Explainable multimodal machine learning model for classifying pregnancy drug safety.
Journal:
Bioinformatics (Oxford, England)
Published Date:
Jan 27, 2022
Abstract
MOTIVATION: Teratogenic drugs can cause severe fetal malformation and therefore have critical impact on the health of the fetus, yet the teratogenic risks are unknown for most approved drugs. This article proposes an explainable machine learning model for classifying pregnancy drug safety based on multimodal data and suggests an orthogonal ensemble for modeling multimodal data. To train the proposed model, we created a set of labeled drugs by processing over 100 000 textual responses collected by a large teratology information service. Structured textual information is incorporated into the model by applying clustering analysis to textual features.