@article{ref1,
title="Field measures are all you need: predicting need for surgery in elderly ground-level fall patients via machine learning",
journal="American surgeon",
year="2023",
author="Shooshani, Tara and Pooladzandi, Omead and Nguyen, Andrew and Shipley, Jonathan H. and Harris, Mark H. and Hovis, Gabrielle E. A. and Barrios, Cristobal",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="BACKGROUND: As ground-level falls (GLFs) are a significant cause of mortality in elderly patients, field triage plays an essential role in patient outcomes. This research investigates how machine learning algorithms can supplement traditional t-tests to recognize statistically significant patterns in medical data and to aid clinical guidelines. <br><br>METHODS: This is a retrospective study using data from 715 GLF patients over 75 years old. We first calculated P-values for each recorded factor to determine the factor's significance in contributing to a need for surgery (P <.05 is significant). We then utilized the XGBoost machine learning method to rank contributing factors. We applied SHapley Additive exPlanations (SHAP) values to interpret the feature importance and provide clinical guidance via decision trees. <br><br>RESULTS: The three most significant P-values when comparing patients with and without surgery are as follows: Glasgow Coma Scale (GCS) (P <.001), no comorbidities (P <.001), and transfer-in (P =.019). The XGBoost algorithm determined that GCS and systolic blood pressure contribute most strongly. The prediction accuracy of these XGBoost results based on the test/train split was 90.3%. <br><br>DISCUSSION: When compared to P-values, XGBoost provides more robust, detailed results regarding the factors that suggest a need for surgery. This demonstrates the clinical applicability of machine learning algorithms. Paramedics can use resulting decision trees to inform medical decision-making in real time. XGBoost's generalizability power increases with more data and can be tuned to prospectively assist individual hospitals.<p /> <p>Language: en</p>",
language="en",
issn="0003-1348",
doi="10.1177/00031348231177917",
url="http://dx.doi.org/10.1177/00031348231177917"
}