Piotr Jaroslaw Chmura
Research programmer
Brunak Group
Blegdamsvej 3, 2200 København N., 06 Bygning 6, Building: 06-2-21
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Targeted serum proteomics of longitudinal samples from newly diagnosed youth with type 1 diabetes distinguishes markers of disease and C-peptide trajectory
Moulder, R., Välikangas, T., Hirvonen, M. K., Suomi, T., Brorsson, C. A., Lietzén, N., Bruggraber, S. F. A., Overbergh, L., Dunger, D. B., Peakman, M., Chmura, Piotr Jaroslaw, Brunak, Søren, Schulte, A. M., Mathieu, C., Knip, M., Elo, L. L., Lahesmaa, R. & on behalf of the INNODIA consortium, O. B. O. T. I. C., 2023, In: Diabetologia. 66, p. 1983-1996Research output: Contribution to journal › Journal article › Research › peer-review
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Survival prediction in intensive-care units based on aggregation of long-term disease history and acute physiology: a retrospective study of the Danish National Patient Registry and electronic patient records
Nielsen, A. B., Thorsen-Meyer, H. C., Belling, K., Nielsen, A. P., Thomas, Cecilia Engel, Chmura, Piotr Jaroslaw, Lademann, M., Moseley, P. L., Heimann, M., Dybdahl, L., Spangsege, L., Hulsen, P., Perner, Anders & Brunak, Søren, 2019, In: The Lancet Digital Health. 1, 2, p. e78-e89 12 p.Research output: Contribution to journal › Journal article › Research › peer-review
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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., Chowdhury, A., Franz, A., Brais, L., Andrews, E., Marks, D. S., Regev, A., Ayandeh, S., Brophy, M. T., Do, N. V., Kraft, P. & 5 others, , 2023, In: Nature Medicine. 29, p. 1113-1122Research output: Contribution to journal › Journal article › Research › peer-review
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High-throughput sequencing of circulating plasma microRNAs in newly diagnosed type 1 diabetes identifies four different patient clusters
Sebastiani, G., Grieco, G. E., Fignani, D., Chmura, Piotr Jaroslaw, Brorsson, C. A., Bruggraber, S., Pugliese, A., Evans-Molina, C., Knip, M., Peakman, M., Schulte, A. M., Brunak, Søren, Dunger, D. B., Mathieu, C. & Dotta, F., 2020, In: Diabetologia. 63, Suppl. 1, p. S160 1 p.Research output: Contribution to journal › Conference abstract in journal › Research
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Gene expression signature predicts rate of type 1 diabetes progression
Suomi, T., Starskaia, I., Kalim, U. U., Rasool, O., Jaakkola, M. K., Grönroos, T., Välikangas, T., Brorsson, C., Mazzoni, G., Bruggraber, S., Overbergh, L., Dunger, D., Peakman, M., Chmura, P., Brunak, S., Schulte, A. M., Mathieu, C., Knip, M., Lahesmaa, R., Elo, L. L. & 4 others, , 2023, In: EBioMedicine. 92, 18 p., 104625.Research output: Contribution to journal › Journal article › Research › peer-review
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Discrete-time survival analysis in the critically ill: a deep learning approach using heterogeneous data
Thorsen-Meyer, H., Placido, Davide, Kaas-Hansen, Benjamin Skov, Nielsen, A. P., Lange, Theis, Nielsen, A. B., Toft, P., Schierbeck, J., Strøm, T., Chmura, Piotr Jaroslaw, Heimann, M., Belling, K., Perner, Anders & Brunak, Søren, 2022, In: npj Digital Medicine. 5, 10 p., 142.Research output: Contribution to journal › Journal article › Research › peer-review
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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., Nielsen, A. B., Nielsen, A. P., Kaas-Hansen, Benjamin Skov, Toft, P., Schierbeck, J., Strøm, T., Chmura, Piotr Jaroslaw, Heimann, M., Dybdahl, L., Spangsege, L., Hulsen, P., Belling, K., Brunak, Søren & Perner, Anders, 2020, In: The Lancet Digital Health. 2, 4, p. e179–91 13 p.Research output: Contribution to journal › Journal article › Research › peer-review
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Study protocol Minimum effective low dose: Anti-human thymocyte globulin (MELD-ATG): Phase II, dose ranging, efficacy study of antithymocyte globulin (ATG) within 6 weeks of diagnosis of type 1 diabetes
Wilhelm-Benartzi, C. S., Miller, S. E., Bruggraber, S., Picton, D., Wilson, M., Gatley, K., Chhabra, A., Marcovecchio, M. L., Hendriks, A. E. J., Morobé, H., Chmura, P. J., Bond, S., Aschemeier-Fuchs, B., Knip, M., Tree, T., Overbergh, L., Pall, J., Arnaud, O., Haller, M. J., Nitsche, A. & 4 others, , 2021, In: BMJ Open. 11, 12, 24 p., e053669.Research output: Contribution to journal › Journal article › Research › peer-review
ID: 233701104
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Survival prediction in intensive-care units based on aggregation of long-term disease history and acute physiology: a retrospective study of the Danish National Patient Registry and electronic patient records
Research output: Contribution to journal › Journal article › Research › peer-review
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The bio.tools registry of software tools and data resources for the life sciences
Research output: Contribution to journal › Letter › Research › peer-review
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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
Research output: Contribution to journal › Journal article › Research › peer-review
Published