%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 22
%N 6
%P e13995
%T Simulator Pre-Screening of Underprepared Drivers Prior to Licensing On-Road Examination: Clustering of Virtual Driving Test Time Series Data
%A Grethlein,David
%A Winston,Flaura Koplin
%A Walshe,Elizabeth
%A Tanner,Sean
%A Kandadai,Venk
%A Ontañón,Santiago
%+ Diagnostic Driving, Inc, 705 S 50th Street, Philadelphia, PA, 19143, United States, 1 215 315 3204, david@diagnosticdriving.com
%K simulated driving assessment
%K on-road exam
%K machine learning
%K adolescent
%K child
%K support vector machines
%K humans
%K accidents, traffic
%K cause of death
%K licensure
%K automobile driving
%K motor vehicle
%K motor vehicles
%D 2020
%7 18.6.2020
%9 Original Paper
%J J Med Internet Res
%G English
%X 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. 
%M 32554384
%R 10.2196/13995
%U https://www.jmir.org/2020/6/e13995
%U https://doi.org/10.2196/13995
%U http://www.ncbi.nlm.nih.gov/pubmed/32554384