Froehlich, Holger and Balling, Rudi and Beerenwinkel, Niko and Kohlbacher, Oliver and Kumar, Santosh and Lengauer, Thomas and Maathuis, Marloes H. and Moreau, Yves and Murphy, Susan A. and Przytycka, Teresa M. and Rebhan, Michael and Rost, Hannes and Schuppert, Andreas and Schwab, Matthias and Spang, Rainer and Stekhoven, Daniel and Sun, Jimeng and Weber, Andreas and Ziemek, Daniel and Zupan, Blaz (2018) From hype to reality: data science enabling personalized medicine. BMC MEDICINE, 16: 150. ISSN 1741-7015,
Full text not available from this repository. (Request a copy)Abstract
Background: Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of 'big data' and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. Conclusions: There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | BIG DATA; BREAST-CANCER; HYBRID MODELS; HEALTH; CAUSAL; PREDICTION; SIGNATURE; PATIENT; EVOLUTION; DISCOVERY; Personalized medicine; Precision medicine; Stratified medicine; P4 medicine; Machine learning; Artificial intelligence; Big data; Biomarkers |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 10 Jan 2020 09:19 |
| Last Modified: | 10 Jan 2020 09:19 |
| URI: | https://pred.uni-regensburg.de/id/eprint/14026 |
Actions (login required)
 |
View Item |
