ArticleBrain : a journal of neurology2022
A recurrent machine learning model predicts intracranial hypertension in neurointensive care patients.
Article in Brain : a journal of neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Using Artificial Intelligence to Predict Intracranial Hypertension in Patients After Traumatic Brain Injury: A Systematic Review.Neurocritical care · 2024Pooled it
- Artificial intelligence in intensive care units: a scoping review addressing the translational gap to clinical practice.Korean journal of anesthesiology · 2026Article
- Should We Do an Individual Management of Septic Shock in Neurocritically Ill Patients? A Proposal of a Conceptual Algorithm.Neurocritical care · 2026Article
- Personalized treatment approaches in neurocritical care.Acute and critical care · 2026Article
- Prognostic value of early intracranial pressure curve following surgical decompression: A machine learning analysis.Brain & spine · 2026Article
- Early mobilization in neurocritical care as a threshold-regulated intervention: a narrative review of mechanistic integration and implications for neurological recovery and physiological stability.Frontiers in neurology · 2026Review
- Idiopathic Intracranial Hypertension: Updates on Pathophysiology and Diagnosis.Advances and technical standards in neurosurgery · 2026Review
- Artificial Intelligence in Anaesthesiology: Current Applications, Challenges, and Future Directions.Turkish journal of anaesthesiology and reanimation · 2025Article
- Decision Support Systems in Neurosurgery: Current Applications and Future Directions.Sensors (Basel, Switzerland) · 2025Review
- From static to dynamic: Artificial intelligence revolution in perioperative care through multimodal data fusion and closed-loop optimization.Journal of anesthesia and translational medicine · 2025Review
- Classification of intracranial pressure epochs using a novel machine learning framework.NPJ digital medicine · 2025Article
- The Role of ICP Monitoring in Minimally Invasive Surgery for the Management of Intracerebral Hemorrhage.Translational stroke research · 2025Review
- Fluid excess on intensive care unit after mechanical thrombectomy after acute ischemic stroke is associated with unfavorable neurological and functional outcomes: An observational cohort study.European stroke journal · 2025Observational
- Machine Learning Approaches to Prognostication in Traumatic Brain Injury.Current neurology and neuroscience reports · 2025Review
- AI and Neurology.Neurological research and practice · 2025Review
- The age of Artificial Intelligence in neurosurgical practice and educational paradigms - Considerations from the EANS Ethico-Legal and Young Neurosurgeons' committees.Brain & spine · 2025Article
- Evolving concepts in intracranial pressure monitoring - from traditional monitoring to precision medicine.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
- Ultrasound-based radiomics and clinical factors-based nomogram for early intracranial hypertension detection in patients with decompressive craniotomy.Frontiers in medical technology · 2025Article
- Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review.Neurology international · 2024Review
- Early prediction of ventricular peritoneal shunt dependency in aneurysmal subarachnoid haemorrhage patients by recurrent neural network-based machine learning using routine intensive care unit data.Journal of clinical monitoring and computing · 2024Article
Corrections and comments
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9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The evolution of intracranial pressure (ICP) of critically ill patients admitted to a neurointensive care unit (ICU) is difficult to predict. Besides the underlying disease and compromised intracranial space, ICP is affected by a multitude of factors, many of which are monitored on the ICU, but the complexity of the resulting patterns limits their clinical use. This paves the way for new machine learning techniques to assist clinical management of patients undergoing invasive ICP monitoring independent of the underlying disease. An institutional cohort (ICP-ICU) of patients with invasive ICP monitoring (n = 1346) was used to train recurrent machine learning models to predict the occurrence of ICP increases of ≥22 mmHg over a long (>2 h) time period in the upcoming hours. External validation was performed on patients undergoing invasive ICP measurement in two publicly available datasets [Medical Information Mart for Intensive Care (MIMIC, n = 998) and eICU Collaborative Research Database (n = 1634)]. Different distances (1-24 h) between prediction time point and upcoming critical phase were evaluated, demonstrating a decrease in performance but still robust AUC-ROC with larger distances (24 h AUC-ROC: ICP-ICU 0.826 ± 0.0071, MIMIC 0.836 ± 0.0063, eICU 0.779 ± 0.0046, 1 h AUC-ROC: ICP-ICU 0.982 ± 0.0008, MIMIC 0.965 ± 0.0010, eICU 0.941 ± 0.0025). The model operates on sparse hourly data and is stable in handling variable input lengths and missingness through its nature of recurrence and internal memory. Calculation of gradient-based feature importance revealed individual underlying decisions for our long short time memory-based model and thereby provided improved clinical interpretability. Recurrent machine learning models have the potential to be an effective tool for the prediction of ICP increases with high translational potential.
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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.