eDoctor: machine learning and the future of medicine
Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML extol its ability to deal with large, complex and disparate data, often found within medicine and feel that ML is the future for bio...
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| Published in: | Journal of internal medicine Vol. 284; no. 6; pp. 603 - 619 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
England
Blackwell Publishing Ltd
01.12.2018
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| Subjects: | |
| ISSN: | 0954-6820, 1365-2796, 1365-2796 |
| Online Access: | Get full text |
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| Abstract | Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML extol its ability to deal with large, complex and disparate data, often found within medicine and feel that ML is the future for biomedical research, personalized medicine, computer‐aided diagnosis to significantly advance global health care. However, the concepts of ML are unfamiliar to many medical professionals and there is untapped potential in the use of ML as a research tool. In this article, we provide an overview of the theory behind ML, explore the common ML algorithms used in medicine including their pitfalls and discuss the potential future of ML in medicine. |
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| AbstractList | Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML extol its ability to deal with large, complex and disparate data, often found within medicine and feel that ML is the future for biomedical research, personalized medicine, computer-aided diagnosis to significantly advance global health care. However, the concepts of ML are unfamiliar to many medical professionals and there is untapped potential in the use of ML as a research tool. In this article, we provide an overview of the theory behind ML, explore the common ML algorithms used in medicine including their pitfalls and discuss the potential future of ML in medicine. Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML extol its ability to deal with large, complex and disparate data, often found within medicine and feel that ML is the future for biomedical research, personalized medicine, computer-aided diagnosis to significantly advance global health care. However, the concepts of ML are unfamiliar to many medical professionals and there is untapped potential in the use of ML as a research tool. In this article, we provide an overview of the theory behind ML, explore the common ML algorithms used in medicine including their pitfalls and discuss the potential future of ML in medicine.Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML extol its ability to deal with large, complex and disparate data, often found within medicine and feel that ML is the future for biomedical research, personalized medicine, computer-aided diagnosis to significantly advance global health care. However, the concepts of ML are unfamiliar to many medical professionals and there is untapped potential in the use of ML as a research tool. In this article, we provide an overview of the theory behind ML, explore the common ML algorithms used in medicine including their pitfalls and discuss the potential future of ML in medicine. |
| Author | Lee, M. J. Handelman, G. S. Razavi, A. H. Asadi, H. Chandra, R. V. Kok, H. K. |
| Author_xml | – sequence: 1 givenname: G. S. orcidid: 0000-0003-4275-783X surname: Handelman fullname: Handelman, G. S. email: guyhandelman@rcsi.ie organization: Royal Victoria Hospital – sequence: 2 givenname: H. K. surname: Kok fullname: Kok, H. K. organization: Northern Hospital Radiology – sequence: 3 givenname: R. V. surname: Chandra fullname: Chandra, R. V. organization: Monash University – sequence: 4 givenname: A. H. surname: Razavi fullname: Razavi, A. H. organization: BCE Corporate Security – sequence: 5 givenname: M. J. surname: Lee fullname: Lee, M. J. organization: Beaumont Hospital and Royal College of Surgeons in Ireland – sequence: 6 givenname: H. surname: Asadi fullname: Asadi, H. organization: Deakin University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30102808$$D View this record in MEDLINE/PubMed |
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| Copyright | 2018 The Association for the Publication of the Journal of Internal Medicine 2018 The Association for the Publication of the Journal of Internal Medicine. Copyright © 2018 The Association for the Publication of the Journal of Internal Medicine |
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| Keywords | supervised machine learning unsupervised machine learning medicine machine learning artificial intelligence |
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| Title | eDoctor: machine learning and the future of medicine |
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