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An analysis of a digital variant of the Trail Making Test using machine learning techniques
- Source :
- Technology and Health Care. 25:251-264
- Publication Year :
- 2017
- Publisher :
- IOS Press, 2017.
-
Abstract
- BACKGROUND The goal of this work is to develop a digital version of a standard cognitive assessment, the Trail Making Test (TMT), and assess its utility. OBJECTIVE This paper introduces a novel digital version of the TMT and introduces a machine learning based approach to assess its capabilities. METHODS Using digital Trail Making Test (dTMT) data collected from (N = 54) older adult participants as feature sets, we use machine learning techniques to analyze the utility of the dTMT and evaluate the insights provided by the digital features. RESULTS Predicted TMT scores correlate well with clinical digital test scores (r = 0.98) and paper time to completion scores (r = 0.65). Predicted TICS exhibited a small correlation with clinically derived TICS scores (r = 0.12 Part A, r = 0.10 Part B). Predicted FAB scores exhibited a small correlation with clinically derived FAB scores (r = 0.13 Part A, r = 0.29 for Part B). Digitally derived features were also used to predict diagnosis (AUC of 0.65). CONCLUSION Our findings indicate that the dTMT is capable of measuring the same aspects of cognition as the paper-based TMT. Furthermore, the dTMT's additional data may be able to help monitor other cognitive processes not captured by the paper-based TMT alone.
- Subjects :
- 050103 clinical psychology
Computer science
Trail Making Test
Biomedical Engineering
Biophysics
Health Informatics
Bioengineering
Machine learning
computer.software_genre
Article
Machine Learning
Biomaterials
Correlation
03 medical and health sciences
Cognition
0302 clinical medicine
Feature (machine learning)
Humans
0501 psychology and cognitive sciences
Problem Solving
Aged
business.industry
05 social sciences
Middle Aged
Test (assessment)
Artificial intelligence
Cognitive Assessment System
Completion time
business
human activities
computer
030217 neurology & neurosurgery
Information Systems
Subjects
Details
- ISSN :
- 18787401 and 09287329
- Volume :
- 25
- Database :
- OpenAIRE
- Journal :
- Technology and Health Care
- Accession number :
- edsair.doi.dedup.....1199ac5f3d334055dbcf52c3f7e9e001