# CoAI: Cost-Aware Artificial Intelligence for Efficient Prehospital Diagnosis of Trauma Patients

> **NIH NIH F30** · UNIVERSITY OF WASHINGTON · 2021 · $51,036

## Abstract

Project Summary/Abstract
While artificial intelligence (AI) and machine learning (ML) are becoming widely used throughout medicine,
the analysis of the cost of an ML model’s predictions has been very limited. For example, an ML model may
accurately predict that a trauma patient will have acute traumatic coagulopathy (ATC), a bleeding disorder;
however, it may heavily rely on hard-to-measure patient features, like blood pressure or Glasgow Coma Score,
to do so. Standard ML techniques do not prioritize timely diagnosis, which is key to minimize death and injury.
This idea, which we refer to as cost-aware prediction, is a topic of recent interest in machine learning. However,
existing methods have substantial limitations, and their clinical impact has not been demonstrated. This
proposal will adopt recent advances in ML and explainable AI to 1) develop improved cost-aware prediction
techniques. 2) demonstrate their value using clinical data and 3) integrate them into the electronic medical
record. These methods will be applicable in many areas of science and medicine.
Aim 1. Develop a novel feature importance-based approach for cost-aware prediction. No existing approach
for cost-aware prediction consistently outperforms the others, and each has its own strengths and weaknesses.
This proposal uses recent discoveries in machine learning to design a new algorithm, CoAI, with new strengths
and fewer weaknesses. CoAI will substantially improve predictive performance, enable analysis on large
datasets, and flexibly work with any ML model. Preliminary results show that CoAI can outperform existing
methods. A new public benchmark for cost-aware prediction will be created and used to compare CoAI to
existing methods, and CoAI will be published as easy-to-use open-source software.
Aim 2. Evaluate CoAI’s potential for clinical time savings. CoAI’s ability to predict bleeding disorders will be
tested on an unprecedentedly detailed dataset that combines trauma hospital data with surveys of doctors and
paramedics. Comparing CoAI to the risk scores used in clinical practice will provide explicit estimates of how
much time CoAI can save and how many misdiagnoses it can prevent. In preliminary analysis with trauma
registry data, CoAI reduces prediction time and increases accuracy relative to an existing risk score.
Aim 3. Incorporate an interactive ML method into the medical record. CoAI will be integrated into the
electronic medical record (EMR), using feedback from professional paramedics. Quantitative estimates of time
and cost savings and subjective impressions will be gathered from paramedics, and open-ended interviews
will be conducted to assess their feelings about interactive machine learning methods like CoAI. These insights
will guide future research in interactive machine learning methods, as well as possible clinical work to study
CoAI’s impact on decision making in simulated trauma scenarios.
 Successful completion of this project will allow faster, mo...

## Key facts

- **NIH application ID:** 10440236
- **Project number:** 5F30HL151074-02
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Gabriel Erion Barner
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $51,036
- **Award type:** 5
- **Project period:** 2020-06-16 → 2023-06-15

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10440236

## Citation

> US National Institutes of Health, RePORTER application 10440236, CoAI: Cost-Aware Artificial Intelligence for Efficient Prehospital Diagnosis of Trauma Patients (5F30HL151074-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10440236. Licensed CC0.

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