Hostname: page-component-8448b6f56d-wq2xx Total loading time: 0 Render date: 2024-04-24T18:30:28.234Z Has data issue: false hasContentIssue false
  • English
  • Français

Artificial intelligence and risk prediction in geriatric mental health: what happens next?

Published online by Cambridge University Press:  09 August 2019

Sarah A. Graham  and
Colin A. Depp
Sarah A. Graham
Affiliation:
Department of Psychiatry, University of California San Diego, San Diego, California, USA Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California, USA
Colin A. Depp*
Affiliation:
Department of Psychiatry, University of California San Diego, San Diego, California, USA Sam and Rose Stein Institute for Research on Aging, University of California San Diego, San Diego, California, USA VA San Diego Healthcare System, San Diego, California, USAEmail: cdepp@ucsd.edu
*
Correspondence should be addressed to: Colin A. Depp, Psychiatry, Director, Research Education and Training, Clinical and Translational Research Institute, Staff Psychologist, San Diego VA, 9500 Gilman Drive, Mail Code #0664, La Jolla, CA, U.S.A. Phone: 92093-0664, Telephone: (858) 822-4251. Email: cdepp@ucsd.edu.
Get access Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Commentary
Information
International Psychogeriatrics , Volume 31 , Issue 7: Issue Theme: Diverse Approaches to Stemming Cognitive Decline , July 2019 , pp. 921 - 923
Copyright
© International Psychogeriatric Association 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Albert, M. S., et al. (2011). The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dementia, 7, 270–279. doi: 10.1016/j.jalz.2011.03.008CrossRefGoogle Scholar
Brookmeyer, R., Abdalla, N., Kawas, C. H. and Corrada, M. M. (2018). Forecasting the prevalence of preclinical and clinical Alzheimer’s disease in the United States. Alzheimer’s Dementia, 14, 121–129. doi: 10.1016/j.jalz.2017.10.009CrossRefGoogle ScholarPubMed
Dukart, J., Sambataro, F. and Bertolino, A. (2015). Accurate prediction of conversion to Alzheimer’s disease using imaging, genetic, and neuropsychological biomarkers. Journal of Alzheimer’s Disease, 49, 1143–1159. doi: 10.3233/JAD-150570CrossRefGoogle Scholar
Gomar, J., Bobes-Bascaran, M., Conejero-Goldberg, C., Davies, P. and Goldberg, T. (2011). Utility of combinations of biomarkers, cognitive markers, and risk factors to predict conversion from mild cognitive impairment to Alzheimer disease in patients in the Alzheimer’s disease neuroimaging initiative. Archives of General Psychiatry, 68, 961–969.CrossRefGoogle ScholarPubMed
Grassi, M., Loewenstein, D. A., Caldirola, D., Schruers, K., Duara, R. and Perna, G. (2018a). A clinically-translatable machine learning algorithm for the prediction of Alzheimer’s disease conversion: further evidence of its accuracy via a transfer learning approach. International Psychogeriatrics, 31, 937–945. doi: 10.1017/S1041610218001618Google Scholar
Grassi, M., Perna, G., Caldirola, D., Schruers, K., Duara, R. and Loewenstein, D. A. (2018b). A clinically-translatable machine learning algorithm for the prediction of Alzheimer’s disease conversion in individuals with mild and premild cognitive impairment. Journal of Alzheimer’s Disease, 61, 1555–1573. doi: 10.3233/JAD-170547CrossRefGoogle ScholarPubMed
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Pedreschi, D. and Giannotti, F. (2018). A survey of methods for explaining black box models. ACM Computer Survey, 51, 93.CrossRefGoogle Scholar
Jeste, D. V., et al. (2019). Study of independent living residents of a continuing care senior housing community: sociodemographic and clinical associations of cognitive, physical, and mental health. American Journal of Geriatric Psychiatry, pii: S1064-7481(19)30335-5. doi: 10.1016/j.jagp.2019.04.002CrossRefGoogle ScholarPubMed
Light, G. A. and Swerdlow, N. R. (2015). Bending the curve on psychosis outcomes. The Lancet Psychiatry, 2, 365–367. doi: 10.1016/s2215-0366(15)00172-8CrossRefGoogle ScholarPubMed
Mitchell, A. J. and Shiri-Feshki, M. (2008). Temporal trends in the long term risk of progression of mild cognitive impairment: a pooled analysis. Journal of Neurology, Neurosurgery and Psychiatry, 79, 1386–1391. doi: 10.1136/jnnp.2007.142679CrossRefGoogle Scholar
Mukadam, N. (2018). Improving the diagnosis and prediction of progression in mild cognitive impairment. International Psychogeriatrics, 30, 1419–1421.CrossRefGoogle ScholarPubMed
Petersen, R. (2011). Mild cognitive impairment. New England Journal of Medicine, 364, 2227–2234.CrossRefGoogle ScholarPubMed
U.S. Food and Drug Administration. (2019). Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based Software as a Medical Device (SaMD)-discussion paper and request for feedback. Available at: https://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm514737.pdf; last accessed 6 January 2019.Google Scholar
Vancampfort, D., Stubbs, B., Lara, E., Vandenbulcke, M., Swinnen, N. and Koyanagi, A. (2019). Correlates of sedentary behavior in middle-aged and old age people with mild cognitive impairment: a multinational study. International Psychogeriatrics, 31, 579–589. doi: 10.1017/S1041610218001163CrossRefGoogle ScholarPubMed
World Health Organization. (2019). Dementia. Available at https://www.who.int/news-room/fact-sheets/detail/dementia; 6 January 2019.Google Scholar