Distillation of Knowledge from the Research Literature on Alzheimer's Dementia

Many countries are aging societies. Since abilities generally deteriorate with age, technologies can assist older adults in their daily life. Loss of cognitive status is particularly severe in cases of dementia, with around 70% (according to Alzheimers.net) of dementia cases involving Alzheimer's Dementia (AD), a progressive and currently incurable disease. There is considerable research on AD with thousands of relevant publications being added to the PubMed online database every year. The knowledge incorporated in this large body of work is spread across hundreds of thousands of pages of text, making it difficult to distill and mobilize that knowledge in terms of treatments and guidelines. Text mining technology may assist in distilling knowledge from the vast corpus of research literature on Alzheimer's dementia. In this paper, we apply the Named Entity Recognition (NER) system, a text mining (TM) method used to group words into classes, in order to extract useful information from free texts. We present findings concerning how well NER can extract information from a corpus of AD research publications.