{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T12:06:27Z","timestamp":1793275587805,"version":"4.5.0"},"reference-count":45,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2021,8,30]],"date-time":"2021-08-30T00:00:00Z","timestamp":1630281600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Vascular"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The objective of this study was to create an algorithm that could predict diabetic foot ulcer (DFU) incidence in the in-patient population.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>The Nationwide Inpatient Sample datasets were examined from 2008 to 2014. The International Classification of Diseases 9th Edition Clinical Modification (ICD-9-CM) and the Agency for Healthcare Research and Quality comorbidity codes were used to assist in the data collection. Chi-square testing was conducted, using variables that positively correlated with DFUs. For descriptive statistics, the Student T-test, Wilcoxon rank sum test, and chi-square test were used. There were six predictive variables that were identified. A decision tree model CTREE was utilized to help develop an algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>326,853 patients were noted to have DFU. The major variables that contributed to this diagnosis (both with p &lt; 0.001) were cellulitis (OR 63.87, 95% CI [63.87\u201364.49]) and Charcot joint (OR 25.64, 95% CI [25.09\u201326.20]). The model performance of the six-variable testing data was 79.5% (80.6% sensitivity and 78.3% specificity). The area under the curve (AUC) for the 6-variable model was 0.88.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We developed an algorithm with a 79.8% accuracy that could predict the likelihood of developing a DFU.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/17085381211040984","type":"journal-article","created":{"date-parts":[[2021,8,30]],"date-time":"2021-08-30T23:42:07Z","timestamp":1630366927000},"page":"1115-1123","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":27,"title":["Machine learning prediction of diabetic foot ulcers in the inpatient population"],"prefix":"10.1177","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5619-5818","authenticated-orcid":false,"given":"Stavros","family":"Stefanopoulos","sequence":"first","affiliation":[{"name":"Department of Surgery, 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