Journal article
Learning classification models of cognitive conditions from subtle behaviors in the digital Clock Drawing Test
Machine learning, v 102(3), pp 393-441
01 Mar 2016
PMID: 27057085
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The Clock Drawing Test-a simple pencil and paper test-has been used for more than 50 years as a screening tool to differentiate normal individuals from those with cognitive impairment, and has proven useful in helping to diagnose cognitive dysfunction associated with neurological disorders such as Alzheimer's disease, Parkinson's disease, and other dementias and conditions. We have been administering the test using a digitizing ballpoint pen that reports its position with considerable spatial and temporal precision, making available far more detailed data about the subject's performance. Using pen stroke data from these drawings categorized by our software, we designed and computed a large collection of features, then explored the tradeoffs in performance and interpretability in classifiers built using a number of different subsets of these features and a variety of different machine learning techniques. We used traditional machine learning methods to build prediction models that achieve high accuracy. We operationalized widely used manual scoring systems so that we could use them as benchmarks for our models. We worked with clinicians to define guidelines for model interpretability, and constructed sparse linear models and rule lists designed to be as easy to use as scoring systems currently used by clinicians, but more accurate. While our models will require additional testing for validation, they offer the possibility of substantial improvement in detecting cognitive impairment earlier than currently possible, a development with considerable potential impact in practice.
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Details
- Title
- Learning classification models of cognitive conditions from subtle behaviors in the digital Clock Drawing Test
- Creators
- William Souillard-Mandar - MIT, Comp Sci & Artificial Intelligence Lab, 77 Massachusetts Ave, Cambridge, MA 02139 USARandall Davis - MIT, Comp Sci & Artificial Intelligence Lab, 77 Massachusetts Ave, Cambridge, MA 02139 USACynthia Rudin - MIT, Comp Sci & Artificial Intelligence Lab, 77 Massachusetts Ave, Cambridge, MA 02139 USARhoda Au - Boston UniversityDavid J. Libon - Drexel UniversityRodney Swenson - University of North DakotaCatherine C. Price - University of FloridaMelissa Lamar - University of Illinois at ChicagoDana L. Penney - Lahey Hospital and Medical Center
- Publication Details
- Machine learning, v 102(3), pp 393-441
- Publisher
- Springer Nature
- Number of pages
- 49
- Grant note
- Robert E. Wise Research and Education Institution RO1-MH073989 / National Institute on Mental Health; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Mental Health (NIMH) D13AP00008 / Defense Advanced Research Projects Agency; United States Department of Defense; Defense Advanced Research Projects Agency (DARPA) R01 AG0333040; AG16492; AG08122 / National Institutes on Aging; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute on Aging (NIA) IIS-1404494 / National Science Foundation; National Science Foundation (NSF) N01-HC25195 / National Heart, Lung, and Blood Institute; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Heart Lung & Blood Institute (NHLBI) UL1TR000064 / IH/NCATS Clinical and Translational Science Award R01-NS17950; K23-NS60660; R01-NS082386 / National Institute of Neurological Disorders and Stroke; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Neurological Disorders & Stroke (NINDS) University of Florida Movement Disorders and Neurorestoration
- Resource Type
- Journal article
- Language
- English
- Web of Science ID
- WOS:000371459700005
- Scopus ID
- 2-s2.0-84959540974
- Other Identifier
- 991021901013804721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence