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

Clinical Informatics (1042) Machine Learning (1489) Tools, Programs and Algorithms (272) Artificial Intelligence (1408) Big Data (241) New Methods (957) Clinical Information and Decision Making (1424) Secondary Use of Clinical Data for Research and Surveillance (373) Hygiene and Infection Prevention (324)

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 Yu1 Author Orcid Image ;   Chun-Hsien Chen1, 2 Author Orcid Image ;   Tsung-Wei Huang3 Author Orcid Image ;   Jang-Jih Lu1 Author Orcid Image ;   Chia-Ru Chung4 Author Orcid Image ;   Ting-Wei Lin1 Author Orcid Image ;   Min-Hsien Wu5 Author Orcid Image ;   Yi-Ju Tseng6 Author Orcid Image ;   Hsin-Yao Wang1, 5 Author Orcid Image
Article Authors Cited by (12) Tweetations (8) Metrics

Journals

  1. Ashtari A, shabani a, Alizadeh B. A comparative study of machine learning classifiers for secure RF-PUF-based authentication in internet of things. Microprocessors and Microsystems 2022;93:104600 View
  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
  7. Wang H, Lin W, Zhou C, Yang Z, Kalpana S, Lebowitz M. Integrating Artificial Intelligence for Advancing Multiple-Cancer Early Detection via Serum Biomarkers: A Narrative Review. Cancers 2024;16(5):862 View
  8. Raman R, Pattnaik D, Lathabai H, Kumar C, Govindan K, Nedungadi P. Green and sustainable AI research: an integrated thematic and topic modeling analysis. Journal of Big Data 2024;11(1) View
  9. Kuo Z, Chen K, Tseng Y. MoCab: A framework for the deployment of machine learning models across health information systems. Computer Methods and Programs in Biomedicine 2024;255:108336 View
  10. Devis L, Closset M, Degosserie J, Lessire S, Modrie P, Gruson D, Favaloro E, Lippi G, Mullier F, Catry E. Revisiting the Environmental Impact of Inappropriate Clinical Laboratory Testing: A Comprehensive Overview of Sustainability, Economic, and Quality of Care Outcomes. The Journal of Applied Laboratory Medicine 2024 View
  11. Olawumi M, Oladapo B. AI-driven predictive models for sustainability. Journal of Environmental Management 2025;373:123472 View

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  1. Mazurek S, Pytlarz M, Malec S, Crimi A. Computational Science – ICCS 2024. View