# Novel deep learning strategy to translate ICD Codes to the Abbreviated Injury Scale

> **NIH NIH R03** · UNIVERSITY OF VIRGINIA · 2022 · $80,750

## Abstract

PROJECT SUMMARY
Trauma is one of the leading causes of death and disability in the US and around the world. Accurate
measurement is critical to improving our understanding of this disease and gauging the effectiveness of
interventions. Tracking the burden of traumatic injuries relies on not only identifying deaths, but also non-fatal
injuries. The widely used International Classification of Disease (ICD) diagnosis coding system, developed by
the World Health Organization. does not have a mechanism for directly measuring injury severity. In order to
measure in severity, ICD codes are often converted to the Abbreviated Injury Scale (AIS). Each AIS code has a
measure of relative injury severity, and multiple codes can be combined to determine the overall injury severity
of an individual patients. However, the currently used methods for conversion of ICD to AIS rely on one-to-one
mapping between these coding systems, which has many inherent difficulties. Specifically, these one-to-one
mappings have been shown to systematically underestimate overall injury severity. Recent advances in
computation linguistics have solved very similar problems with the use of embedding and deep learning. We
intended to apply these techniques ICD to AIS translations. The key innovation is to consider all the information
available about a patient simultaneously, rather than converting each code in isolation. This objective of this
R03 proposal is to develop tools that improve the accuracy of population-level injury research that uses ICD
codes. We will accomplish this objective by: (1) developing a tool to predict overall injury severity for individual
patients from ICD codes, and (2) developing a tool to translate ICD codes to AIS for individual patients. Modern
language translation has algorithms are based on determining the location of words in an embedded space, so
words with similar meaning are near to each other and the relative locations encode relationships between words.
Similarly, we will transfer ICD into an embedded space, which will be used by subsequent deep learning modules
produce our results. There is data for millions of trauma patients collected in in the National Trauma Data Bank
(NTDB) that contains both ICD and AIS extracted by expert coders. We will use this data to train and evaluate
the deep learning models that will underlie our tools. Together, these tools will meet the critical needs to improve
the quality of trauma research and increase the accuracy of injury monitoring using administrative medical
databases.

## Key facts

- **NIH application ID:** 10378868
- **Project number:** 1R03TR004015-01
- **Recipient organization:** UNIVERSITY OF VIRGINIA
- **Principal Investigator:** Thomas Ryan Hartka
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $80,750
- **Award type:** 1
- **Project period:** 2021-12-01 → 2023-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10378868

## Citation

> US National Institutes of Health, RePORTER application 10378868, Novel deep learning strategy to translate ICD Codes to the Abbreviated Injury Scale (1R03TR004015-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10378868. Licensed CC0.

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