{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T06:03:05Z","timestamp":1774504985473,"version":"3.50.1"},"reference-count":22,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T00:00:00Z","timestamp":1650326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002560","name":"Soonchunhyang University","doi-asserted-by":"publisher","award":["20180119"],"award-info":[{"award-number":["20180119"]}],"id":[{"id":"10.13039\/501100002560","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2020R1I1A3056858"],"award-info":[{"award-number":["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>Arterial hypotension is associated with incidence of postoperative complications, such as myocardial infarction or acute kidney injury. Little research has been conducted for the real-time prediction of hypotension, even though many studies have been performed to investigate the factors which affect hypotension events. This forecasting problem is quite challenging compared to diagnosis that detects high-risk patients at current. The forecasting problem that specifies when events occur is more challenging than the forecasting problem that does not specify the event time. In this work, we challenge the forecasting problem in 5 min advance. For that, we aim to build a systematic feature engineering method that is applicable regardless of vital sign species, as well as a machine learning model based on these features for real-time predictions 5 min before hypotension. The proposed feature extraction model includes statistical analysis, peak analysis, change analysis, and frequency analysis. After applying feature engineering on invasive blood pressure (IBP), we build a random forest model to differentiate a hypotension event from other normal samples. Our model yields an accuracy of 0.974, a precision of 0.904, and a recall of 0.511 for predicting hypotensive events.<\/jats:p>","DOI":"10.3390\/s22093108","type":"journal-article","created":{"date-parts":[[2022,4,20]],"date-time":"2022-04-20T00:22:43Z","timestamp":1650414163000},"page":"3108","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Intraoperative Hypotension Prediction Model Based on Systematic Feature Engineering and Machine Learning"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9048-9190","authenticated-orcid":false,"given":"Subin","family":"Lee","sequence":"first","affiliation":[{"name":"Bigdata Engineering Department, SCH Media Labs, Soonchunhyang University, Asan 31538, 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"}]},{"given":"Jiyoung","family":"Woo","sequence":"additional","affiliation":[{"name":"Bigdata Engineering Department, SCH Media Labs, Soonchunhyang University, Asan 31538, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1097\/ALN.0000000000001432","article-title":"Relationship between intraoperative hypotension, defined by either reduction from baseline or absolute thresholds, and acute kidney and myocardial injury after noncardiac surgery: A retrospective cohort analysis","volume":"126","author":"Salmasi","year":"2017","journal-title":"Anesthesiology"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Santos, R.J., Bernardino, J., and Henriques, J. (2011, January 27\u201329). The HTP tool: Monitoring, detecting and predicting hypotensive episodes in critical care. Proceedings of the IEEE EUROCON 2011\u2014International Conference on Computer as a Tool, Lisbon, Portugal.","DOI":"10.1109\/EUROCON.2011.5929313"},{"key":"ref_3","first-page":"407","article-title":"Building computational models to predict one-year mortality in ICU patients with acute myocardial infarction and post myocardial infarction syndrome","volume":"2019","author":"Barrett","year":"2019","journal-title":"AMIA Summits Transl. Sci. Proc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1016\/j.jcrc.2014.05.010","article-title":"CHADS2 and CHA2DS2-VASc scores can predict thromboembolic events after supraventricular arrhythmia in the critically ill patients","volume":"29","author":"Champion","year":"2014","journal-title":"J. Crit. Care"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Dervishi, A. (2020). A deep learning backcasting approach to the electrolyte, metabolite, and acid-base parameters that predict risk in ICU patients. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0242878"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-020-01271-2","article-title":"Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU","volume":"20","author":"Kong","year":"2020","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103626","DOI":"10.1016\/j.compbiomed.2020.103626","article-title":"A machine-learning approach to predicting hypotensive events in ICU settings","volume":"118","author":"Moghadam","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_8","unstructured":"Qin, K., Xu, G., and Huang, J. (2022, April 10). Blood Pressure Prediction by Exploiting Informative Features from ICU Patients\u2019 ECG and PPG Signals under a Heterogeneous Ensemble Learning Framework. Available online: https:\/\/www.semanticscholar.org\/paper\/Blood-pressure-prediction-by-exploiting-informative-Qin-Xu\/d591097e8e71ef1258c0bc28318a2a476ae80fd8."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-020-03814-w","article-title":"Utilizing heart rate variability to predict ICU patient outcome in traumatic brain injury","volume":"21","author":"Zhang","year":"2020","journal-title":"BMC Bioinform."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gopalswamy, S., Tighe, P.J., and Rashidi, P. (2017, January 16\u201319). Deep recurrent neural networks for predicting intraoperative and postoperative outcomes and trends. Proceedings of the 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Orlando, FL, USA.","DOI":"10.1109\/BHI.2017.7897280"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kang, A.R., Lee, J., Jung, W., Lee, M., Park, S.Y., Woo, J., and Kim, S.H. (2020). Development of a prediction model for hypotension after induction of anesthesia using machine learning. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0231172"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kim, H., Jeong, Y.-S., Kang, A.R., Jung, W., Chung, Y.H., Koo, B.S., and Kim, S.H. (2020). Prediction of post-intubation tachycardia using machine-learning models. Appl. Sci., 10.","DOI":"10.3390\/app10031151"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lee, J., Woo, J., Kang, A.R., Jeong, Y.-S., Jung, W., Lee, M., and Kim, S.H. (2020). Comparative analysis on machine learning and deep learning to predict post-induction hypotension. Sensors, 20.","DOI":"10.3390\/s20164575"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jeong, Y.-S., Kim, J., Kim, D., Woo, J., Kim, M.G., Choi, H.W., Kang, A.R., and Park, S.Y. (2021). Prediction of postoperative complications for patients of end stage renal disease. Sensors, 21.","DOI":"10.3390\/s21020544"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, H.-L., Lee, H.-C., Jung, C.-W., and Kim, M.-S. (2020, January 26\u201328). A Deep Learning Method for Intraoperative Age-agnostic and Disease-specific Cardiac Output Monitoring from Arterial Blood Pressure. Proceedings of the 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE), Cincinnati, OH, USA.","DOI":"10.1109\/BIBE50027.2020.00112"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1016\/j.bja.2020.12.035","article-title":"Deep learning models for the prediction of intraoperative hypotension","volume":"126","author":"Lee","year":"2021","journal-title":"Br. J. Anaesth."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-019-0978-6","article-title":"Representation learning in intraoperative vital signs for heart failure risk prediction","volume":"19","author":"Chen","year":"2019","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jeong, Y.-S., Kang, A.R., Jung, W., Lee, S.J., Lee, S., Lee, M., Chung, Y.H., Koo, B.S., and Kim, S.H. (2019). Prediction of blood pressure after induction of anesthesia using deep learning: A feasibility study. Appl. Sci., 9.","DOI":"10.3390\/app9235135"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"53731","DOI":"10.1109\/ACCESS.2019.2912273","article-title":"Spectrum analysis of EEG signals using CNN to model patient\u2019s consciousness level based on anesthesiologists\u2019 experience","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102663","DOI":"10.1016\/j.bspc.2021.102663","article-title":"Deep learning via ECG and PPG signals for prediction of depth of anesthesia","volume":"68","author":"Chowdhury","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_21","first-page":"1","article-title":"Vital Recorder\u2014A free research tool for automatic recording of high-resolution time-synchronised physiological data from multiple anaesthesia devices","volume":"8","author":"Lee","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3108\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:56:30Z","timestamp":1760136990000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3108"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,19]]},"references-count":22,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22093108"],"URL":"https:\/\/doi.org\/10.3390\/s22093108","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,19]]}}}