@Article{info:doi/10.2196/13995,
author="Grethlein, David
and Winston, Flaura Koplin
and Walshe, Elizabeth
and Tanner, Sean
and Kandadai, Venk
and Onta{\~{n}}{\'o}n, Santiago",
title="Simulator Pre-Screening of Underprepared Drivers Prior to Licensing On-Road Examination: Clustering of Virtual Driving Test Time Series Data",
journal="J Med Internet Res",
year="2020",
month="Jun",
day="18",
volume="22",
number="6",
pages="e13995",
keywords="simulated driving assessment; on-road exam; machine learning; adolescent; child; support vector machines; humans; accidents, traffic; cause of death; licensure; automobile driving; motor vehicle; motor vehicles",
abstract="Background: A large Midwestern state commissioned a virtual driving test (VDT) to assess driving skills preparedness before the on-road examination (ORE). Since July 2017, a pilot deployment of the VDT in state licensing centers (VDT pilot) has collected both VDT and ORE data from new license applicants with the aim of creating a scoring algorithm that could predict those who were underprepared. Objective: Leveraging data collected from the VDT pilot, this study aimed to develop and conduct an initial evaluation of a novel machine learning (ML)--based classifier using limited domain knowledge and minimal feature engineering to reliably predict applicant pass/fail on the ORE. Such methods, if proven useful, could be applicable to the classification of other time series data collected within medical and other settings. Methods: We analyzed an initial dataset that comprised 4308 drivers who completed both the VDT and the ORE, in which 1096 (25.4{\%}) drivers went on to fail the ORE. We studied 2 different approaches to constructing feature sets to use as input to ML algorithms: the standard method of reducing the time series data to a set of manually defined variables that summarize driving behavior and a novel approach using time series clustering. We then fed these representations into different ML algorithms to compare their ability to predict a driver's ORE outcome (pass/fail). Results: The new method using time series clustering performed similarly compared with the standard method in terms of overall accuracy for predicting pass or fail outcome (76.1{\%} vs 76.2{\%}) and area under the curve (0.656 vs 0.682). However, the time series clustering slightly outperformed the standard method in differentially predicting failure on the ORE. The novel clustering method yielded a risk ratio for failure of 3.07 (95{\%} CI 2.75-3.43), whereas the standard variables method yielded a risk ratio for failure of 2.68 (95{\%} CI 2.41-2.99). In addition, the time series clustering method with logistic regression produced the lowest ratio of false alarms (those who were predicted to fail but went on to pass the ORE; 27.2{\%}). Conclusions: Our results provide initial evidence that the clustering method is useful for feature construction in classification tasks involving time series data when resources are limited to create multiple, domain-relevant variables. ",
issn="1438-8871",
doi="10.2196/13995",
url="https://www.jmir.org/2020/6/e13995",
url="https://doi.org/10.2196/13995",
url="http://www.ncbi.nlm.nih.gov/pubmed/32554384"
}