Attention-Gated Graph Convolutions for Extracting Drug Interaction Information from Drug Labels
Table 6. Confusion matrix for interaction type
['[EMPTY]', '[EMPTY]', 'Predicted PD', 'Predicted PK', 'Predicted UN']
Lastly, we describe a source of difficulty stemming from incorrectly classifying interaction types. As PD and PK interactions tend to frequently co-occur with effect and trigger mentions respectively, predicted annotations tend to be polarized toward one pair (PD with effect) or the other (PK with trigger). Hence, differentiating between types of interactions for each recognized precipitant is another interesting class of error. Clearly, many errors are due to cases where (1) we mistake unspecified precipitants for PD precipitants and (2) we mistake PK precipitants for unspecified precipitants. We conjecture that making precise implicit connections (not only whether there is evidence in the form of trigger words or phrases, but whether the evidence concerns the particular precipitant) is highly nontrivial. Likely, this aspect may be improved by inclusion of more high quality training data. Confusion between trigger and effect mentions is less concerning; among more than 1000 cases, there are six cases where we mistake effect for trigger and 20 cases where we mistake trigger for effect.