Chmura, Piotr J.

A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories

Placido, D., Yuan, B., Hjaltelin, J. X., Zheng, C., Haue, A. D., Chmura, P. J., Yuan, C., Kim, J., Umeton, R., Antell, G., et al. (2023) · Nature Medicine

Machine Learning in HealthcarePancreatic and Hepatic Oncology Research

Cited by 349

Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records

Thorsen-Meyer, H.-C., Nielsen, A. B., Nielsen, A. P., Kaas-Hansen, B. S., Toft, P., Schierbeck, J., Strøm, T., Chmura, P. J., Heimann, M., Dybdahl, L., et al. (2020) · The Lancet Digital Health

Machine Learning in HealthcareSepsis Diagnosis and Treatment

Cited by 336

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models

Allesøe, R. L., Lundgaard, A. T., Hernández Medina, R., Aguayo-Orozco, A., Johansen, J., Nissen, J. N., Brorsson, C., Mazzoni, G., Niu, L., Biel, J. H., et al. (2023) · Nature Biotechnology

Bioinformatics and Genomic NetworksComputational Drug Discovery Methods

Cited by 111

Minimum effective low dose of antithymocyte globulin in people aged 5–25 years with recent-onset stage 3 type 1 diabetes (MELD-ATG): a phase 2, multicentre, double-blind, randomised, placebo-controlled, adaptive dose-ranging trial

Mathieu, C., Wych, J., Hendriks, A. E. J., Van Ryckeghem, L., Tree, T., Chmura, P., Möller, C., Casteels, K., Danne, T., Reschke, F., et al. (2025) · The Lancet

Diabetes and associated disordersDiabetes Management and Research

Cited by 17