From hype to reality: data science enabling personalized medicine

Holger Fröhlich, Rudi Balling, Niko Beerenwinkel, O. Kohlbacher, Santosh Kumar, Thomas Lengauer, Marloes H. Mathuis, 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 und Blaz Zupan
In: BMC Medicine (2018)
 

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.

Bibtex

@ARTICLE{FroehlichEtAl2018a,
    author = {Fr{\"o}hlich, Holger and Balling, Rudi and Beerenwinkel, Niko and Kohlbacher, O. and Kumar, Santosh and
              Lengauer, Thomas and Mathuis, Marloes H. and Moreau, Yves and Murphy, Susan A. and Przytycka, Teresa
              M. and Rebhan, Michael and R{\"o}st, 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},
     title = {From hype to reality: data science enabling personalized medicine},
   journal = {BMC Medicine},
      year = {2018},
  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.},
       url = {https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-018-1122-7},
       doi = {10.1186/s12916-018-1122-7}
}