@article{ref1,
title="Simulator pre-screening of underprepared drivers prior to licensing on-road examination: clustering of virtual driving test time series data",
journal="Journal of medical internet research",
year="2020",
author="Grethlein, David and Winston, Flaura Koplin and Walshe, Elizabeth and Tanner, Sean and Kandadai, Venk and Ontañón, Santiago",
volume="22",
number="6",
pages="e13995-e13995",
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. <br><br>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. <br><br>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). <br><br>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%). <br><br>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.<br><br>©David Grethlein, Flaura Koplin Winston, Elizabeth Walshe, Sean Tanner, Venk Kandadai, Santiago Ontañón. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 18.06.2020.<p /> <p>Language: en</p>",
language="en",
issn="1438-8871",
doi="10.2196/13995",
url="http://dx.doi.org/10.2196/13995"
}