Liability scores for chronic obstructive pulmonary disease obtained from our deep learning model improve genetic association discovery and risk prediction. We trained our model using full spirograms and noisy medical record labels obtained from self-reporting and hospital diagnostic codes, and demonstrated that the machine-learning-based phenotyping approach can be generalized to diseases that lack expert-defined annotations.
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This is a summary of: Cosentino, J. et al. Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models. Nat. Genet. https://doi.org/10.1038/s41588-023-01372-4 (2023).
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Deep learning model improves COPD risk prediction and gene discovery. Nat Genet 55, 738–739 (2023). https://doi.org/10.1038/s41588-023-01388-w
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DOI: https://doi.org/10.1038/s41588-023-01388-w
