Automated screening for distress: a perspective for the future

Rana, Rajib and Latif, Siddique and Gururajan, Raj ORCID: https://orcid.org/0000-0002-5919-0174 and Gray, Anthony and Mackenzie, Geraldine and Humphris, Gerald and Dunn, Jeff (2019) Automated screening for distress: a perspective for the future. European Journal of Cancer Care, 28 (4):e13033. pp. 1-13. ISSN 0961-5423


Abstract

Distress is a complex condition, which affects a significant percentage of cancer patients and may lead to depression, anxiety, sadness, suicide and other forms of psychological morbidity. Compelling evidence supports screening for distress as a means of facilitating early intervention and subsequent improvements in psychological well-being and overall quality of life. Nevertheless, despite the existence of evidence-based and easily administered screening tools, for example, the Distress Thermometer, routine screening for distress is yet to achieve widespread implementation. Efforts are intensifying to utilise innovative, cost-effective methods now available through emerging technologies in the informatics and computational arenas.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - Institute for Resilient Regions
Faculty/School / Institute/Centre: Current - Institute for Resilient Regions
Date Deposited: 19 Mar 2019 05:57
Last Modified: 08 Jun 2021 00:29
Uncontrolled Keywords: automated distress screen, cancer, deep learnring, spontaneous speech
Fields of Research (2008): 11 Medical and Health Sciences > 1199 Other Medical and Health Sciences > 119999 Medical and Health Sciences not elsewhere classified
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460212 Speech recognition
Socio-Economic Objectives (2008): C Society > 92 Health > 9299 Other Health > 929999 Health not elsewhere classified
Identification Number or DOI: https://doi.org/10.1111/ecc.13033
URI: http://eprints.usq.edu.au/id/eprint/35637

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