Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records.

ABSTRACT: Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from current knowledge. We constructed a knowledge graph containing four types of node: drugs, protein targets, indications and adverse reactions. Using this graph, we developed a machine learning algorithm based on a simple enrichment test and first demonstrated this method performs extremely well at classifying known causes of adverse reactions (AUC 0.92). A cross validation scheme in which 10% of drug-adverse reaction edges were systematically deleted per fold showed that the method correctly predicts 68% of the deleted edges on average. Next, a subs

SUBMITTER: Bean DM 

PROVIDER: S-EPMC5703951 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature