Prediction of outcome after aneurysmal subarachnoid haemorrhage using data from patient admission

Rubbert, Christian; Patil, Kaustubh R.; Beseoglu, Kerim; Mathys, Christian; R, May; Kaschner, Marius; Sigl, Benjamin; Teichert, Nikolas A; Boos, Johannes; Turowski, Bernd; Caspers, Julian

doi:10.1007/s00330-018-5505-0

Cited by 40 publications

(29 citation statements)

References 43 publications

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“…Machine learning is a type of artificial intelligence that learns patterns and rules from the given information. Machine learning has several advantages in detecting the possible interactions among many attributes and hence may be useful in clinical prediction and in the identification of novel prognostic markers (8). Recently, studies have applied machine learning to the severity or outcome prediction model for neurological disorders such as ischemic stroke (9, 10), aneurysmal subarachnoid hemorrhage (8), and traumatic brain injury (11).…”

Section: Introductionmentioning

confidence: 99%

Automatic Machine-Learning-Based Outcome Prediction in Patients With Primary Intracerebral Hemorrhage

et al. 2019

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Background: A predictive model can provide physicians, relatives, and patients the accurate information regarding the severity of disease and its predicted outcome. In this study, we used an automated machine-learning-based approach to construct a prognostic model to predict the functional outcome in patients with primary intracerebral hemorrhage (ICH). Methods: We retrospectively collected data on demographic characteristics, laboratory studies and imaging findings of 333 patients with primary ICH. The functional outcomes at the 1st and 6th months after ICH were defined by the modified Rankin scale. All of the attributes were used for preprocessing and for automatic model selection with Automatic Waikato Environment for Knowledge Analysis. Confusion matrix and areas under the receiver operating characteristic curves (AUC) were used to test the predictive performance. Results: Among the models tested, the random forest provided the best predictive performance for functional outcome. The overall accuracy for predicting the 1st month outcome was 83.1%, with 77.4% sensitivity and 86.9% specificity, and the AUC was 0.899. The overall accuracy for predicting the 6th month outcome was 83.9%, with 72.5% sensitivity and 90.6% specificity, and the AUC was 0.917. Conclusions: Using an automatic machine learning technique to predict functional outcome after ICH is feasible, and the random forest model provides the best predictive performance across all tested models. This prediction model may provide information regarding functional outcome for clinicians that will help provide appropriate medical care for patients and information for their caregivers.

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Section: Introductionmentioning

confidence: 99%

Automatic Machine-Learning-Based Outcome Prediction in Patients With Primary Intracerebral Hemorrhage

et al. 2019

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“…Another interesting application is the use of clinical data and CT perfusion from hospital admissions 48 to predict outcomes of aneurysmal SAH. A random forest model was trained to predict dichotomized mRS (#2 and .2), and the accuracy was 84.4% in the training folds and 70.9% in the validation folds.…”

Section: Ai In the Prediction Of Aneurysm Complicationsmentioning

confidence: 99%

Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives

Shi

Schoepf

et al. 2020

AJNR Am J Neuroradiol

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Intracranial aneurysms with subarachnoid hemorrhage lead to high morbidity and mortality. It is of critical importance to detect aneurysms, identify risk factors of rupture, and predict treatment response of aneurysms to guide clinical interventions. Artificial intelligence has received worldwide attention for its impressive performance in image-based tasks. Artificial intelligence serves as an adjunct to physicians in a series of clinical settings, which substantially improves diagnostic accuracy while reducing physicians' workload. Computerassisted diagnosis systems of aneurysms based on MRA and CTA using deep learning have been evaluated, and excellent performances have been reported. Artificial intelligence has also been used in automated morphologic calculation, rupture risk stratification, and outcomes prediction with the implementation of machine learning methods, which have exhibited incremental value. This review summarizes current advances of artificial intelligence in the management of aneurysms, including detection and prediction. The challenges and future directions of clinical implementations of artificial intelligence are briefly discussed. ABBREVIATIONS: AI¼ artificial intelligence; AUC ¼ area under the curve; CAD ¼ computer-assisted diagnostics; DL ¼ deep learning; FP ¼ false-positive; ML ¼ machine learning; SVM ¼ support vector machines

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“…predicting outcomes of ruptured aneurysms [33]; predicting ischemia after aneurysmal subarachnoid hemorrhage [34];…”

Section: Application Of Artificial Intelligence In Vascular Neurosurgmentioning

confidence: 99%

Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives

Danilov¹,

Shifrin²,

Kotik³

et al. 2020

Sovrem Tehnol Med

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The current increase in the number of publications on the use of artificial intelligence (AI) technologies in neurosurgery indicates a new trend in clinical neuroscience. The aim of the study was to conduct a systematic literature review to highlight the main directions and trends in the use of AI in neurosurgery. Methods. Using the PubMed search engine, 327 original journal articles published from 1996 to July 2019 and related to the use of AI technologies in neurosurgery, were selected. The typical issues addressed by using AI were identified for each area of neurosurgery. Results. The typical AI applications within each of the five main areas of neurosurgery (neuro-oncology, functional, vascular, spinal neurosurgery, and traumatic brain injury) were defined. Conclusion. The article highlights the main areas and trends in the up-to-date AI research in neurosurgery, which might be helpful in planning new scientific projects.

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Prediction of outcome after aneurysmal subarachnoid haemorrhage using data from patient admission

Cited by 40 publications

References 43 publications

Automatic Machine-Learning-Based Outcome Prediction in Patients With Primary Intracerebral Hemorrhage

Automatic Machine-Learning-Based Outcome Prediction in Patients With Primary Intracerebral Hemorrhage

Artificial Intelligence in the Management of Intracranial Aneurysms: Current Status and Future Perspectives

Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives

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