Beyond the ROC Curve: Activity Monitoring to Evaluate Deep Learning Models in Clinical Settings

Beyond the ROC Curve: Activity Monitoring to Evaluate Deep Learning Models in Clinical Settings

We evaluated ‘VITALCARE-SEPS’, a deep learning model for sepsis prediction, using the activity monitoring operator characteristics curve with two different scoring algorithms. This evaluation is crucial as the AMOC curve addresses the time-dependent nature of predictions, providing a more nuanced p...

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Bibliographic Details
Main Authors: Hyunwoo CHOO, Kyung Hyun LEE, Sungsoo HONG, Sungjun HONG, Ki-Byung LEE, Chang Youl LEE
Format: Article
Language:English
Published: Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca 2024-11-01
Series:Applied Medical Informatics
Subjects:
Deep learning
Sepsis
Activity monitoring
ROC (receiver operating characteristic) curve
Online Access:https://ami.info.umfcluj.ro/index.php/AMI/article/view/1074
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Summary:We evaluated ‘VITALCARE-SEPS’, a deep learning model for sepsis prediction, using the activity monitoring operator characteristics curve with two different scoring algorithms. This evaluation is crucial as the AMOC curve addresses the time-dependent nature of predictions, providing a more nuanced performance assessment than traditional ROC metrics. Our findings demonstrate that the AMOC curve significantly enhances the evaluation of time-series predictions, enabling more accurate and continuous performance monitoring of machine learning models in clinical settings. This approach can improve model deployment and ultimately lead to better patient outcomes in healthcare.
ISSN:2067-7855

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