{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T09:44:27Z","timestamp":1778319867445,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,14]],"date-time":"2020-08-14T00:00:00Z","timestamp":1597363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2020R1I1A3056858"],"award-info":[{"award-number":["NRF-2020R1I1A3056858"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Hypotensive events in the initial stage of anesthesia can cause serious complications in the patients after surgery, which could be fatal. In this study, we intended to predict hypotension after tracheal intubation using machine learning and deep learning techniques after intubation one minute in advance. Meta learning models, such as random forest, extreme gradient boosting (Xgboost), and deep learning models, especially the convolutional neural network (CNN) model and the deep neural network (DNN), were trained to predict hypotension occurring between tracheal intubation and incision, using data from four minutes to one minute before tracheal intubation. Vital records and electronic health records (EHR) for 282 of 319 patients who underwent laparoscopic cholecystectomy from October 2018 to July 2019 were collected. Among the 282 patients, 151 developed post-induction hypotension. Our experiments had two scenarios: using raw vital records and feature engineering on vital records. The experiments on raw data showed that CNN had the best accuracy of 72.63%, followed by random forest (70.32%) and Xgboost (64.6%). The experiments on feature engineering showed that random forest combined with feature selection had the best accuracy of 74.89%, while CNN had a lower accuracy of 68.95% than that of the experiment on raw data. Our study is an extension of previous studies to detect hypotension before intubation with a one-minute advance. To improve accuracy, we built a model using state-of-art algorithms. We found that CNN had a good performance, but that random forest had a better performance when combined with feature selection. In addition, we found that the examination period (data period) is also important.<\/jats:p>","DOI":"10.3390\/s20164575","type":"journal-article","created":{"date-parts":[[2020,8,14]],"date-time":"2020-08-14T13:00:18Z","timestamp":1597410018000},"page":"4575","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Comparative Analysis on Machine Learning and Deep Learning to Predict Post-Induction Hypotension"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5485-2776","authenticated-orcid":false,"given":"Jihyun","family":"Lee","sequence":"first","affiliation":[{"name":"SCH Media Labs, Soonchunhyang University, Asan 31538, Korea"}]},{"given":"Jiyoung","family":"Woo","sequence":"additional","affiliation":[{"name":"SCH Media Labs, Soonchunhyang University, Asan 31538, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0732-5313","authenticated-orcid":false,"given":"Ah Reum","family":"Kang","sequence":"additional","affiliation":[{"name":"SCH Media Labs, Soonchunhyang University, Asan 31538, Korea"}]},{"given":"Young-Seob","family":"Jeong","sequence":"additional","affiliation":[{"name":"SCH Media Labs, Soonchunhyang University, Asan 31538, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7786-7679","authenticated-orcid":false,"given":"Woohyun","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology and Pain Medicine, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon 14584, Korea"}]},{"given":"Misoon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology and Pain Medicine, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon 14584, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6267-7365","authenticated-orcid":false,"given":"Sang Hyun","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology and Pain Medicine, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon 14584, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1097\/ALN.0000000000000756","article-title":"Association between Intraoperative Hypotension and Hypertension and 30-day Postoperative Mortality in Noncardiac Surgery","volume":"123","author":"Monk","year":"2015","journal-title":"Anesthesiology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1097\/ALN.0b013e3181c14930","article-title":"Intraoperative Hypotension and 1-Year Mortality after Noncardiac Surgery","volume":"111","author":"Bijker","year":"2009","journal-title":"Anesthesiology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1097\/ALN.0000000000000765","article-title":"Association of Intraoperative Hypotension with Acute Kidney Injury after Elective Noncardiac Surgery","volume":"123","author":"Sun","year":"2015","journal-title":"Anesthesiology"},{"key":"ref_4","first-page":"507","article-title":"Relationship between intraoperative mean arterial pressure and clinical outcomes after noncardiac SurgeryToward an empirical definition of hypotension","volume":"119","author":"Walsh","year":"2013","journal-title":"Anesthesiol. 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