This paper is in the following e-collection/theme issue:

Machine Learning (1501) Data Science (278) Epidemiology (161) Ophthalmology (47)

Published on in Vol 8, No 2 (2020): February

Explanatory Model of Dry Eye Disease Using Health and Nutrition Examinations: Machine Learning and Network-Based Factor Analysis From a National Survey

Explanatory Model of Dry Eye Disease Using Health and Nutrition Examinations: Machine Learning and Network-Based Factor Analysis From a National Survey

Explanatory Model of Dry Eye Disease Using Health and Nutrition Examinations: Machine Learning and Network-Based Factor Analysis From a National Survey

Authors of this article:

Sang Min Nam1 Author Orcid Image ;   Thomas A Peterson2 Author Orcid Image ;   Atul J Butte2 Author Orcid Image ;   Kyoung Yul Seo3 Author Orcid Image ;   Hyun Wook Han4 Author Orcid Image
Article Authors Cited by (10) Tweetations (33) Metrics

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  2. Curia F. Features and explainable methods for cytokines analysis of Dry Eye Disease in HIV infected patients. Healthcare Analytics 2021;1:100001 View
  3. Yang H, Che S, Hyon J, Han S. Integration of Artificial Intelligence into the Approach for Diagnosis and Monitoring of Dry Eye Disease. Diagnostics 2022;12(12):3167 View
  4. Kumar Y, Gupta S. Deep Transfer Learning Approaches to Predict Glaucoma, Cataract, Choroidal Neovascularization, Diabetic Macular Edema, DRUSEN and Healthy Eyes: An Experimental Review. Archives of Computational Methods in Engineering 2023;30(1):521 View
  5. Ramessur R, Raja L, Kilduff C, Kang S, Li J, Thomas P, Sim D. Impact and Challenges of Integrating Artificial Intelligence and Telemedicine into Clinical Ophthalmology. Asia-Pacific Journal of Ophthalmology 2021;10(3):317 View
  6. Agarwal S. Role of artificial intelligence in cornea practice. Indian Journal of Ophthalmology 2024;72(Suppl 2):S159 View
  7. Chang K, Wu H, Chiang P, Hsu Y, Weng P, Yu T, Li C, Chen Y, Dai H, Tsai H, Chang Y, Wu Y, Yang Y, Li C, Hsu C, Chen S, Chen Y, Cheng C, Hsieh A, Chiou S. Decoding and reconstructing disease relations between dry eye and depression: a multimodal investigation comprising meta-analysis, genetic pathways and Mendelian randomization. Journal of Advanced Research 2024 View
  8. Wang M, Xing L, Pan Y, Gu F, Fang J, Yu X, Pang C, Chong K, Cheung C, Liao X, Fang X, Yang J, Zhou R, Zhou X, Wang F, Liu W. AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions. Big Data Mining and Analytics 2024;7(2):445 View
  9. Rajan S, Ponnan S. An efficient enhanced stacked auto encoder assisted optimized deep neural network for forecasting Dry Eye Disease. Scientific Reports 2024;14(1) View
  10. Amouei Sheshkal S, Gundersen M, Alexander Riegler M, Aass Utheim Ø, Gunnar Gundersen K, Rootwelt H, Prestø Elgstøen K, Lewi Hammer H. Classifying Dry Eye Disease Patients from Healthy Controls Using Machine Learning and Metabolomics Data. Diagnostics 2024;14(23):2696 View