Comparing a rule-based decision tree to an LSTM network for the detection of ineffective efforts during expiration in neonates
Current Directions in Biomedical Engineering, vol. 11, pp. 370–373
Abstract
Abstract Detecting Ineffective Efforts during Expiration (IEE) in invasively ventilated neonates could help to improve the ventilation process and lower the discomfort of the patient. We propose an intrinsically explainable rule-based decision tree to detect IEE in high-frequency airway flow data. The algorithm is evaluated and compared to a previously developed Long Short-Term Memory (LSTM) neural network. The decision tree achieves a lower yet comparable performance to the LSTM, while being more stable during training and needing considerably less time for fitting and inferring. Further, the decision tree achieved good results on only 20% of the training data, which in future could reduce annotation labor. Overall, the results encourage us not to disregard rule-based algorithms as outdated, especially when explainability is of importance and annotated data is scarce and time-intensive to acquire.
Authors 4
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Affiliation as printed
Embedded Software (Informatik 11), RWTH Aachen, Aachen , Germany
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Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Neonatology Section of the Department of Paediatric and Adolescent Medicine, RWTH Aachen University Hospital, Aachen , Germany
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Universitätsklinikum Aachen · RWTH Aachen University
Affiliation as printed
Neonatology Section of the Department of Paediatric and Adolescent Medicine, RWTH Aachen University Hospital, Aachen , Germany
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Affiliation as printed
Embedded Software (Informatik 11), RWTH Aachen, Aachen , Germany
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