Improving health records search using multiple query expansion collections

This work presents a system built upon statistical IR methods for searching flat-text health records for patients with particular conditions specified via a keyword query, which improves a strong baseline by 30% on mean average precision (MAP), and has a promising overall performance when compared with a manual system doing the same task.

Abstract

The increasing prevalence of electronic health records (EHR), along with the needs for enhanced clinical care, presents new challenges to information retrieval (IR). Many clinical decision-making tasks following the philosophy of Evidence-Based Medicine (EBM) rely on the ability to find relevant health records and gather sufficient clinical evidence under severe time constraints. In this work, we present a system built upon statistical IR methods for searching flat-text health records (i.e. the doctors' notes sections of EHR) for patients with particular conditions specified via a keyword query. In particular, we use multiple external repositories for query expansion, and introduce two novel model weighting methods. Cross-validation results show that our system improves a strong baseline by 30% on mean average precision (MAP), and has a promising overall performance when compared with a manual system doing the same task.