ArticleSocial psychiatry and psychiatric epidemiology2018
Diagnosis of Dementia by Machine learning methods in Epidemiological studies: a pilot exploratory study from south India.
Article in Social psychiatry and psychiatric epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
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- Analysis of COVID-19 severity from the perspective of coagulation index using evolutionary machine learning with enhanced brain storm optimization.Journal of King Saud University. Computer and information sciences · 2022Article
- Artificial intelligence and Psychiatry: An overview.Asian journal of psychiatry · 2022Review
- Screening dementia and predicting high dementia risk groups using machine learning.World journal of psychiatry · 2022Article
- Prediction of conversion to dementia using interpretable machine learning in patients with amnestic mild cognitive impairment.Frontiers in aging neuroscience · 2022Article
- Evolutionary warning system for COVID-19 severity: Colony predation algorithm enhanced extreme learning machine.Computers in biology and medicine · 2021Article
- Machine learning approach to support taxonomic species discrimination based on helminth collections data.Parasites & vectors · 2021Article
- Artificial intelligence for good health: a scoping review of the ethics literature.BMC medical ethics · 2021Article
- A non-central beta model to forecast and evaluate pandemics time series.Chaos, solitons, and fractals · 2020Article
- Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic.The Science of the total environment · 2020Review
- Applying Machine Learning Techniques to Identify Undiagnosed Patients with Exocrine Pancreatic Insufficiency.Journal of health economics and outcomes research · 2019Article
- Modifiable Lifestyle Factors and Cognitive Function in Older People: A Cross-Sectional Observational Study.Frontiers in neurology · 2019Article
- Technology and Dementia: The Future is Now.Dementia and geriatric cognitive disorders · 2019Review
- Machine Learning in Acute Ischemic Stroke Neuroimaging.Frontiers in neurology · 2018Review
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5 authors.
Funding
Abstract
backgroundThere are limited data on the use of artificial intelligence methods for the diagnosis of dementia in epidemiological studies in low- and middle-income country (LMIC) settings. A culture and education fair battery of cognitive tests was developed and validated for population based studies in low- and middle-income countries including India by the 10/66 Dementia Research Group.
aimsWe explored the machine learning methods based on the 10/66 battery of cognitive tests for the diagnosis of dementia based in a birth cohort study in South India.
methodsThe data sets for 466 men and women for this study were obtained from the on-going Mysore Studies of Natal effect of Health and Ageing (MYNAH), in south India. The data sets included: demographics, performance on the 10/66 cognitive function tests, the 10/66 diagnosis of mental disorders and population based normative data for the 10/66 battery of cognitive function tests. Diagnosis of dementia from the rule based approach was compared against the 10/66 diagnosis of dementia. We have applied machine learning techniques to identify minimal number of the 10/66 cognitive function tests required for diagnosing dementia and derived an algorithm to improve the accuracy of dementia diagnosis.
resultsOf 466 subjects, 27 had 10/66 diagnosis of dementia, 19 of whom were correctly identified as having dementia by Jrip classification with 100% accuracy.
conclusionsThis pilot exploratory study indicates that machine learning methods can help identify community dwelling older adults with 10/66 criterion diagnosis of dementia with good accuracy in a LMIC setting such as India. This should reduce the duration of the diagnostic assessment and make the process easier and quicker for clinicians, patients and will be useful for 'case' ascertainment in population based epidemiological studies.
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