Predicting Negative Side Effects of Surgeries Through Clustering

Abstract

Prescribed treatments to patients often result in side effects that may not be known beforehand. Side effects analysis research focuses on specific treatments and targets small groups of patients. In previous work, we presented methods for extracting treatment effects from the Florida State Inpatient Databases (SID), which contain over 2.5 million visit discharges from 1.5 million patients. We classified these effects into positive, neutral, and negative effects. In addition, we proposed an approach for clustering patients based on negative side effects and analyzed them. As an extension to this work, We believe that a system identifying the cluster membership of a patient prior to applying the procedure is highly beneficial. In this paper, we extended our work and introduced a new approach for predicting patients' negative side effects before applying a given meta-action (or procedure). We propose a system that measures the similarity of a new patient to existing clusters, and makes a personalized decision on the patient's most likely negative side effects. We further evaluate our system using SID, which is part of the Healthcare Cost and Utilization Project (HCUP). Our experiments validated our approach and produced desired results.