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

Clinical Information and Decision Making (1314) Machine Learning (1370) Intensive Care Unit (ICU) (101)

Published on in Vol 22, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20268, first published .
Using Item Response Theory for Explainable Machine Learning in Predicting Mortality in the Intensive Care Unit: Case-Based Approach

Using Item Response Theory for Explainable Machine Learning in Predicting Mortality in the Intensive Care Unit: Case-Based Approach

Using Item Response Theory for Explainable Machine Learning in Predicting Mortality in the Intensive Care Unit: Case-Based Approach

Authors of this article:

Adrienne Kline1, 2, 3 Author Orcid Image ;   Theresa Kline4 Author Orcid Image ;   Zahra Shakeri Hossein Abad3, 5 Author Orcid Image ;   Joon Lee3, 5, 6 Author Orcid Image
Article Authors Cited by (6) Tweetations (8) Metrics

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  1. Coban O. A new modification and application of item response theory‐based feature selection for different machine learning tasks. Concurrency and Computation: Practice and Experience 2022;34(26) View
  2. Jentzer J, Kashou A, Murphree D. Clinical applications of artificial intelligence and machine learning in the modern cardiac intensive care unit. Intelligence-Based Medicine 2023;7:100089 View
  3. Brasse J, Broder H, Förster M, Klier M, Sigler I. Explainable artificial intelligence in information systems: A review of the status quo and future research directions. Electronic Markets 2023;33(1) View
  4. Barbosa A, Crispim M, da Silva L, da Silva J, Barbosa A, Morioka S. How can organizations measure the integration of environmental, social, and governance (ESG) criteria? Validation of an instrument using item response theory to capture workers' perception. Business Strategy and the Environment 2024;33(4):3607 View
  5. Xiong D, Marcus M, Maida C, Lyu Y, Hays R, Wang Y, Shen J, Spolsky V, Lee S, Crall J, Liu H, Cilar Budler L. Development of short forms for screening children’s dental caries and urgent treatment needs using item response theory and machine learning methods. PLOS ONE 2024;19(3):e0299947 View