Machine learning, medical diagnosis, and biomedical engineering research - commentary
By Kenneth R. Foster, Robert Koprowski, Joseph D. Skufca. BioMedical Engineering OnLine, 2014
324 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Health Information Management, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/a20f94ee49d9f29720cf9555457e1fea9050c0b2
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Citations in the corpus
- Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial (2017, background): “9–11 MLAs for sepsis prediction 17 have primarily been tested retrospectively or investigated non-interventionally.”
- Data Science of the People , for the People , by the People : A Viewpoint on an Emerging Dichotomy (2015, methodology): “On one hand, we have applications such as medical diagnosis [22], loan approval [32] and prison sentencing [9] in which data-driven predictions are used to support people in making consequential decisions that can have a profound effect on people.”
- Engineering safety in machine learning (2016, background): “The type of machine learning applications with potentially harmful consequences and the type with harmless consequences can be further analyzed with respect to epistemic uncertainty.”
- Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View (2016, unknown): “It is not rare to see potentially spurious conclusions drawn from methodologically inadequate studies [7-11], which in turn compromises the credibility of other valid studies and discourages many researchers who could benefit from adopting machine learning techniques.”
- Artificial Intelligence in Healthcare: Review and Prediction Case Studies (2020, unknown): “Researchers have also identified some limitations of ML in biomedical diagnosis and have suggested steps to minimize the effects of these drawbacks [77].”