# Application of machine learning models on predicting the length of hospital stay in fragility fracture patients

## TL;DR

This paper uses machine learning to predict hospital stays for elderly patients with fragility fractures, aiming to improve healthcare efficiency.

## Contribution

The study introduces machine learning models to predict prolonged hospital stays in fragility fracture patients, a novel application in this field.

## Key findings

- The Wide & Deep model achieved an accuracy of 0.79 and an AUC-ROC of 0.84 in predicting hospital stay duration.
- Hospital type, patient mental state, and acute hospital stay length were identified as key predictors of prolonged stays.
- Machine learning can help healthcare professionals identify high-risk patients and optimize resource allocation.

## Abstract

The rate of geriatric hip fracture in Hong Kong is increasing steadily and associated mortality in fragility fracture is high. Moreover, fragility fracture patients increase the pressure on hospital bed demand. Hence, this study aims to develop a predictive model on the length of hospital stay (LOS) of geriatric fragility fracture patients using machine learning (ML) techniques.

In this study, we use the basic information, such as gender, age, residence type, etc., and medical parameters of patients, such as the modified functional ambulation classification score (MFAC), elderly mobility scale (EMS), modified Barthel index (MBI) etc, to predict whether the length of stay would exceed 21 days or not.

Our results are promising despite the relatively small sample size of 8000 data. We develop various models with three approaches, namely (1) regularizing gradient boosting frameworks, (2) custom-built artificial neural network and (3) Google’s Wide & Deep Learning technique. Our best results resulted from our Wide & Deep model with an accuracy of 0.79, with a precision of 0.73, with an area under the receiver operating characteristic curve (AUC-ROC) of 0.84. Feature importance analysis indicates (1) the type of hospital the patient is admitted to, (2) the mental state of the patient and (3) the length of stay at the acute hospital all have a relatively strong impact on the length of stay at palliative care.

Applying ML techniques to improve the quality and efficiency in the healthcare sector is becoming popular in Hong Kong and around the globe, but there has not yet been research related to fragility fracture. The integration of machine learning may be useful for health-care professionals to better identify fragility fracture patients at risk of prolonged hospital stays. These findings underline the usefulness of machine learning techniques in optimizing resource allocation by identifying high risk individuals and providing appropriate management to improve treatment outcome.