Better Warnings About Drug Interactions Thanks To AI

Finally, patients will have a chance to receive better medical advice on supplement-drug or drug-drug or even supplement-supplement interactions!


Credit goes to Andrew Ng who made me aware of this interesting AI startup (supp.ai spun off by the Allen Institute for AI and following, accompanying paper.


[1909.08135] Extracting evidence of supplement-drug interactions from literature: Dietary supplements are used by a large portion of the population, but
information on their safety is hard to find. We demonstrate an automated method
for extracting evidence of supplement-drug interactions (SDIs) and
supplement-supplement interactions (SSIs) from scientific text. To address the
lack of labeled data in this domain, we use labels of the closely related task
of identifying drug-drug interactions (DDIs) for supervision, and assess the
feasibility of transferring the model to identify supplement interactions. We
fine-tune the contextualized word representations of BERT-large using labeled
data from the PDDI corpus. We then process 22M abstracts from PubMed using this
model, and extract evidence for 55946 unique interactions between 1923
supplements and 2727 drugs (precision: 0.77, recall: 0.96), demonstrating that
learning the task of DDI classification transfers successfully to the related
problem of identifying SDIs and SSIs. As far as we know, this is the first
published work on detecting evidence of SDIs/SSIs from literature. We implement
a freely-available public interface supp.ai to browse and search evidence
sentences extracted by our model.