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Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible.

Publication ,  Journal Article
Loftus, TJ; Tighe, PJ; Ozrazgat-Baslanti, T; Davis, JP; Ruppert, MM; Ren, Y; Shickel, B; Kamaleswaran, R; Hogan, WR; Moorman, JR; Upchurch, GR ...
Published in: PLOS digital health
January 2022

Established guidelines describe minimum requirements for reporting algorithms in healthcare; it is equally important to objectify the characteristics of ideal algorithms that confer maximum potential benefits to patients, clinicians, and investigators. We propose a framework for ideal algorithms, including 6 desiderata: explainable (convey the relative importance of features in determining outputs), dynamic (capture temporal changes in physiologic signals and clinical events), precise (use high-resolution, multimodal data and aptly complex architecture), autonomous (learn with minimal supervision and execute without human input), fair (evaluate and mitigate implicit bias and social inequity), and reproducible (validated externally and prospectively and shared with academic communities). We present an ideal algorithms checklist and apply it to highly cited algorithms. Strategies and tools such as the predictive, descriptive, relevant (PDR) framework, the Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence (SPIRIT-AI) extension, sparse regression methods, and minimizing concept drift can help healthcare algorithms achieve these objectives, toward ideal algorithms in healthcare.

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Published In

PLOS digital health

DOI

EISSN

2767-3170

ISSN

2767-3170

Publication Date

January 2022

Volume

1

Issue

1

Start / End Page

e0000006
 

Citation

APA
Chicago
ICMJE
MLA
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Loftus, T. J., Tighe, P. J., Ozrazgat-Baslanti, T., Davis, J. P., Ruppert, M. M., Ren, Y., … Bihorac, A. (2022). Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible. PLOS Digital Health, 1(1), e0000006. https://doi.org/10.1371/journal.pdig.0000006
Loftus, Tyler J., Patrick J. Tighe, Tezcan Ozrazgat-Baslanti, John P. Davis, Matthew M. Ruppert, Yuanfang Ren, Benjamin Shickel, et al. “Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible.PLOS Digital Health 1, no. 1 (January 2022): e0000006. https://doi.org/10.1371/journal.pdig.0000006.
Loftus TJ, Tighe PJ, Ozrazgat-Baslanti T, Davis JP, Ruppert MM, Ren Y, et al. Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible. PLOS digital health. 2022 Jan;1(1):e0000006.
Loftus, Tyler J., et al. “Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible.PLOS Digital Health, vol. 1, no. 1, Jan. 2022, p. e0000006. Epmc, doi:10.1371/journal.pdig.0000006.
Loftus TJ, Tighe PJ, Ozrazgat-Baslanti T, Davis JP, Ruppert MM, Ren Y, Shickel B, Kamaleswaran R, Hogan WR, Moorman JR, Upchurch GR, Rashidi P, Bihorac A. Ideal algorithms in healthcare: Explainable, dynamic, precise, autonomous, fair, and reproducible. PLOS digital health. 2022 Jan;1(1):e0000006.

Published In

PLOS digital health

DOI

EISSN

2767-3170

ISSN

2767-3170

Publication Date

January 2022

Volume

1

Issue

1

Start / End Page

e0000006