{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T16:43:33Z","timestamp":1791909813507,"version":"4.3.4"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,10,10]],"date-time":"2019-10-10T00:00:00Z","timestamp":1570665600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2019,10,10]],"date-time":"2019-10-10T00:00:00Z","timestamp":1570665600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Transl Med"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Checking appropriateness of blood transfusion for quality assurance required enormous usage of time and human resources from the healthcare system. We report here a new machine learning algorithm for checking blood transfusion quality.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Materials and methods<\/jats:title>\n                <jats:p>The multilayer perceptron neural network (MLPNN) was designed to learn an expert\u2019s judgement from 4946 clinical cases. The accuracy in predicting the blood transfusion was then reported.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We achieved a 96.8% overall accuracy rate, with a 99% match rate to the experts\u2019 judgement on those appropriate cases and 90.9% on the inappropriate cases.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Machine learning algorithm can accurately match to human judgement by feeding in pre-surgical information and key laboratory variables.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12967-019-2085-y","type":"journal-article","created":{"date-parts":[[2019,10,10]],"date-time":"2019-10-10T10:01:48Z","timestamp":1570701708000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Computer algorithm can match physicians\u2019 decisions about blood transfusions"],"prefix":"10.1186","volume":"17","author":[{"given":"Yuanyuan","family":"Yao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jenny","family":"Cifuentes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"key":"2085_CR1","volume-title":"The 2009 national blood collection and utilization survey report","author":"U.S. Department of Health and Human Services","year":"2011","unstructured":"U.S. Department of Health and Human Services. 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