Using Machine Reading to Understand Alzheimer’s and Related Diseases from the Literature
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.
© 2017 Satoshi Tsutsui, Yi Bu, Ying Ding, published by Chinese Academy of Sciences, National Science Library
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.