Using Machine Reading to Understand Alzheimer’s and Related Diseases from the Literature

• Main Authors: Satoshi Tsutsui, Yi Bu, Ying Ding
• Format: Article
• Language: English
• Published: Chinese Academy of Sciences 2017-12-01
• Series: Journal of Data and Information Science
• Subjects: Machine reading; Alzheimer's disease; Knowledge discovery
• Online Access: http://manu47.magtech.com.cn/Jwk3_jdis/article/2017/2096-157X/2096-157X-2-4-81.shtml
• ISSN: 2096-157X; 2096-157X

Purpose: This paper aims to better understand a large number of papers in the medical domain of Alzheimer’s disease (AD) and related diseases using the machine reading approach. Design/methodology/approach: The study uses the topic modeling method to obtain an overview of the field, and employs open information extraction to further comprehend the field at a specific fact level. Findings: Several topics within the AD research field are identified, such as the Human Immunodeficiency Virus (HIV)/Acquired Immune Deficiency Syndrome (AIDS), which can help answer the question of how AIDS/HIV and AD are very different yet related diseases. Research limitations: Some manual data cleaning could improve the study, such as removing incorrect facts found by open information extraction. Practical implications: This study uses the literature to answer specific questions on a scientific domain, which can help domain experts find interesting and meaningful relations among entities in a similar manner, such as to discover relations between AD and AIDS/HIV. Originality/value: Both the overview and specific information from the literature are obtained using two distinct methods in a complementary manner. This combination is novel because previous work has only focused on one of them, and thus provides a better way to understand an important scientific field using data-driven methods.