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From hype to reality: data science enabling personalized medicine

Overview of attention for article published in BMC Medicine, August 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)

Mentioned by

news
1 news outlet
twitter
34 tweeters
googleplus
1 Google+ user

Citations

dimensions_citation
129 Dimensions

Readers on

mendeley
415 Mendeley
citeulike
1 CiteULike
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Title
From hype to reality: data science enabling personalized medicine
Published in
BMC Medicine, August 2018
DOI 10.1186/s12916-018-1122-7
Pubmed ID
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

Abstract

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. 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.

Twitter Demographics

The data shown below were collected from the profiles of 34 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 415 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 415 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 79 19%
Researcher 67 16%
Student > Master 63 15%
Student > Bachelor 43 10%
Other 22 5%
Other 65 16%
Unknown 76 18%
Readers by discipline Count As %
Computer Science 60 14%
Biochemistry, Genetics and Molecular Biology 52 13%
Medicine and Dentistry 47 11%
Engineering 31 7%
Agricultural and Biological Sciences 28 7%
Other 103 25%
Unknown 94 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 32. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 January 2021.
All research outputs
#816,683
of 18,439,562 outputs
Outputs from BMC Medicine
#622
of 2,788 outputs
Outputs of similar age
#21,509
of 286,956 outputs
Outputs of similar age from BMC Medicine
#1
of 1 outputs
Altmetric has tracked 18,439,562 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,788 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 39.5. This one has done well, scoring higher than 77% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 286,956 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them