Explaining Machine Learning: A Deeper Look into Admission Prediction

Abstract

The popularization of artificial intelligence solutions in both research and industry that has been occurring due to the rise of tools such as the GPT, Gemini and Claude large language models has revitalized research in the area. There are many possible uses within the medical field, but a key determinant of the adoption of new tools by medical professionals is trust. To augment tool trust, the tool must be made understandable and explainable, but this is a problem for “black box” machine learning models. In an effort to promote transparency, we have performed a deep study of the reasoning behind an XGBoost machine learning model that performed well in the task of inpatient admission prediction.