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New computer models aim to classify, help reduce injury accidents
Date:9/2/2009

WEST LAFAYETTE, Ind. - Researchers are developing computer models to comb through thousands of injury reports in large administrative medical datasets or insurance claims data to automatically classify them based on specific words or phrases.

"One goal is to identify the most important causes of injuries so that efforts could be directed toward reducing the burden of injuries in society," said Mark Lehto, an associate professor in Purdue University's School of Industrial Engineering.

The reports, usually filled out by employers, health-care professionals or claimants themselves, are currently classified by manual coders hired by users such as the National Center for Health Statistics, hospital staff or insurance industry handlers who review thousands of "injury narratives" included in reports.

"This is obviously very labor-intensive," Lehto said.

The Purdue engineer and researchers at the Liberty Mutual Research Institute for Safety in Hopkinton, Mass., assigned codes to injury reports from workers' compensation claims using two different models developed with a technique called "Bayesian methods."

"The predictions were quite good," Lehto said. "The results were comparable to the human coders. The accuracy is surprising considering all of the misspellings, run-on words, abbreviations and inconsistent or missing punctuations seen in these workers' compensation claim narratives."

An example of an injury-claim narrative included in the paper is: "HUSB. & SON WERE REARENDED AT RED TRAFFIC LIGHT BY DRUNKEN DRIVER DRIV-ING AT LEAST 45 MPH INFULL SIZE PICK-UP TRUCK//N."

"Can you imagine reading through 10,000 of these narratives and trying to interpret what the cause of injury is and assign different codes?" said Lehto, the 2008 Liberty Mutual Research Institute for Safety visiting scholar.

Research findings were detailed in a paper published in August in the journal Injury Prevention
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Contact: Emil Venere
venere@purdue.edu
765-494-4709
Purdue University
Source:Eurekalert

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