지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.1
- 수록면
- 71 - 84 (14page)
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초록· 키워드
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Machine learning (ML) applications have received extensive attention in endocrinology research during the last decade. This reviewsummarizes the basic concepts of ML and certain research topics in endocrinology and metabolism where ML principles have beenactively deployed. Relevant studies are discussed to provide an overview of the methodology, main findings, and limitations of ML,with the goal of stimulating insights into future research directions. Clear, testable study hypotheses stem from unmet clinical needs,and the management of data quality (beyond a focus on quantity alone), open collaboration between clinical experts and ML engineers, the development of interpretable high-performance ML models beyond the black-box nature of some algorithms, and a creative environment are the core prerequisites for the foreseeable changes expected to be brought about by ML and artificial intelligence in the field of endocrinology and metabolism, with actual improvements in clinical practice beyond hype. Of note, endocrinologists will continue to play a central role in these developments as domain experts who can properly generate, refine, analyze, andinterpret data with a combination of clinical expertise and scientific rigor.
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참고문헌 (31)
참고문헌 신청
McCarthy J / 2007 / From here to human-level AI / Artif Intell 171:1174~1182
McCarthy J / What is artificial intelligence?
Beaulieu-Jones B / 2019 / Trends and focus of machine learning applications for health research / JAMA Netw Open 2:e1914051
Artzi NS / 2020 / Prediction of gestational diabetes based on nationwide electronic health records / Nat Med 26:71~76
Oroojeni Mohammad Javad M / 2019 / A reinforcement learning-based method for management of type 1 diabetes : exploratory study / JMIR Diabetes 4:e12905
McCarthy J / What is artificial intelligence?
Beaulieu-Jones B / 2019 / Trends and focus of machine learning applications for health research / JAMA Netw Open 2:e1914051
Artzi NS / 2020 / Prediction of gestational diabetes based on nationwide electronic health records / Nat Med 26:71~76
Oroojeni Mohammad Javad M / 2019 / A reinforcement learning-based method for management of type 1 diabetes : exploratory study / JMIR Diabetes 4:e12905
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