{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Lee KS"],"funding":["Korea Health Technology R&amp;D Project","Ministry of Trade, Industry &amp; Energy of South Korea","Korea Health Industry Development Institute","Korea University College of Medicine","Ministry of Health and Welfare of South Korea, and the Technology Innovation Program","Korea University College of Medicine grant"],"pagination":["2740"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9689865"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12(11)"],"pubmed_abstract":["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: <i>Roseburia</i> undefined, <i>Lachnospira</i> spp., <i>Oscillibacter</i> undefined, <i>Subdoligranulum</i> spp., <i>Streptococcus salivarius</i> subsp. <i>thermophiles</i>, <i>Parabacteroides distasonis</i>, <i>Roseburia</i> spp., <i>Anaerostipes</i> spp., Lachnospiraceae <i>ND3007</i> group undefined, and <i>Ruminiclostridium</i> spp. Explainable artificial intelligence provides an effective, non-invasive decision support system for the early diagnosis of GID."],"journal":["Diagnostics (Basel, Switzerland)"],"pubmed_title":["Explainable Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease."],"pmcid":["PMC9689865"],"funding_grant_id":["HI22C1302","K2209721","NA","20001533","HI21C156001"],"pubmed_authors":["Lee KS","Kim ES"],"additional_accession":[]},"is_claimable":false,"name":"Explainable Artificial Intelligence in the Early Diagnosis of Gastrointestinal Disease.","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: <i>Roseburia</i> undefined, <i>Lachnospira</i> spp., <i>Oscillibacter</i> undefined, <i>Subdoligranulum</i> spp., <i>Streptococcus salivarius</i> subsp. <i>thermophiles</i>, <i>Parabacteroides distasonis</i>, <i>Roseburia</i> spp., <i>Anaerostipes</i> spp., Lachnospiraceae <i>ND3007</i> group undefined, and <i>Ruminiclostridium</i> spp. Explainable artificial intelligence provides an effective, non-invasive decision support system for the early diagnosis of GID.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Nov","modification":"2025-04-18T20:32:07.653Z","creation":"2025-04-07T08:24:42.92Z"},"accession":"S-EPMC9689865","cross_references":{"pubmed":["36359583"],"doi":["10.3390/diagnostics12112740"]}}