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

Clinical Informatics (702) Machine Learning (1190) Tools, Programs and Algorithms (207) Artificial Intelligence (916) Big Data (187) New Methods (798) Clinical Information and Decision Making (1115) Secondary Use of Clinical Data for Research and Surveillance (268) Hygiene and Infection Prevention (265)

Published on in Vol 24, No 1 (2022): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28036, first published .
Energy Efficiency of Inference Algorithms for Clinical Laboratory Data Sets: Green Artificial Intelligence Study

Energy Efficiency of Inference Algorithms for Clinical Laboratory Data Sets: Green Artificial Intelligence Study

Energy Efficiency of Inference Algorithms for Clinical Laboratory Data Sets: Green Artificial Intelligence Study

Authors of this article:

Jia-Ruei Yu 1 Author Orcid Image ;   Chun-Hsien Chen 1, 2 Author Orcid Image ;   Tsung-Wei Huang 3 Author Orcid Image ;   Jang-Jih Lu 1 Author Orcid Image ;   Chia-Ru Chung 4 Author Orcid Image ;   Ting-Wei Lin 1 Author Orcid Image ;   Min-Hsien Wu 5 Author Orcid Image ;   Yi-Ju Tseng 6 Author Orcid Image ;   Hsin-Yao Wang 1, 5 Author Orcid Image
Article Authors Cited by (6) Tweetations (8) Metrics

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  2. Hofer I, Kupina M, Laddaran L, Halperin E. Integration of feature vectors from raw laboratory, medication and procedure names improves the precision and recall of models to predict postoperative mortality and acute kidney injury. Scientific Reports 2022;12(1) View
  3. Chen C, Chen K, Huang Z, Huang X, Wang Z, He F, Qin M, Long C, Tang B, Mo X, Liu J, Tang W. Identification of intestinal microbiome associated with lymph-vascular invasion in colorectal cancer patients and predictive label construction. Frontiers in Cellular and Infection Microbiology 2023;13 View
  4. Verdecchia R, Sallou J, Cruz L. A systematic review of Green AI. WIREs Data Mining and Knowledge Discovery 2023;13(4) View
  5. Liu J, Huang X, Chen C, Wang Z, Huang Z, Qin M, He F, Tang B, Long C, Hu H, Pan S, Wu J, Tang W. Identification of colorectal cancer progression-associated intestinal microbiome and predictive signature construction. Journal of Translational Medicine 2023;21(1) View
  6. Yokoyama A, Ferro M, de Paula F, Vieira V, Schulze B. Investigating hardware and software aspects in the energy consumption of machine learning: A green AI‐centric analysis. Concurrency and Computation: Practice and Experience 2023;35(24) View