Reliably automating ASA classification: a case for artificial intelligence in preoperative assessment

Y. Vogelaar; Sander F. van den Heuvel; Jaap J. van Rijn; Robert jan Stolker; Kim Batselier; Jan-Wiebe Korstanje

Reliably automating ASA classification: a case for artificial intelligence in preoperative assessment

Y. Vogelaar
, Sander F. van den Heuvel
, Jaap J. van Rijn
, Robert jan Stolker
, Kim Batselier
, Jan-Wiebe Korstanje

Anesthesiology

Erasmus University Medical Centre
Delft University of Technology

Research output: Contribution to conference › Abstract › Academic

Abstract

With 99% of American Society of Anesthesiologists Physical Status III and IV patients correctly being detected by the LS SVM algorithm, the automation of screening for high-risk patients appears feasible, albeit with a false positive rate of 67%. Accuracy could likely be improved by fine-tuning the questionnaire and adding other clinical features (e.g. blood pressure or ECG).

Original language	English
Pages	261
Publication status	Published - 4 Jun 2022
Event	ESAIC - Milan, Italy Duration: 4 Jun 2022 → 6 Jun 2022

Conference

Conference	ESAIC
Country/Territory	Italy
City	Milan
Period	4/06/22 → 6/06/22

Bibliographical note

In: European Journal of Anaesthesiology, Volume 39 I-e Supplement 60 I June 2022

Copyright © 2022 by European Society of Anaesthesiology and Intensive Care.

Access to Document

https://journals.lww.com/ejanaesthesiology/Documents/ESAIC_Abstracts_2022.pdf

Cite this

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title = "Reliably automating ASA classification: a case for artificial intelligence in preoperative assessment",

abstract = "With 99\% of American Society of Anesthesiologists Physical Status III and IV patients correctly being detected by the LS SVM algorithm, the automation of screening for high-risk patients appears feasible, albeit with a false positive rate of 67\%. Accuracy could likely be improved by fine-tuning the questionnaire and adding other clinical features (e.g. blood pressure or ECG).",

author = "Y. Vogelaar and \{van den Heuvel\}, \{Sander F.\} and \{van Rijn\}, \{Jaap J.\} and Stolker, \{Robert jan\} and Kim Batselier and Jan-Wiebe Korstanje",

note = "In: European Journal of Anaesthesiology, Volume 39 I-e Supplement 60 I June 2022 Copyright {\textcopyright} 2022 by European Society of Anaesthesiology and Intensive Care.; ESAIC ; Conference date: 04-06-2022 Through 06-06-2022",

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language = "English",

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T1 - Reliably automating ASA classification

T2 - ESAIC

AU - Vogelaar, Y.

AU - van den Heuvel, Sander F.

AU - van Rijn, Jaap J.

AU - Stolker, Robert jan

AU - Batselier, Kim

AU - Korstanje, Jan-Wiebe

PY - 2022/6/4

Y1 - 2022/6/4

N2 - With 99% of American Society of Anesthesiologists Physical Status III and IV patients correctly being detected by the LS SVM algorithm, the automation of screening for high-risk patients appears feasible, albeit with a false positive rate of 67%. Accuracy could likely be improved by fine-tuning the questionnaire and adding other clinical features (e.g. blood pressure or ECG).

AB - With 99% of American Society of Anesthesiologists Physical Status III and IV patients correctly being detected by the LS SVM algorithm, the automation of screening for high-risk patients appears feasible, albeit with a false positive rate of 67%. Accuracy could likely be improved by fine-tuning the questionnaire and adding other clinical features (e.g. blood pressure or ECG).

M3 - Abstract

SP - 261

Y2 - 4 June 2022 through 6 June 2022

ER -

Reliably automating ASA classification: a case for artificial intelligence in preoperative assessment

Abstract

Conference

Bibliographical note

Access to Document

Fingerprint

Cite this