ArticleAnnals of clinical and translational neurology2019
Comparison of machine learning models for seizure prediction in hospitalized patients.
Article in Annals of clinical and translational neurology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Structure and Outcomes of Educational Programs for Training Non-electroencephalographers in Performing and Screening Adult EEG: A Systematic Review.Neurocritical care · 2021Pooled it
- Duration of EEG monitoring needed to ensure a low risk of seizure recurrence in hospitalized patients.Journal of neurology · 2026Article
- Predicting Seizure Risk from Routine Electroencephalographs in Medical Intensive Care Units Using the 2HELPS2B Score.Life (Basel, Switzerland) · 2025Article
- Harvard Electroencephalography Database: A comprehensive clinical electroencephalographic resource from four Boston hospitals.Epilepsia · 2025Article
- Article
- Development and validation of a machine-learning model for the risk of potentially inappropriate medications in elderly stroke patients.Frontiers in pharmacology · 2025Article
- The Neurostimulationist will see you now: prescribing direct electrical stimulation therapies for the human brain in epilepsy and beyond.Frontiers in human neuroscience · 2024Review
- Continuous electroencephalography in the intensive care unit: A critical review and position statement from an Australian and New Zealand perspective.Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine · 2023Article
- Precision Care in Cardiac Arrest: ICECAP (PRECICECAP) Study Protocol and Informatics Approach.Neurocritical care · 2022Article
- BIRDs (Brief Potentially Ictal Rhythmic Discharges) watching during EEG monitoring.Frontiers in neurology · 2022Review
- Predictive modeling in neurocritical care using causal artificial intelligence.World journal of critical care medicine · 2021Review
- Monitoring the Burden of Seizures and Highly Epileptiform Patterns in Critical Care with a Novel Machine Learning Method.Neurocritical care · 2021Article
- Current Status and Future Directions of Neuromonitoring With Emerging Technologies in Neonatal Care.Frontiers in pediatrics · 2021Review
- Prediction of Seizure Recurrence. A Note of Caution.Frontiers in neurology · 2021Article
- The Random Forest Model Has the Best Accuracy Among the Four Pressure Ulcer Prediction Models Using Machine Learning Algorithms.Risk management and healthcare policy · 2021Article
- Validation of the 2HELPS2B Seizure Risk Score in Acute Brain Injury Patients.Neurocritical care · 2020Article
- Detecting Seizures and Epileptiform Abnormalities in Acute Brain Injury.Current neurology and neuroscience reports · 2020Review
- Translational Genomics in Neurocritical Care: a Review.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2020Review
- Machine Learning Applications in the Neuro ICU: A Solution to Big Data Mayhem?Frontiers in neurology · 2020Review
Corrections and comments
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Authors and funding
12 authors at 8 institutions in 2 countries.
Funding
Abstract
objectiveTo compare machine learning methods for predicting inpatient seizures risk and determine the feasibility of 1-h screening EEG to identify low-risk patients (<5% seizures risk in 48 h).
methodsThe Critical Care EEG Monitoring Research Consortium (CCEMRC) multicenter database contains 7716 continuous EEGs (cEEG). Neural networks (NN), elastic net logistic regression (EN), and sparse linear integer model (RiskSLIM) were trained to predict seizures. RiskSLIM was used previously to generate 2HELPS2B model of seizure predictions. Data were divided into training (60% for model fitting) and evaluation (40% for model evaluation) cohorts. Performance was measured using area under the receiver operating curve (AUC), mean risk calibration (CAL), and negative predictive value (NPV). A secondary analysis was performed using Monte Carlo simulation (MCS) to normalize all EEG recordings to 48 h and use only the first hour of EEG as a "screening EEG" to generate predictions.
resultsRiskSLIM recreated the 2HELPS2B model. All models had comparable AUC: evaluation cohort (NN: 0.85, EN: 0.84, 2HELPS2B: 0.83) and MCS (NN: 0.82, EN; 0.82, 2HELPS2B: 0.81) and NPV (absence of seizures in the group that the models predicted to be low risk): evaluation cohort (NN: 97%, EN: 97%, 2HELPS2B: 97%) and MCS (NN: 97%, EN: 99%, 2HELPS2B: 97%). 2HELPS2B model was able to identify the largest proportion of low-risk patients.
interpretationFor seizure risk stratification of hospitalized patients, the RiskSLIM generated 2HELPS2B model compares favorably to the complex NN and EN generated models. 2HELPS2B is able to accurately and quickly identify low-risk patients with only a 1-h screening EEG.
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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.