Pediatric injuries classification using openAI: new opportunities for epidemiological surveillance

Abstract

Abstract Background The burden of unintentional pediatric injuries remains a significant concern for healthcare systems worldwide. Timely and accurate surveillance is critical for injury prevention and resource allocation. However, the diagnosis reported in the emergency department records is often not coded, and the manual coding of these records is labor-intensive, time-consuming, and prone to errors. To implement an automatic coding system using GPT-based models to extract and classify injury data in the Italian language from pediatric emergency department records. Methods The study included 283,468 admission records to the pediatric Emergency Department of Padova University Hospital from 2007 to 2018. Each access is mandatorily registered in an electronic data collection system. For each emergency department access, both coded (administrative and demographic data) and free-text (diagnosis) information are reported. A random subset of 40,030 records underwent classification of free-text diagnosis (as injury or not) by an expert clinician (gold standard). OpenAI was used for the classification task. OpenAI is an extensive language model based on the GPT architecture. Specifically, the GPT-3.5 variant was employed for the present work. openAI was accessed through a public application programming interface (API) using R software. Results Preliminary results showed a classification accuracy of 96.2%. The tool's ability to correctly classify the injuries (sensitivity) was 95%, while the specificity was 96.5%. Conclusions The performance of the classification task was excellent. The present results demonstrate the feasibility of GPT-based models for processing unstructured free text information from medical records. Key messages • OpenAI would represent a promising opportunity to improve pediatric injuries surveillance • OpenAI would improve the classification of unstructured information from medical records