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Data-driven automated classification algorithms for acute health conditions: applying PheNorm to COVID-19 disease.

, , , , , , , , , , , , , , , , , and . J. Am. Medical Informatics Assoc., 31 (3): 574-582 (February 2024)

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Data-driven automated classification algorithms for acute health conditions: applying PheNorm to COVID-19 disease., , , , , , , , , and 8 other author(s). J. Am. Medical Informatics Assoc., 31 (3): 574-582 (February 2024)Practical considerations for variable screening in the Super Learner., , and . CoRR, (2023)Super LeArner Prediction of NAb Panels (SLAPNAP): a containerized tool for predicting combination monoclonal broadly neutralizing antibody sensitivity., , , , , and . Bioinform., 37 (22): 4187-4192 (November 2021)Nonparametric variable importance using an augmented neural network with multi-task learning., , , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 1495-1504. PMLR, (2018)Prediction of VRC01 neutralization sensitivity by HIV-1 gp160 sequence features., , , , , , , , , and 10 other author(s). PLoS Comput. Biol., (2019)Improving random forest predictions in small datasets from two-phase sampling designs., , and . BMC Medical Informatics Decis. Mak., 21 (1): 322 (2021)Efficient nonparametric statistical inference on population feature importance using Shapley values., and . ICML, volume 119 of Proceedings of Machine Learning Research, page 10282-10291. PMLR, (2020)rigr: Regression, Inference, and General Data Analysis Tools in R., , , , , , , , and . J. Open Source Softw., 7 (80): 4847 (December 2022)