Marc Zimmermann, MSc in Computer Science EPFL
Simplicity is the ultimate sophistication. (Leonardo da Vinci)
- marc.zimmermann@ inf.ethz.ch
- +41 44 632 86 04
Department of Computer Science
Biomedical Informatics Group Universitätsstrasse 6
- CAB F52.2
I'm a software engineer and hence help within the group forging tools and pipelines. Before that, I crunched large quantities of data at Swisscom. I'm holding a master's degree in computer science from EPFL with a specialisation in "Foundations of Software".
Abstract Intensive-care clinicians are presented with large quantities of measurements from multiple monitoring systems. The limited ability of humans to process complex information hinders early recognition of patient deterioration, and high numbers of monitoring alarms lead to alarm fatigue. We used machine learning to develop an early-warning system that integrates measurements from multiple organ systems using a high-resolution database with 240 patient-years of data. It predicts 90% of circulatory-failure events in the test set, with 82% identified more than 2 h in advance, resulting in an area under the receiver operating characteristic curve of 0.94 and an area under the precision-recall curve of 0.63. On average, the system raises 0.05 alarms per patient and hour. The model was externally validated in an independent patient cohort. Our model provides early identification of patients at risk for circulatory failure with a much lower false-alarm rate than conventional threshold-based systems.
Authors Stephanie L. Hyland, Martin Faltys, Matthias Hüser, Xinrui Lyu, Thomas Gumbsch, Cristóbal Esteban, Christian Bock, Max Horn, Michael Moor, Bastian Rieck, Marc Zimmermann, Dean Bodenham, Karsten Borgwardt, Gunnar Rätsch & Tobias M. Merz
Submitted Nature Medicine
Abstract Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and act on early signs of patient deterioration. We used machine learning to develop an early warning system for circulatory failure based on a high-resolution ICU database with 240 patient years of data. This automatic system predicts 90.0% of circulatory failure events (prevalence 3.1%), with 81.8% identified more than two hours in advance, resulting in an area under the receiver operating characteristic curve of 94.0% and area under the precision-recall curve of 63.0%. The model was externally validated in a large independent patient cohort.
Authors Stephanie Hyland, Martin Faltys, Matthias Hüser, Xinrui Lyu, Thomas Gumbsch, Cristóbal Esteban, Christian Bock, Max Horn, Michael Moor, Bastian Rieck, Marc Zimmermann, Dean Bodenham, Karsten Borgwardt, Gunnar Rätsch, Tobias M. Merz
Submitted arXiv Preprints
Abstract The BRCA Challenge is a long-term data-sharing project initiated within the Global Alliance for Genomics and Health (GA4GH) to aggregate BRCA1 and BRCA2 data to support highly collaborative research activities. Its goal is to generate an informed and current understanding of the impact of genetic variation on cancer risk across the iconic cancer predisposition genes, BRCA1 and BRCA2. Initially, reported variants in BRCA1 and BRCA2 available from public databases were integrated into a single, newly created site, www.brcaexchange.org. The purpose of the BRCA Exchange is to provide the community with a reliable and easily accessible record of variants interpreted for a high-penetrance phenotype. More than 20,000 variants have been aggregated, three times the number found in the next-largest public database at the project’s outset, of which approximately 7,250 have expert classifications. The data set is based on shared information from existing clinical databases—Breast Cancer Information Core (BIC), ClinVar, and the Leiden Open Variation Database (LOVD)—as well as population databases, all linked to a single point of access. The BRCA Challenge has brought together the existing international Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) consortium expert panel, along with expert clinicians, diagnosticians, researchers, and database providers, all with a common goal of advancing our understanding of BRCA1 and BRCA2 variation. Ongoing work includes direct contact with national centers with access to BRCA1 and BRCA2 diagnostic data to encourage data sharing, development of methods suitable for extraction of genetic variation at the level of individual laboratory reports, and engagement with participant communities to enable a more comprehensive understanding of the clinical significance of genetic variation in BRCA1 and BRCA2.
Authors Melissa S. Cline , Rachel G. Liao , Michael T. Parsons , Benedict Paten , Faisal Alquaddoomi, Antonis Antoniou, Samantha Baxter, Larry Brody, Robert Cook-Deegan, Amy Coffin, Fergus J. Couch, Brian Craft, Robert Currie, Chloe C. Dlott, Lena Dolman, Johan T. den Dunnen, Stephanie O. M. Dyke, Susan M. Domchek, Douglas Easton, Zachary Fischmann, William D. Foulkes, Judy Garber, David Goldgar, Mary J. Goldman, Peter Goodhand, Steven Harrison, David Haussler, Kazuto Kato, Bartha Knoppers, Charles Markello, Robert Nussbaum, Kenneth Offit, Sharon E. Plon, Jem Rashbass, Heidi L. Rehm, Mark Robson, Wendy S. Rubinstein, Dominique Stoppa-Lyonnet, Sean Tavtigian, Adrian Thorogood, Can Zhang, Marc Zimmermann, BRCA Challenge Authors , John Burn , Stephen Chanock , Gunnar Rätsch , Amanda B. Spurdle
Submitted PLOS Genetics