Machine Learning-Based Diagnosis of Epilepsy in Clinical Routine: Lessons Learned from a Retrospective Pilot Study

Thilo Rieg (), Janek Frick and Ricardo Buettner
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Thilo Rieg: Aalen University
Janek Frick: Aalen University
Ricardo Buettner: Aalen University

A chapter in Information Systems and Neuroscience, 2020, pp 276-283 from Springer

Abstract: Abstract In this work-in-progress paper, we present preliminary results of a large pilot study for implementing a novel machine learning approach presented at HICSS 2019 [1] and ICIS 2019 [2] in a German hospital to detect epileptic episodes in EEG data. While the algorithm achieved a balanced accuracy of 75.6% on real clinical data we could gain valuable experience regarding the implementation barriers of machine learning algorithms in practice, which is discussed in this paper. These lessons learned have practical implications for future work.

Keywords: Electroencephalography; Random forests; Spectral analysis; Machine learning (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-60073-0_32

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DOI: 10.1007/978-3-030-60073-0_32

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