Evidence map›Paper›PMID 27057085›Full record

ArticleMachine learning2016

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

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

Registry-linked trialOpen access · bronzeAbstract read
In one paragraph

Article in Machine learning, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03238001 (Evaluation of DCTclock™ as a Cognitive Assessment Aid), which is not on this map. Cited by 75 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
75citing papers in PubMed, 2 pooled it
7.6field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03238001 nacompletednot on this mapstarted 2017, after this paper: background citation

Evaluation of DCTclock™ as a Cognitive Assessment Aid

TypeinterventionalSponsorDigital Cognition TechnologiesRan2017 to 2017Enrolled450ConditionsCognitive FunctionArmsDCTclock
3 · Its place in the literature

Who cites it

75 citing papers in PubMed, 2 syntheses or guidelines pooled it, 175 citations in OpenAlex.

  1. Pooled it
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15 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 1 country.

William Souillard-MandarMIT Computer Science And Artificial Intelligence Laboratory, Tel.: +1-617-800-3033, souillardmandar@csail.mit.edu.
Randall DavisMIT Computer Science And Artificial Intelligence Laboratory, Tel.: +1-617-253-5879, davis@csail.mit.edu.
Cynthia RudinMIT Computer Science And Artificial Intelligence Laboratory, rudin@mit.edu.
Rhoda AuBoston University School of Medicine, rhodaau@bu.edu.
David J LibonDrexel Neuroscience Institute, Drexel University College of Medicine, dlibon@drexelmed.edu.
Rodney SwensonUniversity of North Dakota Medical School, rod@neuropsychnd.com.
Catherine C PriceUniversity of Florida, Gainesville, cep23@phhp.ufl.edu.
Melissa LamarUniversity of Illinois, Chicago, mlamar@psych.uic.edu.
Dana L PenneyLahey Health, Burlington, Massachusetts, dana.l.penney@lahey.org.
Drexel University · USLahey Hospital and Medical Center · USUniversity of Florida · USUniversity of Illinois Chicago · USUniversity of North Dakota · US

Funding

Using social networks to map and evaluate team science across CTSA hubsUL1TR001427 · NCATS · UNIVERSITY OF FLORIDA · PI MITCHELL, DUANE A. · 2015 to 2024
$37.2M
PRECURSORS OF STROKE INCIDENCE AND PROGNOSISR01NS017950 · NINDS · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI Hugo Javier Aparicio, Jose Rafael Romero · 1985 to 2026
$22.9M
EPIDEMIOLOGY OF DEMENTIA IN THE FRAMINGHAM STUDYR01AG008122 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI AU, RHODA, SESHADRI, SUDHA · 1989 to 2015
$14.4M
MRI, Genetics &Cognitive Precursors of AD &DementiaR01AG016495 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI AU, RHODA · 1999 to 2016
$12.5M
UF Clinical and Translational Science AwardUL1TR000064 · NCATS · UNIVERSITY OF FLORIDA · PI NELSON, DAVID R · 2012 to 2014
$12.2M
MRI, Cognitive, Genetic & Biomarker Precursors of AD & Dementia in Young AdultsR01AG033040 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI AU, RHODA, SESHADRI, SUDHA · 2009 to 2019
$11.2M
Cortical/Subcortical Circuits in Late-Life DepressionR01MH073989 · NIMH · UNIVERSITY OF ILLINOIS AT CHICAGO · PI KUMAR, ANAND · 2006 to 2010
$2.6M
White Matter Connectivity and PD Cognitive PhenotypesR01NS082386 · NINDS · UNIVERSITY OF FLORIDA · PI PRICE, CATHERINE E · 2013 to 2017
$2.3M
White Matter and Cognition in Parkinson's DiseaseK23NS060660 · NINDS · UNIVERSITY OF FLORIDA · PI PRICE, CATHERINE E · 2007 to 2011
$619k
THE FRAMINGHAM HEART STUDY-N01HC25195-268025195-268025195N01HC025195 · HC · TRUSTEES OF BOSTON UNIVERSITY · PI WOLF, PHILIP A · 2002 to 2006
–
NCATS NIH HHS UL1 TR000064NCATS NIH HHS UL1 TR001427NHLBI NIH HHS N01 HC025195NIA NIH HHS R01 AG008122NIA NIH HHS R01 AG016495NIA NIH HHS R01 AG033040NIMH NIH HHS R01 MH073989NINDS NIH HHS K23 NS060660NINDS NIH HHS R01 NS017950NINDS NIH HHS R01 NS082386
6 · The paper itself

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.

Indexed as

Clock Drawing TestCognitive Impairment DiagnosticsInterpretable Machine LearningMachine Learning ApplicationsMedical Scoring Systems

Identifiers

PMID27057085
PMCPMC4821477
OpenAlexW2177693422

What OpenQuestion holds

Textmetadata
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Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.