47
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Ethical perspectives on recommending digital technology for patients with mental illness

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The digital revolution in medicine not only offers exciting new directions for the treatment of mental illness, but also presents challenges to patient privacy and security. Changes in medicine are part of the complex digital economy based on creating value from analysis of behavioral data acquired by the tracking of daily digital activities. Without an understanding of the digital economy, recommending the use of technology to patients with mental illness can inadvertently lead to harm. Behavioral data are sold in the secondary data market, combined with other data from many sources, and used in algorithms that automatically classify people. These classifications are used in commerce and government, may be discriminatory, and result in non-medical harm to patients with mental illness. There is also potential for medical harm related to poor quality online information, self-diagnosis and self-treatment, passive monitoring, and the use of unvalidated smartphone apps. The goal of this paper is to increase awareness and foster discussion of the new ethical issues. To maximize the potential of technology to help patients with mental illness, physicians need education about the digital economy, and patients need help understanding the appropriate use and limitations of online websites and smartphone apps.

          Related collections

          Most cited references146

          • Record: found
          • Abstract: not found
          • Article: not found

          Statistical pattern recognition: a review

            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement

            Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). Editors’ note: In order to encourage dissemination of the TRIPOD Statement, this article is freely accessible on the Annals of Internal Medicine Web site (www.annals.org) and will be also published in BJOG, British Journal of Cancer, British Journal of Surgery, BMC Medicine, British Medical Journal, Circulation, Diabetic Medicine, European Journal of Clinical Investigation, European Urology, and Journal of Clinical Epidemiology. The authors jointly hold the copyright of this article. An accompanying Explanation and Elaboration article is freely available only on www.annals.org; Annals of Internal Medicine holds copyright for that article.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A survey of affect recognition methods: audio, visual, and spontaneous expressions.

              Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. However, the existing methods typically handle only deliberately displayed and exaggerated expressions of prototypical emotions despite the fact that deliberate behaviour differs in visual appearance, audio profile, and timing from spontaneously occurring behaviour. To address this problem, efforts to develop algorithms that can process naturally occurring human affective behaviour have recently emerged. Moreover, an increasing number of efforts are reported toward multimodal fusion for human affect analysis including audiovisual fusion, linguistic and paralinguistic fusion, and multi-cue visual fusion based on facial expressions, head movements, and body gestures. This paper introduces and surveys these recent advances. We first discuss human emotion perception from a psychological perspective. Next we examine available approaches to solving the problem of machine understanding of human affective behavior, and discuss important issues like the collection and availability of training and test data. We finally outline some of the scientific and engineering challenges to advancing human affect sensing technology.
                Bookmark

                Author and article information

                Contributors
                +49-351-458-0 , michael.bauer@uniklinikum-dresden.de
                tglenn@chronorecord.org
                monteit2@msu.edu
                rita.bauer@uniklinikum-dresden.de
                PWhybrow@MEDNET.ucla.edu
                john.geddes@psych.ox.ac.uk
                Journal
                Int J Bipolar Disord
                Int J Bipolar Disord
                International Journal of Bipolar Disorders
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                2194-7511
                7 February 2017
                7 February 2017
                2017
                : 5
                : 6
                Affiliations
                [1 ]ISNI 0000 0001 2111 7257, GRID grid.4488.0, Department of Psychiatry and Psychotherapy, Universitätsklinikum Carl Gustav Carus, , Technische Universität Dresden, ; Fetscherstr. 74, 01307 Dresden, Germany
                [2 ]ChronoRecord Association, Inc., Fullerton, CA 92834 USA
                [3 ]Michigan State University College of Human Medicine, Traverse City Campus, 1400 Medical Campus Drive, Traverse City, MI 49684 USA
                [4 ]ISNI 0000 0000 9632 6718, GRID grid.19006.3e, Department of Psychiatry and Biobehavioral Sciences, , Semel Institute for Neuroscience and Human Behavior University of California Los Angeles (UCLA), ; 300 UCLA Medical Plaza, Los Angeles, CA 90095 USA
                [5 ]ISNI 0000 0004 1936 8948, GRID grid.4991.5, Department of Psychiatry, , University of Oxford, Warneford Hospital, ; Oxford, OX3 7JX UK
                Author information
                http://orcid.org/0000-0002-2666-859X
                Article
                73
                10.1186/s40345-017-0073-9
                5293713
                28155206
                b37db590-8d68-4304-b1bc-4d733d17fde6
                © The Author(s) 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 22 November 2016
                : 4 January 2017
                Categories
                Review
                Custom metadata
                © The Author(s) 2017

                mental illness,digital healthcare,ethics,digital economy,privacy

                Comments

                Comment on this article