sensors

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