Abstract
The generation and usage of extensive data from medical care aims at answering crucial medical research questions. Buzzwords in this area are learning health system, data-driven medicine and big data. In addition to classical biostatistical methods, machine learning approaches are frequently applied for analysis.In the evaluation of projects from data-driven medicine by research ethics committees, the question arises of how to assess the benefit-risk ratio and the scientific and social value. Which knowledge is required for that purpose? How can research ethics committees prepare for these challenges? Scientific approaches from the area of observational studies and the consideration of agreed-upon ethical aspects (consent, validity, justice, benefit-risk ratio and transparency) can help to answer the above-mentioned questions. One has to bear in mind that data-driven medicine is no paradigm shift that in principle challenges the established scientific and ethical evaluation procedures. Nevertheless, the evaluation of projects from data-driven medicine requires enhanced specialisation and comprehensive methodical expertise from the areas of machine learning and observational studies.Empirical research of the progression and governance of data-driven medicine will support the development and continual adaptation of effective strategies for evaluation by research ethics committees. Training and networking of experts will enable us to meet the challenges of data-driven medicine.
| Dokumententyp: | Zeitschriftenartikel |
|---|---|
| Fakultät: | Medizin > Institut für Medizinische Informationsverarbeitung, Biometrie und Epidemiologie |
| Themengebiete: | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit |
| ISSN: | 1437-1588 |
| Sprache: | Deutsch |
| Dokumenten ID: | 62317 |
| Datum der Veröffentlichung auf Open Access LMU: | 12. Jul. 2019, 11:00 |
| Letzte Änderungen: | 04. Nov. 2020, 13:40 |
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