  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.
