From hype to reality: data science enabling personalized medicine

• Main Authors: Holger Fröhlich, Rudi Balling, Niko Beerenwinkel, Oliver Kohlbacher, Santosh Kumar, Thomas Lengauer, Marloes H. Maathuis, Yves Moreau, Susan A. Murphy, Teresa M. Przytycka, Michael Rebhan, Hannes Röst, Andreas Schuppert, Matthias Schwab, Rainer Spang, Daniel Stekhoven, Jimeng Sun, Andreas Weber, Daniel Ziemek, Blaz Zupan
• Format: Article
• Language: English
• Published: BMC 2018-08-01
• Series: BMC Medicine
• Subjects: Personalized medicine; Precision medicine; Stratified medicine; P4 medicine; Machine learning; Artificial intelligence
• Online Access: http://link.springer.com/article/10.1186/s12916-018-1122-7

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.