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BMC Med. 2018 Aug 27;16(1):150. doi: 10.1186/s12916-018-1122-7.

From hype to reality: data science enabling personalized medicine.

Author information

1
UCB Biosciences GmbH, Alfred-Nobel-Str. Str. 10, 40789, Monheim, Germany. holger.froehlich@ucb.com.
2
University of Bonn, Bonn-Aachen International Center for IT, Endenicher Allee 19c, 53115, Bonn, Germany. holger.froehlich@ucb.com.
3
University of Luxembourg, 6 avenue du Swing, 4367, Belvaux, Luxembourg.
4
Department of Biosciences and Engineering, ETH Zurich, Mattenstr. 26, 4058, Basel, Switzerland.
5
University of Tübingen, WSI/ZBIT, Sand 14, 72076, Tübingen, Germany.
6
Max Planck Institute for Developmental Biology, Max-Planck-Ring 5, 72076, Tübingen, Germany.
7
Quantitative Biology Center, University of Tübingen, Auf der Morgenstelle 8, 72076, Tübingen, Germany.
8
Institute for Translational Bioinformatics, University Medical Center Tübingen, Sand 14, 72076, Tübingen, Germany.
9
Department of Computer Science, University of Memphis, 2222 Dunn Hall, Memphis, TN, 38152, USA.
10
Max-Planck-Institute for Informatics, 66123, Saarbrücken, Germany.
11
ETH Zurich, Seminar für Statistik, Rämistrasse 101, 8092, Zurich, Switzerland.
12
University of Leuven, ESAT, Kasteelpark Arenberg 10, 3001, Leuven, Belgium.
13
Harvard University, Science Center 400 Suite, Oxford Street, Cambridge, MA, 02138-2901, USA.
14
National Center of Biotechnology Information, National Institute of Health, 8600 Rockville Pike, Bethesda, MD, 20894-6075, USA.
15
Novartis Institutes for Biomedical Research, 4056, Basel, Switzerland.
16
Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, ON, M5S 3E1, Canada.
17
RWTH Aachen, Joint Research Center for Computational Biomedicine, Pauwelsstrasse 19, 52074, Aachen, Germany.
18
Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, Aucherbachstrasse 112, 70376, Stuttgart, Germany.
19
University of Tübingen, Departments of Clinical Pharmacology and of Pharmacy and Biochemistry, Tübingen, Germany.
20
University of Regensburg, Institute of Functional Genomics, Am BioPark 9, 93053, Regensburg, Germany.
21
ETH Zurich, NEXUS Personalized Health Technol., Otto-Stern-Weg 7, 8093, Zurich, Switzerland.
22
Georgia Tech University, 801 Atlantic Drive, Atlanta, GA, 30332-0280, USA.
23
Institute for Computer Science, University of Bonn, Endenicher Allee 19a, 53115, Bonn, Germany.
24
Pfizer, Worldwide Research and Development, Linkstraße 10, 10785, Berlin, Germany.
25
Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, SI-1000, Ljubljana, Slovenia.

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.

KEYWORDS:

Artificial intelligence; Big data; Biomarkers; Machine learning; P4 medicine; Personalized medicine; Precision medicine; Stratified medicine

PMID:
30145981
PMCID:
PMC6109989
DOI:
10.1186/s12916-018-1122-7
[Indexed for MEDLINE]
Free PMC Article

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