Performance of algorithms and clinician on clinical outcomes task.

<p>A receiver operating characteristic (ROC) curve for the CatBoost algorithm at predicting clinical outcome is shown. Also shown are operating points for the clinician (using the murmur label) and the parallel HSMM murmur detection algorithm. The colourmap shows how sensitivity and specificit...

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Bibliographic Details
Main Author:	Andrew McDonald (2307400) (author)
Other Authors:	Mark J. F. Gales (20323754) (author), Anurag Agarwal (516905) (author)
Published:	2024
Subjects:	Cell Biology Molecular Biology Biotechnology Cancer Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified representative clinical datasets recurrent neural network provide interpretable predictions new test data machine learning analysis existing approaches require 2022 george b cued_acoustics &# 8217 moody physionet challenge model also estimates many previous algorithms key first step individual sound components heart sound recordings phonocardiograms </ p algorithm &# 8217 heart disease using detect heart murmurs heart sound &# 8216 strong murmurs markov model many end first prize expected sound challenge tasks heart murmur xlink "> stage algorithm significantly alter segmentation errors s2 sounds resource environments properties make promising tool performance exceeded improve upon hidden semi find s1 algorithm formed
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Performance of algorithms and clinician on clinical outcomes task.

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