Machine Learning for Outcome Prediction of Acute Ischemic Stroke Post Intra-Arterial Therapy
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Summary
It is proposed that a robust machine learning system can potentially optimise the selection process for endovascular versus medical treatment in the management of acute stroke, with potential for incorporation of larger multicenter datasets, likely further improving prediction.
- Type
- article
- Published
- 2014-02-10
- Cited by
- 222
- References
- 70
- Access
- Open access
- OpenAlex
- https://openalex.org/W1968452917
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:17466565
Keywords
Logistic regression, Machine learning, Stroke (engine), Support vector machine, Artificial intelligence
References
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- Intra-Arterial Thrombolytic Therapy for Acute Basilar Occlusion: Pro
- Clinical Deterioration Following Improvement in the NINDS rt-PA Stroke Trial
- Treatment of Acute Ischemic Stroke With Clot Retrieval Devices
- Mechanical thrombectomy for acute ischemic stroke using the MERCI retriever and penumbra aspiration systems.
- A Trial of Imaging Selection and Endovascular Treatment for Ischemic Stroke
- Endovascular Approaches to Acute Stroke, Part 2: A Comprehensive Review of Studies and Trials
- Early Identification of Potentially Salvageable Tissue with MRI-Based Predictive Algorithms after Experimental Ischemic Stroke
- Mechanical Thrombectomy for Acute Ischemic Stroke: Final Results of the Multi MERCI Trial
- Merci mechanical thrombectomy retriever for acute ischemic stroke therapy
- Factors affecting survival rates for acute vertebrobasilar artery occlusions treated with intra-arterial thrombolytic therapy: a meta-analytical approach.
- Does the Merci Retriever work? For.
- Pre-intervention cerebral blood volume predicts outcomes in patients undergoing endovascular therapy for acute ischemic stroke
- Mechanical Revascularization for Acute Ischemic Stroke: A Single-Center, Retrospective Analysis
- Stent-Assisted Mechanical Recanalization for Treatment of Acute Intracerebral Artery Occlusions
Cited by
- Fighting healthcare rocketing costs with value-based medicine: the case of stroke management
- Using Computational Approaches to Improve Risk-Stratified Patient Management: Rationale and Methods
- Population-Based Stroke Atlas for Outcome Prediction: Method and Preliminary Results for Ischemic Stroke from CT
- MLBCD: a machine learning tool for big clinical data
- Intra-arterial therapy for basilar artery thrombosis: the role of machine learning in outcome prediction
- Expansins: roles in plant growth and potential applications in crop improvement
- Predicting Appropriate Admission of Bronchiolitis Patients in the Emergency Department: Rationale and Methods
- Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction
- Changing Management of Acute Ischaemic Stroke: the New Treatments and Emerging Role of Endovascular Therapy
- Is prognostication possible in patients with aneurysmal subarachnoid haemorrhage post endovascular treatment
- Outcomes and Complications After Endovascular Treatment of Brain Arteriovenous Malformations: A Prognostication Attempt Using Artificial Intelligence.
- Evaluating the lexico-grammatical differences in the writing of native and non-native speakers of English in peer-reviewed medical journals in the field of pediatric oncology: Creation of the genuine index scoring system
- Improved fat suppression of the breast using discretized frequency shimming
- Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification
- Use of Machine Learning Classifiers and Sensor Data to Detect Neurological Deficit in Stroke Patients
- Artificial Intelligence and Neurology
- Artificial intelligence in healthcare: past, present and future
- Predicting two-year survival versus non-survival after first myocardial infarction using machine learning and Swedish national register data
- Artificial neural network models for early diagnosis of hepatocellular carcinoma using serum levels of α-fetoprotein, α-fetoprotein-L3, des-γ-carboxy prothrombin, and Golgi protein 73
- A connectionist model for cerebrovascular accident risk prediction
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