TY - JOUR AU - Subramanian, Shivashankar AU - Baldini, Ioana AU - Ravichandran, Sushma AU - Katz-Rogozhnikov, Dmitriy A. AU - Natesan Ramamurthy, Karthikeyan AU - Sattigeri, Prasanna AU - Varshney, Kush R. AU - Wang, Annmarie AU - Mangalath, Pradeep AU - Kleiman, Laura B. PY - 2020/04/03 Y2 - 2024/03/29 TI - A Natural Language Processing System for Extracting Evidence of Drug Repurposing from Scientific Publications JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 08 SE - IAAI Technical Track: Emerging Papers DO - 10.1609/aaai.v34i08.7052 UR - https://ojs.aaai.org/index.php/AAAI/article/view/7052 SP - 13369-13381 AB - <p>More than 200 generic drugs approved by the U.S. Food and Drug Administration for non-cancer indications have shown promise for treating cancer. Due to their long history of safe patient use, low cost, and widespread availability, repurposing of these drugs represents a major opportunity to rapidly improve outcomes for cancer patients and reduce healthcare costs. In many cases, there is already evidence of efficacy for cancer, but trying to manually extract such evidence from the scientific literature is intractable. In this emerging applications paper, we introduce a system to automate non-cancer generic drug evidence extraction from PubMed abstracts. Our primary contribution is to define the natural language processing pipeline required to obtain such evidence, comprising the following modules: querying, filtering, cancer type entity extraction, therapeutic association classification, and study type classification. Using the subject matter expertise on our team, we create our own datasets for these specialized domain-specific tasks. We obtain promising performance in each of the modules by utilizing modern language processing techniques and plan to treat them as baseline approaches for future improvement of individual components.</p> ER -