Intraoperative Hypotension Prediction Model Based on Systematic Feature Engineering and Machine Learning
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/9/3108 |
| _version_ | 1797502978982150144 |
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| author | Subin Lee Misoon Lee Sang-Hyun Kim Jiyoung Woo |
| author_facet | Subin Lee Misoon Lee Sang-Hyun Kim Jiyoung Woo |
| author_sort | Subin Lee |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:43:55Z |
| format | Article |
| id | doaj.art-69f18e0717c040fbbbc07eea0845da4a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:43:55Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-69f18e0717c040fbbbc07eea0845da4a2023-11-23T09:13:35ZengMDPI AGSensors1424-82202022-04-01229310810.3390/s22093108Intraoperative Hypotension Prediction Model Based on Systematic Feature Engineering and Machine LearningSubin Lee0Misoon Lee1Sang-Hyun Kim2Jiyoung Woo3Bigdata Engineering Department, SCH Media Labs, Soonchunhyang University, Asan 31538, KoreaDepartment of Anesthesiology and Pain Medicine, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon 14584, KoreaDepartment of Anesthesiology and Pain Medicine, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon 14584, KoreaBigdata Engineering Department, SCH Media Labs, Soonchunhyang University, Asan 31538, KoreaArterial 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.https://www.mdpi.com/1424-8220/22/9/3108machine learningvital signinvasive blood pressurefeature engineeringhypotensionarterial hypotension |
| spellingShingle | Subin Lee Misoon Lee Sang-Hyun Kim Jiyoung Woo Intraoperative Hypotension Prediction Model Based on Systematic Feature Engineering and Machine Learning Sensors machine learning vital sign invasive blood pressure feature engineering hypotension arterial hypotension |
| title | Intraoperative Hypotension Prediction Model Based on Systematic Feature Engineering and Machine Learning |
| title_full | Intraoperative Hypotension Prediction Model Based on Systematic Feature Engineering and Machine Learning |
| title_fullStr | Intraoperative Hypotension Prediction Model Based on Systematic Feature Engineering and Machine Learning |
| title_full_unstemmed | Intraoperative Hypotension Prediction Model Based on Systematic Feature Engineering and Machine Learning |
| title_short | Intraoperative Hypotension Prediction Model Based on Systematic Feature Engineering and Machine Learning |
| title_sort | intraoperative hypotension prediction model based on systematic feature engineering and machine learning |
| topic | machine learning vital sign invasive blood pressure feature engineering hypotension arterial hypotension |
| url | https://www.mdpi.com/1424-8220/22/9/3108 |
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