Clustering-aided approach for predicting patient outcomes with application to elderly healthcare in Ireland

Predictive analytics have proved promising capabilities and opportunities to many aspects of healthcare practice. Data-driven insights can provide an important part of the solution for curbing rising costs and improving care quality. The paper implements machine learning techniques in an attempt to support decision making in relation to elderly healthcare in Ireland, with a particular focus on hip fracture care. We adopt a combination of unsupervised and supervised learning for predicting patient outcomes. Initially, elderly patients are grouped based on the similarity of age, length of stay (LOS) and elapsed time to surgery. Using the K-Means algorithm, our clustering experiments suggest the presence of three coherent clusters of patients. Subsequently, the discovered clusters are utilised to train prediction models that address a particular cluster of patients individually. In particular, two machine learning models are trained for every cluster of patients in order to predict the inpatient LOS, and discharge destination. The developed models are claimed to make predictions with relatively high accuracy. Furthermore, the potential usefulness of the clustering-guided approach of prediction is discussed in general.