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Machine Learning in Medicine
- Source :
- Circulation, vol 132, iss 20
- Publication Year :
- 2015
- Publisher :
- Ovid Technologies (Wolters Kluwer Health), 2015.
-
Abstract
- Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games − tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome.
- Subjects :
- Clinical Sciences
Cardiorespiratory Medicine and Haematology
Machine learning
computer.software_genre
Article
Machine Learning
Physiology (medical)
computers
Behavioral and Social Science
Health care
risk factors
Humans
Medicine
Relevance (information retrieval)
Clinical care
Physical law
business.industry
Statistical learning
artificial intelligence
Cardiovascular System & Hematology
statistics
Public Health and Health Services
prognosis
Generic health relevance
Artificial intelligence
Cardiology and Cardiovascular Medicine
business
computer
Algorithms
Subjects
Details
- ISSN :
- 15244539 and 00097322
- Volume :
- 132
- Database :
- OpenAIRE
- Journal :
- Circulation
- Accession number :
- edsair.doi.dedup.....9913af5c35a72fb8cb3858f3fd64d879