SynthesisComputational and mathematical methods in medicine2013
Clinical outcome prediction in aneurysmal subarachnoid hemorrhage using Bayesian neural networks with fuzzy logic inferences.
Synthesis in Computational and mathematical methods in medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 41 citations in OpenAlex.
- Analysis of Cerebral Spinal Fluid Drainage and Intracranial Pressure Peaks in Patients with Subarachnoid Hemorrhage.Neurocritical care · 2024Trial
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Multi-class subarachnoid hemorrhage severity prediction: addressing challenges in predicting rare outcomes.Neurosurgical review · 2025Article
- Machine Learning in Neurosurgery: Toward Complex Inputs, Actionable Predictions, and Generalizable Translations.Cureus · 2024Review
- Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan.Brain sciences · 2023Article
- Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model.Frontiers in oncology · 2023Article
- An accurate prognostic prediction for aneurysmal subarachnoid hemorrhage dedicated to patients after endovascular treatment.Therapeutic advances in neurological disorders · 2022Article
- Outcome prediction in aneurysmal subarachnoid hemorrhage: a comparison of machine learning methods and established clinico-radiological scores.Neurosurgical review · 2021Article
- Recognition of moyamoya disease and its hemorrhagic risk using deep learning algorithms: sourced from retrospective studies.Neural regeneration research · 2021Article
- Assessing Contribution of Higher Order Clinical Risk Factors to Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage Patients.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention.BMC medical informatics and decision making · 2017Article
- Robust effects of genetic background on responses to subarachnoid hemorrhage in mice.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2016Article
- Essentials of research methods in neurosurgery and allied sciences for research, appraisal and application of scientific information to patient care (Part I).Neurosciences (Riyadh, Saudi Arabia) · 2016Article
- Clinical outcome prediction in aneurysmal subarachnoid hemorrhage - Alterations in brain-body interface.Surgical neurology international · 2016Article
- Observational
- Delayed neurological deterioration after subarachnoid haemorrhage.Nature reviews. Neurology · 2014Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH).
methodsThe approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients).
resultsBayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs). Similar trends were noted in Bayesian and linear regression ORs. Significant outcome predictors include normal motor response, cerebral infarction, history of myocardial infarction, cerebral edema, history of diabetes mellitus, fever on day 8, prior subarachnoid hemorrhage, admission angiographic vasospasm, neurological grade, intraventricular hemorrhage, ruptured aneurysm size, history of hypertension, vasospasm day, age and mean arterial pressure. Heteroscedasticity was present in the nontransformed dataset. Artificial neural networks found nonlinear relationships with 11 hidden variables in 1 layer, using the multilayer perceptron model. Fuzzy logic decision rules (centroid defuzzification technique) denoted cut-off points for poor prognosis at greater than 2.5 clusters. DISCUSSION: This aSAH prognostic system makes use of existing knowledge, recognizes unknown areas, incorporates one's clinical reasoning, and compensates for uncertainty in prognostication.
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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.