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The prediction accuracy was determined by comparing the predicted TBI status with the computed tomography (CT) scan results since CT scan is the gold standard for diagnosing TBI.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>At a significance level of <jats:inline-formula><jats:alternatives><jats:tex-math>$$p = 0.05$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mi>p<\/mml:mi>\n                      <mml:mo>=<\/mml:mo>\n                      <mml:mn>0.05<\/mml:mn>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>, DNN, RF, XGB, and SVM identified 9,\u00a01,\u00a02,\u00a0 and 4 significant features, respectively. In a comparison of accuracy (Accuracy), the area under the curve (AUC), and the precision-recall area under the curve (PR-AUC), the permutation feature importance test for DNN model was the most powerful framework for identifying significant features and outperformed other methods, i.e., RF, XGB, and SVM, with Accuracy, AUC, and PR-AUC as 0.915, 0.794, and 0.974, respectively.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>These results indicate that the PermFIT-DNN framework robustly identifies significant clinical features associated with TBI status and improves prediction performance. The findings could be used to inform the development of clinical decision tools designed to inform triage decisions.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-023-02155-x","type":"journal-article","created":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T13:04:42Z","timestamp":1680786282000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A deep neural network framework to derive interpretable decision rules for accurate traumatic brain injury identification of infants"],"prefix":"10.1186","volume":"23","author":[{"given":"Baiming","family":"Zou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinlei","family":"Mi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth","family":"Stone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,4,6]]},"reference":[{"issue":"9","key":"2155_CR1","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1111\/acem.12458","volume":"21","author":"J Martin","year":"2014","unstructured":"Martin J, Weaver M, Barnato A, Yabes J, Yealy D, Roberts M. 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