Shenoi Samuel J, Ly Vi, Soni Sarvesh, Roberts Kirk
Baylor University, Waco, Texas.
University of Houston-Downtown, Houston, Texas.
AMIA Jt Summits Transl Sci Proc. 2020 May 30;2020:579-588. eCollection 2020.
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 oncologists 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 experimented with 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.
精准医学专注于根据个体的基因、环境和生活方式概况开发新的治疗方法。虽然这种数据驱动的方法已经带来了重大进展，但由于数据量庞大，肿瘤学家要获取特定于患者病情的信息已被证明具有挑战性。在本文中，我们为癌症患者提出了精准医学稳健肿瘤搜索引擎（PRIMROSE），它可以根据患者的病情、基因概况、年龄和性别检索科学文章和临床试验。我们的搜索引擎利用Elasticsearch索引进行信息存储和检索，并且我们开发了一个知识图谱用于查询扩展，以提高召回率。此外，我们在搜索引擎中试验了机器学习和排序学习组件，并比较了这两种方法的结果。最后，我们开发了一个面向前端的ReactJS网站和一个用于与我们的搜索引擎连接的REST API。这个面向前端的网站的开发使医疗保健提供者能够轻松访问我们的系统。
