103 193

Cited 0 times in

Machine Learning Applications in Endocrinology and Metabolism Research: An Overview

Authors
 Namki Hong  ;  Heajeong Park  ;  Yumie Rhee 
Citation
 Endocrinology and Metabolism (대한내분비학회지), Vol.35(1) : 71-84, 2020-03 
Journal Title
Endocrinology and Metabolism(대한내분비학회지)
ISSN
 2093-596X 
Issue Date
2020-03
Keywords
Adrenal ; Artificial intelligence ; Deep learning ; Diabetes ; Endocrinology ; Machine learning ; Metabolism ; Osteoporosis ; Pituitary ; Thyroid
Abstract
Machine learning (ML) applications have received extensive attention in endocrinology research during the last decade. This review summarizes the basic concepts of ML and certain research topics in endocrinology and metabolism where ML principles have been actively 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, and interpret data with a combination of clinical expertise and scientific rigor.
Files in This Item:
T202002045.pdf Download
DOI
10.3803/EnM.2020.35.1.71
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Rhee, Yumie(이유미) ORCID logo https://orcid.org/0000-0003-4227-5638
Hong, Nam Ki(홍남기) ORCID logo https://orcid.org/0000-0002-8246-1956
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/179054
사서에게 알리기
  feedback

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse

Links