Machine Learning-Based Identification of Natural History Studies in Rare Diseases: A Step toward Understanding Disease Development and Outcome.

According to the Food and Drug Administration's definition, natural history studies (NHS) are observational studies that collect data on the course of a disease from onset to resolution or death, in the absence of an intervention. NHS plays a critical role in rare disease research to support clinical development objectives, including estimating disease prevalence, identifying diagnostic biomarkers. In this study, we present a machine learning based approach for systematically identifying NHS from PubMed as a foundational step toward large-scale NHS analysis for drug development in rare diseases. We explored a manually curated NHS corpus to develop machine learning and deep learning predictive models for NHS classification. We evaluated both binary and multiclass classifications and found that models trained specifically for binary classification (distinguishing NHS-relevant from NHS-irrelevant studies) achieved substantially higher performance than those trained to distinguish among four NHS-related categories, namely,