Programmatic Theme: Translational Bioinformatics

Abstract: Precision medicine focuses on developing new treatments based on an individual’s genetic, environmental, and lifestyle profile. While this data-driven approach has led to significant advances, retrieving information specific to a patient’s condition has proved challenging for physicians due to the large volume of data. In this paper, we propose the PRecIsion Medicine Robust Oncology Search Engine (PRIMROSE) for cancer patients that retrieves scientific articles and clinical trials based on a patient’s condition, genetic profile, age, and gender. Our search engine utilizes Elasticsearch indexes for information storage and retrieval, and we developed a knowledge graph for query expansion in order to improve recall. Additionally, we added machine learning and learning-to-rank components to the search engine and compared the results of the two approaches. Finally, we developed a front-facing ReactJS website and a REST API for connecting with our search engine. The development of this front-facing website allows for easy access to our system by healthcare providers.

Learning Objective: Understand the need for a user accessible search engine for precision medicine documents.
Learn approaches to retrieve relavent precision medicine information

Authors:

Samuel Shenoi (Presenter)
Baylor University

Vi Ly, University of Houston, Downtown
Sarvesh Soni, University of Texas Health Science Center at Houston
Kirk Roberts, University of Texas Health Science Center at Houston