LLMs with a synthesize-execute-debug-instruct loop generate concise, interpretable computable phenotypes for hypertension and apparent treatment-resistant hypertension that approach symbolic-regression accuracy on held-out patients.
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Iterative Learning of Computable Phenotypes for Treatment Resistant Hypertension using Large Language Models
LLMs with a synthesize-execute-debug-instruct loop generate concise, interpretable computable phenotypes for hypertension and apparent treatment-resistant hypertension that approach symbolic-regression accuracy on held-out patients.