Biosemantics Group

Biomedical text mining

Vast amounts of biomedical information are only available in textual form, such as in scientific publications or electronic health records. The Biosemantics group develops and applies natural language processing techniques to extract information from medical texts in different European languages. An important research topic is to investigate the use of textual data from electronic health records to improve prediction models.

Biomedical terminologies

We use biomedical terminologies to recognize biomedical concepts in unstructured text, but also investigate methods to map between concepts in different terminologies. In particular we are interested in mapping between terminologies in the clinical domain and in the preclinical domain, enabling translational drug safety research.

Biosignal analysis

The group also has a long history in the analysis of biosignals, with a focus on electrocardiograms (ECGs). We developed the Modular ECG Analysis System (MEANS) to automatically measure and interpret ECGs. MEANS has been used in numerous research projects, including population-based epidemiological studies in the Netherlands, Germany and the USA, and has been licensed to several biomedical companies.

We are participating in the following projects:

European Health Data and Evidence Network (EHDEN, www.ehden.eu). The EHDEN project aspires to be the trusted observational research ecosystem to enable better health decisions, outcomes and care. Its mission is to provide a new paradigm for the discovery and analysis of health data in Europe, by building a large-scale, federated network of data sources standardized to a common data model

4D PICTURE (www.4dpicture.eu). The central aim of the 4D PICTURE project is to transform health care delivery decision-making processes in oncology by redesigning patients’ care paths and integrating evidence-based decision-support tools. Its mission is that individualized medicine becomes truly person-centered by empowering clinicians and cancer patients to engage in treatment decision-making.

eTRANSAFE (www.etransafe.eu). The eTRANSAFE project is a large collaborative effort of academia, SMEs and pharmaceutical companies aiming at the development of a data platform and new methods and tools to improve translational safety assessment and research.

1. Seinen TM, Kors JA, van Mulligen EM, Fridgeirsson E, Rijnbeek PR. The added value of text from Dutch general practitioner notes in predictive modeling. J Am Med Inform Assoc. 2023. doi: 10.1093/jamia/ocad160.
2. Sanz F, Pognan F, Steger-Hartmann T, et al. eTRANSAFE: data science to empower translational safety assessment. Nat Rev Drug Discov. 2023;22:605. doi: 10.1038/d41573-023-00099-5.
3. Eikelboom WS, Singleton EH, van den Berg E, et al. The reporting of neuropsychiatric symptoms in electronic health records of individuals with Alzheimer's disease: a natural language processing study. Alzheimers Res Ther. 2023;15:94. doi: 10.1186/s13195-023-01240-7.
4. Vlietstra WJ, Vos R, van Mulligen EM, Jenster GW, Kors JA. Identifying genes targeted by disease-associated non-coding SNPs with a protein knowledge graph. PLoS One. 2022;17:e0271395. doi: 10.1371/journal.pone.0271395.
5. Seinen TM, Fridgeirsson EA, Ioannou S, et al. Use of unstructured text in prognostic clinical prediction models: a systematic review. J Am Med Inform Assoc. 2022;29:1292. doi: 10.1093/jamia/ocac058.
6. Visser JJ, de Vries M, Kors JA. Automatic detection of actionable findings and communication mentions in radiology reports using natural language processing. Eur Radiol. 2022;32:3996. doi: 10.1007/s00330-021-08467-8.
7. Vlietstra WJ, Vos R, van den Akker M, van Mulligen EM, Kors JA. Identifying disease trajectories with predicate information from a knowledge graph. J Biomed Semant. 2020;11:9. doi: 10.1186/s13326-020-00228-8.
8. van den Berg ME, Rijnbeek PR, Niemeijer MN, et al. Normal values of corrected heart-rate variability in 10-second electrocardiograms for all ages. Front Physiol. 2018;9:424. doi: 10.3389/fphys.2018.00424.
9. Rijnbeek PR, van den Berg ME, van Herpen G, Ritsema van Eck HJ, Kors JA. Validation of automatic measurement of QT interval variability. PLoS One. 2017;12:e0175087. doi: 10.1371/journal.pone.0175087.
10. Kors JA, Clematide S, Akhondi SA, van Mulligen EM, Rebholz-Schuhmann R. A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC. J Am Med Inform Assoc. 2015;22:948. doi: 10.1093/jamia/ocv037.
11. Afzal Z, Pons E, Kang N, Sturkenboom MC, Schuemie MJ, Kors JA. ContextD: an algorithm to identify contextual properties of medical terms in a Dutch clinical corpus. BMC Bioinformatics. 2014;15:373. doi: 10.1186/s12859-014-0373-3.

Collaboration within Erasmus MC

Department of Public Health (Dr. J. Rietjens, Dr. I. Korfage)

Department of Epidemiology (Dr. M. Kavousi)

Collaboration outside Erasmus MC

Leiden Institute of Advanced Computer Science (Dr. S. Verberne)

Amsterdam University Medical Center (Dept. Cardiology, Dr. P. Postema)

German Cancer Research Center (NAKO Gesundheitsstudie, Dr. H. Greiser)

Euretos (Dr. O. Beckerhof)

We receive funding from the following projects:

4D PICTURE (www.4dpicture.eu)

EHDEN (www.ehden.eu)

NAKO (www.nako.de)

Principal investigator:

Jan Kors, PhD

Associate Professor

j.kors@erasmusmc.nl

Erik van Mulligen, PhD

Assistant Professor

e.vanmulligen@erasmusmc.nl

Tom Seinen, PhD candidate

t.seinen@erasmusmc.nl