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We generated three ML algorithms (neural network, regression, and kernel-based methods) using seven clinical variable features (N\u2009=\u20099900 ICU events) and subsequently three features (N\u2009=\u200920,198 ICU events) as input into the models. Data from mechanically ventilated ICU patients were obtained from the publicly available Medical Information Mart for Intensive Care (MIMIC III) database and used for analysis. Compared to seven features, three features (SpO\n                    <jats:sub>2<\/jats:sub>\n                    , FiO\n                    <jats:sub>2<\/jats:sub>\n                    and PEEP) were sufficient to impute PaO\n                    <jats:sub>2<\/jats:sub>\n                    from the SpO\n                    <jats:sub>2<\/jats:sub>\n                    . Any of the ML models enabled imputation of PaO\n                    <jats:sub>2<\/jats:sub>\n                    from the SpO\n                    <jats:sub>2<\/jats:sub>\n                    with lower error and showed greater accuracy in predicting PaO\n                    <jats:sub>2<\/jats:sub>\n                    \/FiO\n                    <jats:sub>2<\/jats:sub>\n                    \u2009\u2264\u2009150 compared to the previously published log-linear and non-linear equations. To address potential hidden hypoxemia that occurs more frequently in Black patients, we conducted sensitivity analysis and show ML models outperformed published equations in both Black and White patients. Imputation using data from an independent validation cohort of ICU patients (N\u2009=\u2009133) showed greater accuracy with ML models.\n                  <\/jats:p>","DOI":"10.1038\/s41598-022-12419-7","type":"journal-article","created":{"date-parts":[[2022,5,17]],"date-time":"2022-05-17T13:17:55Z","timestamp":1652793475000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Machine learning based algorithms to impute PaO2 from SpO2 values and development of an online calculator"],"prefix":"10.1038","volume":"12","author":[{"given":"Shuangxia","family":"Ren","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jill A.","family":"Zupetic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammadreza","family":"Tabary","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rebecca","family":"DeSensi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mehdi","family":"Nouraie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinghua","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard D.","family":"Boyce","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Janet S.","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,17]]},"reference":[{"key":"12419_CR1","doi-asserted-by":"publisher","first-page":"2526","DOI":"10.1001\/jama.2012.5669","volume":"307","author":"ADT Force","year":"2012","unstructured":"Force, A. 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Lee discloses a paid consultantship with Janssen Pharmaceuticals, Inc. unrelated to this study. The authors have no other relevant conflicts of interest to disclose.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"8235"}}