ArticlePhysiological measurement2019
An active learning framework for enhancing identification of non-artifactual intracranial pressure waveforms.
Article in Physiological measurement, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 23 citations in OpenAlex.
- Data for Machine Learning Models: Clinical Experience Determines Interrater Reliability in Waveform Anomaly Detection.Neurocritical care · 2026Article
- Intracranial compliance monitoring using pulse shape index in traumatic brain injury: relation to cerebral physiology and clinical outcome.Critical care (London, England) · 2026Article
- Deriving Automated Device Metadata From Intracranial Pressure Waveforms: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury ICU Physiology Cohort Analysis.Critical care explorations · 2024Article
- From bed to bench and back again: Challenges facing deployment of intracranial pressure data analysis in clinical environments.Brain & spine · 2024Review
- Automatic identification of intracranial pressure waveform during external ventricular drainage clamping: segmentation via wavelet analysis.Physiological measurement · 2023Article
- Dynamic Intracranial Pressure Waveform Morphology Predicts Ventriculitis.Neurocritical care · 2022Article
- Peak appearance time in pulse waveforms of intracranial pressure and cerebral blood flow velocity.Frontiers in physiology · 2022Article
- Big data and predictive analytics in neurocritical care.Current neurology and neuroscience reports · 2022Review
- Predictive modeling in neurocritical care using causal artificial intelligence.World journal of critical care medicine · 2021Review
- Patient-adaptable intracranial pressure morphology analysis using a probabilistic model-based approach.Physiological measurement · 2020Article
- IRIS: A Modular Platform for Continuous Monitoring and Caretaker Notification in the Intensive Care Unit.IEEE journal of biomedical and health informatics · 2020Article
- Neurocritical Care: Bench to Bedside (Eds. Claude Hemphill, Michael James) Integrating and Using Big Data in Neurocritical Care.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2020Review
- Intracranial Pressure Monitoring Signals After Traumatic Brain Injury: A Narrative Overview and Conceptual Data Science Framework.Frontiers in neurology · 2020Review
Corrections and comments
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Authors and funding
14 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveIntracranial pressure (ICP) is an important and established clinical measurement that is used in the management of severe acute brain injury. ICP waveforms are usually triphasic and are susceptible to artifact because of transient catheter malfunction or routine patient care. Existing methods for artifact detection include threshold-based, stability-based, or template matching, and result in higher false positives (when there is variability in the ICP waveforms) or higher false negatives (when the ICP waveforms lack complete triphasic components but are valid). APPROACH: We hypothesized that artifact labeling of ICP waveforms can be optimized by an active learning approach which includes interactive querying of domain experts to identify a manageable number of informative training examples. MAIN
resultsThe resulting active learning based framework identified non-artifactual ICP pulses with a superior AUC of 0.96 + 0.012, compared to existing methods: template matching (AUC: 0.71 + 0.04), ICP stability (AUC: 0.51 + 0.036) and threshold-based (AUC: 0.5 + 0.02). SIGNIFICANCE: The proposed active learning framework will support real-time ICP-derived analytics by improving precision of artifact-labelling.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.