Regression-Based Documents Reranking for Precision Medicine

Abstract

Precision medicine information retrieval (PM IR) is about matching the most relevant scientific articles to an individual patient for reliable disease treatment. To achieve effectiveness and efficiency, the task usually consists of two stages: conventional information retrieval and reranking. Many approaches have been proposed for reranking. However, the performance is still far from satisfactory. In this work, we propose a regression-based reranking scheme for PM IR which uses labelled data regardless of empirical knowledge from similar but not identical documents set. Experiments validate that the performance of our approach is significantly better than that of the state-of-the-art approaches.