6
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Comparative Analysis on Machine Learning and Deep Learning to Predict Post-Induction Hypotension

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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.

          Related collections

          Most cited references35

          • Record: found
          • Abstract: found
          • Article: not found

          Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network

          Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow1. Widely available digital ECG data and the algorithmic paradigm of deep learning2 present an opportunity to substantially improve the accuracy and scalability of automated ECG analysis. However, a comprehensive evaluation of an end-to-end deep learning approach for ECG analysis across a wide variety of diagnostic classes has not been previously reported. Here, we develop a deep neural network (DNN) to classify 12 rhythm classes using 91,232 single-lead ECGs from 53,549 patients who used a single-lead ambulatory ECG monitoring device. When validated against an independent test dataset annotated by a consensus committee of board-certified practicing cardiologists, the DNN achieved an average area under the receiver operating characteristic curve (ROC) of 0.97. The average F1 score, which is the harmonic mean of the positive predictive value and sensitivity, for the DNN (0.837) exceeded that of average cardiologists (0.780). With specificity fixed at the average specificity achieved by cardiologists, the sensitivity of the DNN exceeded the average cardiologist sensitivity for all rhythm classes. These findings demonstrate that an end-to-end deep learning approach can classify a broad range of distinct arrhythmias from single-lead ECGs with high diagnostic performance similar to that of cardiologists. If confirmed in clinical settings, this approach could reduce the rate of misdiagnosed computerized ECG interpretations and improve the efficiency of expert human ECG interpretation by accurately triaging or prioritizing the most urgent conditions.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Relationship between intraoperative mean arterial pressure and clinical outcomes after noncardiac surgery: toward an empirical definition of hypotension.

            Intraoperative hypotension may contribute to postoperative acute kidney injury (AKI) and myocardial injury, but what blood pressures are unsafe is unclear. The authors evaluated the association between the intraoperative mean arterial pressure (MAP) and the risk of AKI and myocardial injury.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Feature selection: evaluation, application, and small sample performance

                Bookmark

                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                14 August 2020
                August 2020
                : 20
                : 16
                : 4575
                Affiliations
                [1 ]SCH Media Labs, Soonchunhyang University, Asan 31538, Korea; jihyun@ 123456sch.ac.kr (J.L.); armk@ 123456arkang.net (A.R.K.); bytecell@ 123456sch.ac.kr (Y.-S.J.)
                [2 ]Department of Anesthesiology and Pain Medicine, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon 14584, Korea; mulungtomato@ 123456naver.com (W.J.); misoonlee@ 123456schmc.ac.kr (M.L.)
                Author notes
                [* ]Correspondence: jywoo@ 123456sch.ac.kr (J.W.); skim@ 123456schmc.ac.kr (S.H.K.)
                Author information
                https://orcid.org/0000-0001-5485-2776
                https://orcid.org/0000-0002-0732-5313
                https://orcid.org/0000-0002-7786-7679
                https://orcid.org/0000-0001-6267-7365
                Article
                sensors-20-04575
                10.3390/s20164575
                7472016
                32824073
                956b06bb-f0c7-4d07-ad6c-f97ee69a6dc3
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 07 July 2020
                : 11 August 2020
                Categories
                Article

                Biomedical engineering
                hypotension prediction,machine learning,deep learning,anesthesia,vital records,biomedical sensor

                Comments

                Comment on this article