Will Sorafenib Help?: Treatment-aware Reranking in Precision Medicine Search

Abstract

High-quality evidence from the biomedical literature is crucial for decision making of oncologists who treat cancer patients. Search for evidence on a specific treatment for a patient is the challenge set by the precision medicine track of TREC in 2020. To address this challenge, we propose a two-step method to incorporate treatment into the query formulation and ranking. Training of such ranking function uses a zero-shot setup to incorporate the novel focus on treatments which did not exist in any of the previous TREC tracks. Our treatment-aware neural reranking approach, FAT, achieves state-of-the-art effectiveness for TREC Precision Medicine 2020. Our analysis indicates that the BERT-based rerankers automatically learn to score documents through identifying concepts relevant to precision medicine, similar to hand-crafted heuristics successful in the earlier studies.