RT Journal Article
SR Electronic
T1 Text Mining of Disease-lifestyle Associations to Explain Comorbidities in Electronic Health Registries
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 168211
DO 10.1101/168211
A1 Lars Juhl Jensen
YR 2017
UL http://biorxiv.org/content/early/2017/08/10/168211.abstract
AB Mining of electronic health registries can reveal vast numbers of disease correlations (from hereon referred to as comorbidities for simplicity). However, the underlying causes can be hard to identify, in part because health registries usually do not record important lifestyle factors such as diet, substance consumption, and physical activity. To address this challenge, I developed a text-mining approach that uses dictionaries of diseases and lifestyle factors for named entity recognition and subsequently for co-occurrence extraction of disease–lifestyle associations from Medline. I show that this approach is able to extract many correct associations and provide proof-of-concept that these can provide plausible explanations for comorbidities observed in Swedish and Danish health registry data.