A Medical Literature Search System for Identifying Effective Treatments in Precision Medicine

TL;DR

This paper presents a specialized literature search system for precision medicine that enhances retrieval of effective cancer treatments by expanding medical terminology and employing machine learning reranking, achieving high recall and precision.

Contribution

The system combines knowledge-based term expansion with a machine learning reranker to improve retrieval accuracy for treatment-related biomedical literature in precision medicine.

Findings

Improved recall and precision in literature retrieval

Achieved performance comparable to top leaderboard entries

Effective identification of treatment reports for cancer patients

Abstract

The Precision Medicine Initiative states that treatments for a patient should take into account not only the patient's disease, but his/her specific genetic variation as well. The vast biomedical literature holds the potential for physicians to identify effective treatment options for a cancer patient. However, the complexity and ambiguity of medical terms can result in vocabulary mismatch between the physician's query and the literature. The physician's search intent (finding treatments instead of other types of studies) is difficult to explicitly formulate in a query. Therefore, simple ad hot retrieval approach will suffer from low recall and precision. In this paper, we propose a new retrieval system that helps physicians identify effective treatments in precision medicine. Given a cancer patient with a specific disease, genetic variation, and demographic information, the system aims to identify biomedical publications that report effective treatments. We approach this goal from two directions. First, we expand the original disease and gene terms using biomedical knowledge bases to improve recall of the initial retrieval. We then improve precision by promoting treatment-related publications to the top using a machine learning reranker trained on 2017 Text Retrieval Conference Precision Medicine (PM) track corpus. Batch evaluation results on 2018 PM track corpus show that the proposed approach effectively improves both recall and precision, achieving performance comparable to the top entries on the leaderboard of 2018 PM track.