An analysis of a digital variant of the Trail Making Test using machine learning techniques

作者：Dahmen Jessamyn^*; Cook Diane; Fellows Robert; Schmitter Edgecombe Maureen

来源：Technology and Health Care, 2017, 25(2): 251-264.

DOI：10.3233/THC-161274

• Computerized cognitive assessment
• design and validation
• machine learning
• mobile application
• TrailMaking Test

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.

• 出版日期2017