Off-Label Drug Use Detection Based on Heterogeneous Network Mining

Off-label drug use refers to prescribing marketed medications for the indications that are not included in their FDA-approved labeling information. Off-label drug use is quite common in clinical practice and inevitable to some extent. Considering the increasing discussions in online health communities (OHCs) among the health consumers, we proposed to harness the large volume of timely information in OHCs to develop an automated method for detecting off-label drug uses from health consumer generated data. From the text corpus, we extracted medical with lexicon-based approaches and measured their interactions with word embedding models, based on which, we constructed a heterogeneous healthcare network. We defined several meta-path-based indicators to describe the drug-disease associations in the heterogeneous network and used them as features to train classifiers to recognize the known drug-disease associations. Lastly, we identified the off-label drug uses from the false-positive results.