Key result
Lightweight machine learning algorithms were evaluated for forecasting intracranial hypertension episodes to identify models suitable for medical monitoring devices using limited labeled data.
Why the study?
To identify lightweight, noncomputationally dependent machine learning algorithms capable of detecting and forecasting intracranial hypertensive episodes from historical intracranial pressure signals using limited labeled samples.
Lightweight machine learning algorithms may be applicable for detecting and forecasting intracranial hypertension episodes using limited labeled samples on medical monitoring devices.
Answers are grounded in this paper and cite their sources.
May support lightweight ML for device-based intracranial hypertension forecasting; leaves open prospective validation.
The objective of the work presented in this article is to investigate the applicability of lightweight machine learning (ML) algorithms capable of detecting and forecasting hypertensive (HT) episodes from historical intracranial pressure (ICP) signals. Specifically, we aim at identifying noncomputationally dependent algorithms, which can be supported by lightweight hardware such as medical monitoring devices. We also propose applicable algorithms, which can be trained with a limited number of labeled samples due to the unfeasibility of manually labeling large volumes of ICP signals in most instances.
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Jahangir et al. (2022) studied Intracranial hypertension. Lightweight machine learning algorithms vs. Various algorithms was evaluated on Detection and forecasting of hypertensive episodes from historical ICP signals. Lightweight machine learning algorithms were evaluated for forecasting intracranial hypertension episodes to identify models suitable for medical monitoring devices using limited labeled data.
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