Using Machine Reading to Understand Alzheimer’s and Related Diseases from the Literature
Satoshi Tsutsui, Yi Bu, Ying Ding · Journal of Data and Information Science · 2017
Abstract 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.