Early Detection of Hypotension Using a Multivariate Machine Learning Approach.
Introduction: The ability to accurately detect hypotension in trauma patients at the earliest possible time is important in improving trauma outcomes. The earlier an accurate detection can be made, the more time is available to take corrective action. Currently, there is limited research on combinin...
| Publicado en: | Military Medicine Vol. 186; pp. 440 - 445 |
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| Autores principales: | , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
2021 Supplement
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=148591627&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 148591627 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00264075 4DV jtl: Military Medicine issn: 00264075 maglogo: N pubinfo: dt: 2021 Supplement vid: 186 pid: 622 pub: Oxford University Press / USA artinfo: ui: 148591627 148591627 NLM33499451 148591627 10.1093/milmed/usaa323 NLM33499451 148591627 ppf: 440 ppct: 5 formats: fmt: @attributes: type: P tig: atl: Early Detection of Hypotension Using a Multivariate Machine Learning Approach. aug: au: Rashedi, Navid Yifei Sun Vaze, Vikrant Shah, Parikshit Halter, Ryan Elliott, Jonathan T. Paradis, Norman A. Sun, Yifei affil: Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA. sug: subj: Hypotension Diagnosis Models, Theoretical Sheep Animal Studies Comparative Studies Multicenter Studies Evaluation Research Validation Studies ab: Introduction: The ability to accurately detect hypotension in trauma patients at the earliest possible time is important in improving trauma outcomes. The earlier an accurate detection can be made, the more time is available to take corrective action. Currently, there is limited research on combining multiple physiological signals for an early detection of hemorrhagic shock. We studied the viability of early detection of hypotension based on multiple physiologic signals and machine learning methods. We explored proof of concept with a small (5 minutes) prediction window for application of machine learning tools and multiple physiologic signals to detecting hypotension.Materials and Methods: Multivariate physiological signals from a preexisting dataset generated by an experimental hemorrhage model were employed. These experiments were conducted previously by another research group and the data made available publicly through a web portal. This dataset is among the few publicly available which incorporate measurement of multiple physiological signals from large animals during experimental hemorrhage. The data included two hemorrhage studies involving eight sheep. Supervised machine learning experiments were conducted in order to develop deep learning (viz., long short-term memory or LSTM), ensemble learning (viz., random forest), and classical learning (viz., support vector machine or SVM) models for the identification of physiological signals that can detect whether or not overall blood loss exceeds a predefined threshold 5 minutes ahead of time. To evaluate the performance of the machine learning technologies, 3-fold cross-validation was conducted and precision (also called positive predictive value) and recall (also called sensitivity) values were compared. As a first step in this development process, 5 minutes prediction windows were utilized.Results: The results showed that SVM and random forest outperform LSTM neural networks, likely because LSTM tends to overfit the data on small sized datasets. Random forest has the highest recall (84%) with 56% precision while SVM has 62% recall with 82% precision. Upon analyzing the feature importance, it was observed that electrocardiogram has the highest significance while arterial blood pressure has the least importance among all other signals.Conclusion: In this research, we explored the viability of early detection of hypotension based on multiple signals in a preexisting animal hemorrhage dataset. The results show that a multivariate approach might be more effective than univariate approaches for this detection task. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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