Towards trustworthy seizure onset detection using workflow notes

• URL: http://arxiv.org/abs/2306.08728v1
• Date: Wed, 14 Jun 2023 20:13:24 GMT
• Title: Towards trustworthy seizure onset detection using workflow notes
• Authors: Khaled Saab, Siyi Tang, Mohamed Taha, Christopher Lee-Messer, Christopher R\'e, Daniel Rubin
• Abstract summary: We propose to leverage annotations that are produced by healthcare personnel in routine clinical. We show that by scaling training data to an unprecedented level of 68,920 EEG hours, seizure onset detection performance significantly improves. We also train a multilabel model that classifies 26 attributes other than seizures, such as spikes, slowing, and movement artifacts.
• Score: 5.536372101225628
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A major barrier to deploying healthcare AI models is their trustworthiness. One form of trustworthiness is a model's robustness across different subgroups: while existing models may exhibit expert-level performance on aggregate metrics, they often rely on non-causal features, leading to errors in hidden subgroups. To take a step closer towards trustworthy seizure onset detection from EEG, we propose to leverage annotations that are produced by healthcare personnel in routine clinical workflows -- which we refer to as workflow notes -- that include multiple event descriptions beyond seizures. Using workflow notes, we first show that by scaling training data to an unprecedented level of 68,920 EEG hours, seizure onset detection performance significantly improves (+12.3 AUROC points) compared to relying on smaller training sets with expensive manual gold-standard labels. Second, we reveal that our binary seizure onset detection model underperforms on clinically relevant subgroups (e.g., up to a margin of 6.5 AUROC points between pediatrics and adults), while having significantly higher false positives on EEG clips showing non-epileptiform abnormalities compared to any EEG clip (+19 FPR points). To improve model robustness to hidden subgroups, we train a multilabel model that classifies 26 attributes other than seizures, such as spikes, slowing, and movement artifacts. We find that our multilabel model significantly improves overall seizure onset detection performance (+5.9 AUROC points) while greatly improving performance among subgroups (up to +8.3 AUROC points), and decreases false positives on non-epileptiform abnormalities by 8 FPR points. Finally, we propose a clinical utility metric based on false positives per 24 EEG hours and find that our multilabel model improves this clinical utility metric by a factor of 2x across different clinical settings.

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