Feasibility Study of a Machine Learning Approach to Predict Dementia Progression

Chih Lin Chi, Wonsuk Oh, Soo Borson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

We conducted a feasibility study of machine-learning to predict progression of cognitive impairment to Alzheimer's disease (AD) among individuals enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our approach uses diverse participant information including genetic, imaging, biomarker, and neuropsychological data to predict transition to dementia in three clinical scenarios: short-term prediction (half or one year) based on a single assessment (simulating a "new patient" visit), short-term prediction based on information from two time points (simulating a "follow up" visit), and long-term (multiple years) prediction (simulating ongoing follow-up with repeated opportunities for assessment).

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Conference on Healthcare Informatics, ICHI 2015
EditorsWai-Tat Fu, Prabhakaran Balakrishnan, Sanda Harabagiu, Fei Wang, Jaideep Srivatsava
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages1
ISBN (Electronic)9781467395489
DOIs
StatePublished - Dec 8 2015
Event3rd IEEE International Conference on Healthcare Informatics, ICHI 2015 - Dallas, United States
Duration: Oct 21 2015Oct 23 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Healthcare Informatics, ICHI 2015

Other

Other3rd IEEE International Conference on Healthcare Informatics, ICHI 2015
CountryUnited States
CityDallas
Period10/21/1510/23/15

Keywords

  • data mining
  • dementia progression
  • machine learning

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  • Cite this

    Chi, C. L., Oh, W., & Borson, S. (2015). Feasibility Study of a Machine Learning Approach to Predict Dementia Progression. In W-T. Fu, P. Balakrishnan, S. Harabagiu, F. Wang, & J. Srivatsava (Eds.), Proceedings - 2015 IEEE International Conference on Healthcare Informatics, ICHI 2015 [7349729] (Proceedings - 2015 IEEE International Conference on Healthcare Informatics, ICHI 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI.2015.68