Learning Classification Models of Cognitive Conditions from Subtle Behaviors in the Digital Clock Drawing Test

By William Souillard-Mandar, Randall Davis, Cynthia Rudin, Rhoda Au, David J. Libon, Rodney Swenson, Catherine C. Price, Melissa Lamar, Dana L. Penney. 2015

165 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Industrial and Manufacturing Engineering, Mechanical Engineering, Medical & Health AI

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

  1. Cynthia Rudin
  2. Berk Ustun

In Industrial and Manufacturing Engineering

  1. Berk Ustun
  2. Cynthia Rudin

In Medical & Health AI

  1. Cynthia Rudin
  2. Berk Ustun
  3. Tong Wang
  4. Marco Tulio Ribeiro
  5. Carlos Guestrin
  6. Sameer Singh
  7. Ş. Ertekin
  8. Nadia Burkart

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