Prediction of the infecting organism in peritoneal dialysis patients with acute peritonitis using interpretable Tsetlin Machines

Authors

Anatoliy Gorbenko (ORCID: https://orcid.org/0000-0001-6757-1797)
Matthias Eberl (ORCID: https://orcid.org/0000-0002-9390-5348)
Nicholas Topley (ORCID: https://orcid.org/0000-0003-2925-4779)
Alex Yakovlev (ORCID: https://orcid.org/0000-0003-0826-9330)
Olga Tarasyuk (ORCID: https://orcid.org/0000-0001-5991-8631)
Rishad Shafik (ORCID: https://orcid.org/0000-0001-5444-537X)
Jingjing Zhang (ORCID: https://orcid.org/0000-0002-8970-7568)

Institutions

University of Wales Institute Cardiff (SG)
Institute of Infection and Immunity (CA)
Cardiff University (GB)
Leeds Beckett University (GB)
Newcastle University (GB)

Publication Details

Journal: Bioinformatics Advances
Published: 2024-12-26
DOI: https://doi.org/10.1093/bioadv/vbaf140
Citations: 5
Primary Topic: Machine Learning in Healthcare
Type: article
Field-Weighted Citation Impact: 1.74

Funders

National Institute for Health and Care Research
Medical Research Council
Engineering and Physical Sciences Research Council

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article

Prediction of the infecting organism in peritoneal dialysis patients with acute peritonitis using interpretable Tsetlin Machines

Anatoliy Gorbenko, Matthias Eberl, Nicholas Topley, Alex Yakovlev et al.

Dec 26, 2024

5 citations

Bioinformatics Advances

Machine Learning in Healthcare

1.74

article

Prediction of the infecting organism in peritoneal dialysis patients with acute peritonitis using interpretable Tsetlin Machines

Anatoliy Gorbenko, Matthias Eberl, Nicholas Topley, Alex Yakovlev, Olga Tarasyuk, Rishad Shafik, Jingjing Zhang

December 26, 2024article en

5 citations

Abstract

No abstract available for this paper.

Bioinformatics AdvancesVol. 5(1)

University of Wales Institute Cardiff (SG), Institute of Infection and Immunity (CA), Cardiff University (GB), Leeds Beckett University (GB), Newcastle University (GB)

National Institute for Health and Care Research, Medical Research Council, Engineering and Physical Sciences Research Council

Peace, Justice and strong institutions

Openalex Percentile: Top 12%

Machine Learning in Healthcare

1.74

DOI Open Access PDF OpenAlex

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Prediction of the infecting organism in peritoneal dialysis patients with acute peritonitis using interpretable Tsetlin Machines — Anatoliy Gorbenko, Matthias Eberl, et al. · Bioinformatics Advances (2024) | TGRS Research Map | TGRS