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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Machine learning and medicine: book review and commentary
|
|---|---|
| Published in |
BioMedical Engineering OnLine, February 2018
|
| DOI | 10.1186/s12938-018-0449-9 |
| Pubmed ID | |
| Authors |
Robert Koprowski, Kenneth R. Foster |
| Abstract |
This article is a review of the book "Master machine learning algorithms, discover how they work and implement them from scratch" (ISBN: not available, 37 USD, 163 pages) edited by Jason Brownlee published by the Author, edition, v1.10 http://MachineLearningMastery.com . An accompanying commentary discusses some of the issues that are involved with use of machine learning and data mining techniques to develop predictive models for diagnosis or prognosis of disease, and to call attention to additional requirements for developing diagnostic and prognostic algorithms that are generally useful in medicine. Appendix provides examples that illustrate potential problems with machine learning that are not addressed in the reviewed book. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 34 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 15% |
| Student > Master | 4 | 12% |
| Student > Ph. D. Student | 3 | 9% |
| Student > Bachelor | 2 | 6% |
| Lecturer | 2 | 6% |
| Other | 1 | 3% |
| Unknown | 17 | 50% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 9% |
| Engineering | 3 | 9% |
| Nursing and Health Professions | 3 | 9% |
| Biochemistry, Genetics and Molecular Biology | 2 | 6% |
| Medicine and Dentistry | 2 | 6% |
| Other | 1 | 3% |
| Unknown | 20 | 59% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 07 February 2018.
All research outputs
#18,585,544
of 23,020,670 outputs
Outputs from BioMedical Engineering OnLine
#565
of 824 outputs
Outputs of similar age
#329,676
of 440,112 outputs
Outputs of similar age from BioMedical Engineering OnLine
#13
of 19 outputs
Altmetric has tracked 23,020,670 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 824 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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 440,112 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.