Code for developing and validating HIVE model for Appendicitis Prediction (XGBoost)

This repository contains the code and trained model for developing the <b>HIVE (History, Intake, Vitals, Examination)</b> machine learning model for predicting appendicitis in patients presenting with acute abdominal pain at the Emergency Department (ED).The HIVE model was developed using structured data derived from ED intake forms, vital signs, and clinical signs and symptoms extracted from free-text ED reports. These clinical features were either manually annotated or automatically extracted using a large language model (LLM), depending on the experiment.The codebase includes:Preprocessing scripts for merging structured and unstructured inputsFeature engineering and selection stepsModel development using XGBoostHyperparameter tuning via OptunaEvaluation procedures including AUROC calculation and bootstrapping for confidence intervalsThe repository also includes a <b>pickled version of the final trained model</b> (developed on 268 training cases), which can be used to generate appendicitis risk predictions on new patient data when provided with the appropriate input features.This repository is linked to the paper <b><i>LLM-automated extraction of clinical signs and symptoms from Emergency Department reports for machine learning prediction models</i></b>.