Endocrinol Metab.  2020 Mar;35(1):71-84. 10.3803/EnM.2020.35.1.71.

Machine Learning Applications in Endocrinology and Metabolism Research: An Overview

Affiliations
  • 1Division of Endocrinology and Metabolism, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea. nkhong84@yuhs.ac

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.

Keyword

Machine learning; Artificial intelligence; Deep learning; Endocrinology; Metabolism; Diabetes; Osteoporosis; Pituitary; Adrenal; Thyroid

MeSH Terms

Artificial Intelligence
Cooperative Behavior
Data Accuracy
Endocrinology*
Machine Learning*
Metabolism*
Osteoporosis
Thyroid Gland

Figure

  • Fig. 1 The increasing trend in the number of artificial intelligence or machine learning-related publications per year in the endocrinology and metabolism field. The included publications were confined to PubMed-indexed records until the search date (January 17th, 2020), with combinations of search terms including machine learning, artificial intelligence, deep learning, endocrinology, metabolism, diabetes, pituitary, thyroid, adrenal gland, and osteoporosis, using PubMed query as follows: search ((((((“Machine Learning”[Mesh]) OR “Artificial Intelligence”[Mesh]) OR “Deep Learning”[Mesh])) OR (((machine learning[Title/Abstract]) OR artificial intelligence[Title/Abstract]) OR deep learning[Title/Abstract]))) AND ((((((((endocrinology[Title/Abstract]) OR diabetes[Title/Abstract]) OR pituitary[Title/Abstract]) OR thyroid[Title/Abstract]) OR adrenal gland[Title/Abstract]) OR osteoporosis[Title/Abstract])) OR ((((((“Endocrinology”[Mesh]) OR “Diabetes Mellitus”[Mesh]) OR “Pituitary Gland”[Mesh]) OR “Thyroid Gland”[Mesh]) OR “Adrenal Glands”[Mesh]) OR “Osteoporosis”[Mesh])).

  • Fig. 2 A brief workflow of machine learning-based medical research.

  • Fig. 3 Top 30 frequently appeared words in the titles of machine learning (ML)-based endocrinology studies between 2015 and 2019. Among a total of 2028 literatures searched by PubMed querya on Jan 17th, 2020, text analysis was performed with nouns and adjectives parsed from the titles of 611 studies (English language, human study without review or meta-analysis) published within last 5 years. Cumulative counts of appearance of top 30 words were plotted as horizontal bar plot. Frequently appeared diseases and ML tasks were plotted as pie charts separately. aPubMed query: (Search ((((((“Machine Learning”[Mesh]) OR “Artificial Intelligence”[Mesh]) OR “Deep Learning”[Mesh])) OR (((machine learning[Title/Abstract]) OR artificial intelligence[Title/Abstract]) OR deep learning[Title/Abstract]))) AND ((((((((endocrinology[Title/Abstract]) OR diabetes[Title/Abstract]) OR pituitary[Title/Abstract]) OR thyroid[Title/Abstract]) OR adrenal gland[Title/Abstract]) OR osteoporosis[Title/Abstract])) OR ((((((“Endocrinology”[Mesh]) OR “Diabetes Mellitus”[Mesh]) OR “Pituitary Gland”[Mesh]) OR “Thyroid Gland”[Mesh]) OR “Adrenal Glands”[Mesh]) OR “Osteoporosis”[Mesh])).


Cited by  2 articles

Real World Data and Artificial Intelligence in Diabetology
Kwang Joon Kim
J Korean Diabetes. 2020;21(3):140-148.    doi: 10.4093/jkd.2020.21.3.140.

Applications of Machine Learning in Bone and Mineral Research
Sung Hye Kong, Chan Soo Shin
Endocrinol Metab. 2021;36(5):928-937.    doi: 10.3803/EnM.2021.1111.


Reference

1. McCarthy J. From here to human-level AI. Artif Intell. 2007; 171:1174–1182.
Article
2. McCarthy J. What is artificial intelligence? [Internet]. Stanford: Stanford University;2007. cited 2020 Feb 24. Available from: http://www-formal.stanford.edu/jmc/whatisai/.
3. Beaulieu-Jones B, Finlayson SG, Chivers C, Chen I, McDermott M, Kandola J, et al. Trends and focus of machine learning applications for health research. JAMA Netw Open. 2019; 2:e1914051. PMID: 31651969.
Article
4. Artzi NS, Shilo S, Hadar E, Rossman H, Barbash-Hazan S, Ben-Haroush A, et al. Prediction of gestational diabetes based on nationwide electronic health records. Nat Med. 2020; 26:71–76. PMID: 31932807.
Article
5. Oroojeni Mohammad Javad M, Agboola SO, Jethwani K, Zeid A, Kamarthi S. A reinforcement learning-based method for management of type 1 diabetes: exploratory study. JMIR Diabetes. 2019; 4:e12905. PMID: 31464196.
Article
6. Shomorony I, Cirulli ET, Huang L, Napier LA, Heister RR, Hicks M, et al. An unsupervised learning approach to identify novel signatures of health and disease from multimodal data. Genome Med. 2020; 12:7. PMID: 31924279.
Article
7. Ballinger B, Hsieh J, Singh A, Sohoni N, Wang J, Tison GH, et al. DeepHeart: semi-supervised sequence learning for cardiovascular risk prediction [Internet]. arXiv;2018. cited 2020 Feb 24. Available from: https://arxiv.org/abs/1802.02511.
8. Dinga R, Penninx BW, Veltman DJ, Schmaal L, Marquand AF. Beyond accuracy: measures for assessing machine learning models, pitfalls and guidelines. bioRxiv. 2019; 743138. DOI: 10.1101/743138.
Article
9. Handelman GS, Kok HK, Chandra RV, Razavi AH, Huang S, Brooks M, et al. Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods. AJR Am J Roentgenol. 2019; 212:38–43. PMID: 30332290.
Article
10. Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS One. 2015; 10:e0118432. PMID: 25738806.
Article
11. Chicco D, Jurman G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics. 2020; 21:6. PMID: 31898477.
Article
12. Valentinitsch A, Trebeschi S, Kaesmacher J, Lorenz C, Loffler MT, Zimmer C, et al. Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures. Osteoporos Int. 2019; 30:1275–1285. PMID: 30830261.
Article
13. Kong X, Gong S, Su L, Howard N, Kong Y. Automatic detection of acromegaly from facial photographs using machine learning methods. EBioMedicine. 2018; 27:94–102. PMID: 29269039.
Article
14. Buda M, Wildman-Tobriner B, Hoang JK, Thayer D, Tessler FN, Middleton WD, et al. Management of thyroid nodules seen on US images: deep learning may match performance of radiologists. Radiology. 2019; 292:695–701. PMID: 31287391.
Article
15. Perakakis N, Polyzos SA, Yazdani A, Sala-Vila A, Kountouras J, Anastasilakis AD, et al. Non-invasive diagnosis of non-alcoholic steatohepatitis and fibrosis with the use of omics and supervised learning: a proof of concept study. Metabolism. 2019; 101:154005. PMID: 31711876.
Article
16. Kruse C, Eiken P, Vestergaard P. Clinical fracture risk evaluated by hierarchical agglomerative clustering. Osteoporos Int. 2017; 28:819–832. PMID: 27848006.
Article
17. Segar MW, Vaduganathan M, Patel KV, McGuire DK, Butler J, Fonarow GC, et al. Machine learning to predict the risk of incident heart failure hospitalization among patients with diabetes: the WATCH-DM risk score. Diabetes Care. 2019; 42:2298–2306. PMID: 31519694.
Article
18. Su Y, Kwok TC, Cummings SR, Yip BH, Cawthon PM. Can classification and regression tree analysis help identify clinically meaningful risk groups for hip fracture prediction in older American men (the MrOS cohort study)? JBMR Plus. 2019; 3:e10207. PMID: 31687643.
Article
19. Basu S, Raghavan S, Wexler DJ, Berkowitz SA. Characteristics associated with decreased or increased mortality risk from glycemic therapy among patients with type 2 diabetes and high cardiovascular risk: machine learning analysis of the ACCORD trial. Diabetes Care. 2018; 41:604–612. PMID: 29279299.
Article
20. Fan Y, Li Y, Li Y, Feng S, Bao X, Feng M, et al. Development and assessment of machine learning algorithms for predicting remission after transsphenoidal surgery among patients with acromegaly. Endocrine. 2020; 67:412–422. PMID: 31673954.
Article
21. Zaborek NA, Cheng A, Imbus JR, Long KL, Pitt SC, Sippel RS, et al. The optimal dosing scheme for levothyroxine after thyroidectomy: a comprehensive comparison and evaluation. Surgery. 2019; 165:92–98. PMID: 30413325.
Article
22. Liu Y, Wang Y, Ni Y, Cheung CK, Lam KS, Wang Y, et al. Gut microbiome fermentation determines the efficacy of exercise for diabetes prevention. Cell Metab. 2020; 31:77–91. PMID: 31786155.
Article
23. Williams SA, Kivimaki M, Langenberg C, Hingorani AD, Casas JP, Bouchard C, et al. Plasma protein patterns as comprehensive indicators of health. Nat Med. 2019; 25:1851–1857. PMID: 31792462.
Article
24. De Silva K, Jonsson D, Demmer RT. A combined strategy of feature selection and machine learning to identify predictors of prediabetes. J Am Med Inform Assoc. 2020; 27:396–406. PMID: 31889178.
Article
25. Somnay YR, Craven M, McCoy KL, Carty SE, Wang TS, Greenberg CC, et al. Improving diagnostic recognition of primary hyperparathyroidism with machine learning. Surgery. 2017; 161:1113–1121. PMID: 27989606.
Article
26. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016; 316:2402–2410. PMID: 27898976.
Article
27. Eastell R, Brandi ML, Costa AG, D'Amour P, Shoback DM, Thakker RV. Diagnosis of asymptomatic primary hyperparathyroidism: proceedings of the Fourth International Workshop. J Clin Endocrinol Metab. 2014; 99:3570–3579. PMID: 25162666.
Article
28. Akkus Z, Cai J, Boonrod A, Zeinoddini A, Weston AD, Philbrick KA, et al. A survey of deep-learning applications in ultrasound: artificial intelligence-powered ultrasound for improving clinical workflow. J Am Coll Radiol. 2019; 16(9 Pt B):1318–1328. PMID: 31492410.
Article
29. Katsiki N, Gastaldelli A, Mikhailidis DP. Predictive models with the use of omics and supervised machine learning to diagnose non-alcoholic fatty liver disease: a “non-invasive alternative” to liver biopsy? Metabolism. 2019; 101:154010. PMID: 31711877.
Article
30. Kim HS, Kim DJ, Yoon KH. Medical big data is not yet available: why we need realism rather than exaggeration. Endocrinol Metab (Seoul). 2019; 34:349–354. PMID: 31884734.
Article
31. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019; 25:44–56. PMID: 30617339.
Article
Full Text Links
  • ENM
Actions
Cited
CITED
export Copy
Close
Share
  • Twitter
  • Facebook
Similar articles
Copyright © 2024 by Korean Association of Medical Journal Editors. All rights reserved.     E-mail: koreamed@kamje.or.kr