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

作     者：Satoshi Tsutsui Yi Bu Ying Ding 

作者机构：School of InformaticsComputingand EngineeringIndiana UniversityBloomingtonIN 47408USA School of Information ManagementWuhan UniversityWuhan 430072China 

出 版 物：《Journal of Data and Information Science》 (数据与情报科学学报（英文版）)

年 卷 期：2017年第2卷第4期

页      面：81-94页

核心收录：

学科分类：12[管理学] 1205[管理学-图书情报与档案管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 1002[医学-临床医学] 100203[医学-老年医学] 10[医学] 

主　　题：Machine reading Alzheimer＇s disease Knowledge discovery Data mining 

摘      要：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 A1DS/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. Origlnality/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.