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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/18162

Title: Machine Learning Algorithm to Predict Cardiac Output Using Arterial Pressure Waveform Analysis
Authors: Ke, Liao
Elibol, Armagan
Wei, Xiao
Cenyu, Liao
Wei, Wang
Nak-Young, Chong
Keywords: Cardiac Output
feature engineering
machine learning
Arterial Pressure Waveform
Issue Date: 2022-12
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Start page: 1586
End page: 1591
DOI: 10.1109/BIBM55620.2022.9995429
Abstract: Cardiac Output (CO) is a key hemodynamic variable that can be estimated in a minimally invasive way via using Arterial Pressure Waveform Analysis (APWA). Many models use circulation mechanics to build the relationship between arterial pressure and CO. In this study, we attempt to apply machine learning and feature engineering to analyze the Arterial Pressure Waveform (APW) and create regression models to predict the CO. We utilize the traditional APWA model knowledge and the time-domain, frequency-domain, and other characteristics of time series data for feature engineering. We present the benchmarking results for several machine learning models using the MIMICII waveform database. We compare the predicted CO values from our proposed models with the “gold standard” TCO (CO measured by intermittent pulmonary artery thermodilution). Our results show that the Random forest model has the most accurate agreement (MSE: 1.421 L/min, bias: -0.01 L/min, 95% limits of agreement: -2.35 L/min to +2.32 L/min, percentage error: 39.44%). Notably, the XGBoost model demonstrates good tracking ability with TCO (radius bias: 11.79°, 95% radius limits of agreement: ± 28.89°), achieving the clinically acceptable level.
Rights: This is the author's version of the work. Copyright (C)2022 IEEE. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022, pp.1586-1591. DOI:10.1109/BIBM55620.2022.9995429. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
URI: http://hdl.handle.net/10119/18162
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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