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Explainable Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease

This study reviews the recent progress of explainable artificial intelligence for the early diagnosis of gastrointestinal disease (GID). The source of data was eight original studies in PubMed. The search terms were “gastrointestinal” (title) together with “random forest” or ”explainable artificial...

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Autores principales: Lee, Kwang-Sig, Kim, Eun Sun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689865/
https://www.ncbi.nlm.nih.gov/pubmed/36359583
http://dx.doi.org/10.3390/diagnostics12112740
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author Lee, Kwang-Sig
Kim, Eun Sun
author_facet Lee, Kwang-Sig
Kim, Eun Sun
author_sort Lee, Kwang-Sig
collection PubMed
description This study reviews the recent progress of explainable artificial intelligence for the early diagnosis of gastrointestinal disease (GID). The source of data was eight original studies in PubMed. The search terms were “gastrointestinal” (title) together with “random forest” or ”explainable artificial intelligence” (abstract). The eligibility criteria were the dependent variable of GID or a strongly associated disease, the intervention(s) of artificial intelligence, the outcome(s) of accuracy and/or the area under the receiver operating characteristic curve (AUC), the outcome(s) of variable importance and/or the Shapley additive explanations (SHAP), a publication year of 2020 or later, and the publication language of English. The ranges of performance measures were reported to be 0.70–0.98 for accuracy, 0.04–0.25 for sensitivity, and 0.54–0.94 for the AUC. The following factors were discovered to be top-10 predictors of gastrointestinal bleeding in the intensive care unit: mean arterial pressure (max), bicarbonate (min), creatinine (max), PMN, heart rate (mean), Glasgow Coma Scale, age, respiratory rate (mean), prothrombin time (max) and aminotransferase aspartate (max). In a similar vein, the following variables were found to be top-10 predictors for the intake of almond, avocado, broccoli, walnut, whole-grain barley, and/or whole-grain oat: Roseburia undefined, Lachnospira spp., Oscillibacter undefined, Subdoligranulum spp., Streptococcus salivarius subsp. thermophiles, Parabacteroides distasonis, Roseburia spp., Anaerostipes spp., Lachnospiraceae ND3007 group undefined, and Ruminiclostridium spp. Explainable artificial intelligence provides an effective, non-invasive decision support system for the early diagnosis of GID.
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spelling pubmed-96898652022-11-25 Explainable Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease Lee, Kwang-Sig Kim, Eun Sun Diagnostics (Basel) Review This study reviews the recent progress of explainable artificial intelligence for the early diagnosis of gastrointestinal disease (GID). The source of data was eight original studies in PubMed. The search terms were “gastrointestinal” (title) together with “random forest” or ”explainable artificial intelligence” (abstract). The eligibility criteria were the dependent variable of GID or a strongly associated disease, the intervention(s) of artificial intelligence, the outcome(s) of accuracy and/or the area under the receiver operating characteristic curve (AUC), the outcome(s) of variable importance and/or the Shapley additive explanations (SHAP), a publication year of 2020 or later, and the publication language of English. The ranges of performance measures were reported to be 0.70–0.98 for accuracy, 0.04–0.25 for sensitivity, and 0.54–0.94 for the AUC. The following factors were discovered to be top-10 predictors of gastrointestinal bleeding in the intensive care unit: mean arterial pressure (max), bicarbonate (min), creatinine (max), PMN, heart rate (mean), Glasgow Coma Scale, age, respiratory rate (mean), prothrombin time (max) and aminotransferase aspartate (max). In a similar vein, the following variables were found to be top-10 predictors for the intake of almond, avocado, broccoli, walnut, whole-grain barley, and/or whole-grain oat: Roseburia undefined, Lachnospira spp., Oscillibacter undefined, Subdoligranulum spp., Streptococcus salivarius subsp. thermophiles, Parabacteroides distasonis, Roseburia spp., Anaerostipes spp., Lachnospiraceae ND3007 group undefined, and Ruminiclostridium spp. Explainable artificial intelligence provides an effective, non-invasive decision support system for the early diagnosis of GID. MDPI 2022-11-09 /pmc/articles/PMC9689865/ /pubmed/36359583 http://dx.doi.org/10.3390/diagnostics12112740 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Lee, Kwang-Sig
Kim, Eun Sun
Explainable Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease
title Explainable Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease
title_full Explainable Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease
title_fullStr Explainable Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease
title_full_unstemmed Explainable Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease
title_short Explainable Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease
title_sort explainable artificial intelligence in the early diagnosis of gastrointestinal disease
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689865/
https://www.ncbi.nlm.nih.gov/pubmed/36359583
http://dx.doi.org/10.3390/diagnostics12112740
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