ArticleNeurocritical care2022
Demystifying the Black Box: The Importance of Interpretability of Predictive Models in Neurocritical Care.
Article in Neurocritical care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed.
- Machine Learning and Explainable AI for Breast Cancer Patient Prioritization: An Intelligent Decision-Support Framework.Bioengineering (Basel, Switzerland) · 2026Article
- Article
- Tongue muscle strength and resting posture across sagittal skeletal patterns in adolescents: a cross-sectional study using mediation analysis and decision tree.BMC oral health · 2026Article
- Predicting End-Stage Renal Disease and Mortality in Chronic Kidney Disease Using Machine Learning: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- An Introduction to AI for Clinicians: Tutorial.Interactive journal of medical research · 2026Article
- Letter to the Editor concerning "Development of machine learning models for predicting patient-perceived benefit following lumbar disc herniation or spinal stenosis surgery" by Z.A. Toh, et al. (Eur Spine J [2025]; doi:10.1007/s00586-025-09304-y).European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2025Article
- Machine Learning Reveals Demographic Disparities in Palliative Care Timing Among Patients With Traumatic Brain Injury Receiving Neurosurgical Consultation.Neurocritical care · 2025Article
- Prediction of the functional outcome of intensive inpatient rehabilitation after stroke using machine learning methods.Scientific reports · 2025Article
- Multiple imputation integrated to machine learning: predicting post-stroke recovery of ambulation after intensive inpatient rehabilitation.Scientific reports · 2024Article
- Machine Learning Identifies Variation in Timing of Palliative Care Consultations Among Traumatic Brain Injury Patients.Research square · 2024Article
- Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction.medRxiv : the preprint server for health sciences · 2024Article
- From bed to bench and back again: Challenges facing deployment of intracranial pressure data analysis in clinical environments.Brain & spine · 2024Review
- Accelerated and Interpretable Oblique Random Survival Forests.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- International e-Delphi survey to define best practice in the reporting of intracranial pressure monitoring recording data.Brain & spine · 2024Article
- Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU.Scientific reports · 2023Observational
- Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit.BMC neurology · 2023Article
- Comparative effectiveness of explainable machine learning approaches for extrauterine growth restriction classification in preterm infants using longitudinal data.Frontiers in medicine · 2023Article
- Evaluation of nutritional status and clinical depression classification using an explainable machine learning method.Frontiers in nutrition · 2023Article
- Navigating the Ocean of Big Data in Neurocritical Care.Neurocritical care · 2022Article
- Topical losartan for established corneal fibrosis with machine learning-based predictors.Therapeutic advances in ophthalmologyArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neurocritical care patients are a complex patient population, and to aid clinical decision-making, many models and scoring systems have previously been developed. More recently, techniques from the field of machine learning have been applied to neurocritical care patient data to develop models with high levels of predictive accuracy. However, although these recent models appear clinically promising, their interpretability has often not been considered and they tend to be black box models, making it extremely difficult to understand how the model came to its conclusion. Interpretable machine learning methods have the potential to provide the means to overcome some of these issues but are largely unexplored within the neurocritical care domain. This article examines existing models used in neurocritical care from the perspective of interpretability. Further, the use of interpretable machine learning will be explored, in particular the potential benefits and drawbacks that the techniques may have when applied to neurocritical care data. Finding a solution to the lack of model explanation, transparency, and accountability is important because these issues have the potential to contribute to model trust and clinical acceptance, and, increasingly, regulation is stipulating a right to explanation for decisions made by models and algorithms. To ensure that the prospective gains from sophisticated predictive models to neurocritical care provision can be realized, it is imperative that interpretability of these models is fully considered.
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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.