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      Automated assessment of psychiatric disorders using speech: A systematic review

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          Abstract

          Objective

          There are many barriers to accessing mental health assessments including cost and stigma. Even when individuals receive professional care, assessments are intermittent and may be limited partly due to the episodic nature of psychiatric symptoms. Therefore, machine‐learning technology using speech samples obtained in the clinic or remotely could one day be a biomarker to improve diagnosis and treatment. To date, reviews have only focused on using acoustic features from speech to detect depression and schizophrenia. Here, we present the first systematic review of studies using speech for automated assessments across a broader range of psychiatric disorders.

          Methods

          We followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA) guidelines. We included studies from the last 10 years using speech to identify the presence or severity of disorders within the Diagnostic and Statistical Manual of Mental Disorders (DSM‐5). For each study, we describe sample size, clinical evaluation method, speech‐eliciting tasks, machine learning methodology, performance, and other relevant findings.

          Results

          1395 studies were screened of which 127 studies met the inclusion criteria. The majority of studies were on depression, schizophrenia, and bipolar disorder, and the remaining on post‐traumatic stress disorder, anxiety disorders, and eating disorders. 63% of studies built machine learning predictive models, and the remaining 37% performed null‐hypothesis testing only. We provide an online database with our search results and synthesize how acoustic features appear in each disorder.

          Conclusion

          Speech processing technology could aid mental health assessments, but there are many obstacles to overcome, especially the need for comprehensive transdiagnostic and longitudinal studies. Given the diverse types of data sets, feature extraction, computational methodologies, and evaluation criteria, we provide guidelines for both acquiring data and building machine learning models with a focus on testing hypotheses, open science, reproducibility, and generalizability.

          Level of Evidence

          3a

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          Most cited references149

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          Diagnostic and Statistical Manual of Mental Disorders

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            Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.

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              Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

              Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practices and can potentially cause catastrophic harm to society. There is a way forward - it is to design models that are inherently interpretable. This manuscript clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare, and computer vision.
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                Author and article information

                Contributors
                dlow@mit.edu
                satra@mit.edu
                Journal
                Laryngoscope Investig Otolaryngol
                Laryngoscope Investig Otolaryngol
                10.1002/(ISSN)2378-8038
                LIO2
                Laryngoscope Investigative Otolaryngology
                John Wiley & Sons, Inc. (Hoboken, USA )
                2378-8038
                31 January 2020
                February 2020
                : 5
                : 1 ( doiID: 10.1002/lio2.v5.1 )
                : 96-116
                Affiliations
                [ 1 ] Program in Speech and Hearing Bioscience and Technology, Harvard Medical School Boston Massachusetts
                [ 2 ] Department of Brain and Cognitive Sciences MIT Cambridge Massachusetts
                [ 3 ] Department of Psychiatry Massachusetts General Hospital/Harvard Medical School Boston Massachusetts
                [ 4 ] McGovern Institute for Brain Research, MIT Cambridge Massachusetts
                [ 5 ] Department of Otolaryngology, Head and Neck Surgery Harvard Medical School Boston Massachusetts
                Author notes
                [*] [* ] Correspondence

                Daniel M. Low and Satrajit S. Ghosh, 46‐4033F, 43 Vassar Street, Cambridge, MA 02139.

                Email: dlow@ 123456mit.edu (D. M. L.) and satra@ 123456mit.edu (S. S. G.)

                Author information
                https://orcid.org/0000-0002-8866-8667
                https://orcid.org/0000-0002-5312-6729
                Article
                LIO2354
                10.1002/lio2.354
                7042657
                32128436
                c0176efe-722b-419d-97e0-55a021c6bf9e
                © 2020 The Authors. Laryngoscope Investigative Otolaryngology published by Wiley Periodicals, Inc. on behalf of The Triological Society.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 05 September 2019
                : 31 December 2019
                : 17 January 2020
                Page count
                Figures: 5, Tables: 3, Pages: 21, Words: 15694
                Funding
                Funded by: Gift to the McGovern Institute for Brain Research at MIT
                Funded by: MIT‐Philips Research Award for Clinicians
                Funded by: National Institute of Health
                Award ID: 5T32DC000038‐28
                Categories
                Laryngology, Speech and Language Science
                Laryngology, Speech and Language Science
                Review
                Custom metadata
                2.0
                February 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.6.1 mode:remove_FC converted:26.02.2020

                machine learning,mental health,psychiatry,speech,voice
                machine learning, mental health, psychiatry, speech, voice

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