Szczesniak, Rhonda D.

Control of confounding and reporting of results in causal inference studies. Guidance for authors from editors of respiratory, sleep, and critical care journals

Lederer, D. J., Bell, S. C., Branson, R. D., Chalmers, J. D., Marshall, R., Maslove, D. M., Ost, D. E., Punjabi, N. M., Schatz, M., Smyth, A. R., et al. (2019) · Annals of the American Thoracic Society

Advanced Causal Inference TechniquesStatistical Methods in Epidemiology

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Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery

Wissel, B. D., Greiner, H. M., Glauser, T. A., Holland‐Bouley, K. D., Mangano, F. T., Santel, D., Faist, R., Zhang, N., Pestian, J. P., Szczesniak, R. D., et al. (2020) · Epilepsia

Machine Learning in HealthcareEpilepsy research and treatment

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Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression

Szczesniak, R. D., Su, W., Brokamp, C., Keogh, R. H., Pestian, J. P., Seid, M., Diggle, P. J., & Clancy, J. P. (2020) · Statistics in Medicine

Cystic Fibrosis Research AdvancesStatistical Methods and Bayesian Inference

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Improving detection of rapid cystic fibrosis disease progression–early translation of a predictive algorithm into a point-of-care tool

Szczesniak, R. D., Brokamp, C., Su, W., Mcphail, G. L., Pestian, J., & Clancy, J. P. (2019) · IEEE Journal of Translational Engineering in Health and Medicine

Cystic Fibrosis Research AdvancesArtificial Intelligence in Healthcare

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CellDrift: inferring perturbation responses in temporally sampled single-cell data

Jin, K., Schnell, D., Li, G., Salomonis, N., Prasath, V. B. S., Szczesniak, R., & Aronow, B. J. (2022) · Briefings in Bioinformatics

Single-cell and spatial transcriptomicsGene expression and cancer classification

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