Title |
Machine learning, medical diagnosis, and biomedical engineering research - commentary
|
---|---|
Published in |
BioMedical Engineering OnLine, July 2014
|
DOI | 10.1186/1475-925x-13-94 |
Pubmed ID | |
Authors |
Kenneth R Foster, Robert Koprowski, Joseph D Skufca |
Abstract |
A large number of papers are appearing in the biomedical engineering literature that describe the use of machine learning techniques to develop classifiers for detection or diagnosis of disease. However, the usefulness of this approach in developing clinically validated diagnostic techniques so far has been limited and the methods are prone to overfitting and other problems which may not be immediately apparent to the investigators. This commentary is intended to help sensitize investigators as well as readers and reviewers of papers to some potential pitfalls in the development of classifiers, and suggests steps that researchers can take to help avoid these problems. Building classifiers should be viewed not simply as an add-on statistical analysis, but as part and parcel of the experimental process. Validation of classifiers for diagnostic applications should be considered as part of a much larger process of establishing the clinical validity of the diagnostic technique. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Canada | 1 | 25% |
United Kingdom | 1 | 25% |
Unknown | 2 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | <1% |
Spain | 2 | <1% |
United Kingdom | 2 | <1% |
Portugal | 1 | <1% |
Australia | 1 | <1% |
Taiwan | 1 | <1% |
Germany | 1 | <1% |
China | 1 | <1% |
Switzerland | 1 | <1% |
Other | 2 | <1% |
Unknown | 426 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 74 | 17% |
Student > Master | 70 | 16% |
Researcher | 65 | 15% |
Student > Bachelor | 53 | 12% |
Student > Doctoral Student | 29 | 7% |
Other | 59 | 13% |
Unknown | 91 | 21% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 110 | 25% |
Computer Science | 87 | 20% |
Medicine and Dentistry | 39 | 9% |
Agricultural and Biological Sciences | 24 | 5% |
Biochemistry, Genetics and Molecular Biology | 15 | 3% |
Other | 58 | 13% |
Unknown | 108 | 24% |