Prognostic estimation for acute ischemic stroke patients undergoing mechanical thrombectomy within an extended therapeutic window using an interpretable machine learning model

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

(2023) Sajid Nazir et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Thrombectomy with and without computed tomography perfusion imaging for large-vessel occlusion stroke in the extended time window: a meta-analysis of randomized clinical trials

(2023) Zheng Zhan et al.   Frontiers in Neurology

Interpretable Machine Learning Modeling for Ischemic Stroke Outcome Prediction

(2022) Mohamed Sobhi Jabal et al.   Frontiers in Neurology

Machine Learning for Hypertension Prediction: a Systematic Review

(2022) Gabriel F. S. Silva et al.   CURRENT HYPERTENSION REPORTS

Use of Machine Learning Algorithms to Predict the Outcomes of Mechanical Thrombectomy in Acute Ischemic Stroke Patients With an Extended Therapeutic Time Window

(2022) Shanshan Lu et al.   JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY

Development and validation of comprehensive clinical outcome prediction models for acute ischaemic stroke in anterior circulation based on machine learning

(2022) Haiyan Zhang et al.   JOURNAL OF CLINICAL NEUROSCIENCE

Interpretable machine learning model to predict rupture of small intracranial aneurysms and facilitate clinical decision

(2022) WeiGen Xiong et al.   NEUROLOGICAL SCIENCES

An interpretable machine learning prognostic system for risk stratification in oropharyngeal cancer

(2022) Rasheed Omobolaji Alabi et al.   INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS

Common Data Elements Analysis of Mechanical Thrombectomy Clinical Trials for Acute Ischemic Stroke with Large Core Infarct

(2022) Mohamed Sobhi Jabal et al.   Clinical Neuroradiology

Prediction of Clinical Outcome in Patients with Large-Vessel Acute Ischemic Stroke: Performance of Machine Learning versus SPAN-100

(2021) B. Jiang et al.   AMERICAN JOURNAL OF NEURORADIOLOGY

Using a Multiclass Machine Learning Model to Predict the Outcome of Acute Ischemic Stroke Requiring Reperfusion Therapy

(2021) I-Min Chiu et al.   Diagnostics

Explanation of machine learning models using shapley additive explanation and application for real data in hospital

(2021) Yasunobu Nohara et al.   COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

Mechanical Thrombectomy for Acute Stroke: Early versus Late Time Window Outcomes

(2020) Chantal Bhan et al.   JOURNAL OF NEUROIMAGING

Comparing extended versus standard time window for thrombectomy: caseload, patient characteristics, treatment rates and outcomes—a prospective single-centre study

(2020) Bence Gunda et al.   NEURORADIOLOGY

Multimodal Predictive Modeling of Endovascular Treatment Outcome for Acute Ischemic Stroke Using Machine-Learning

(2020) Gianluca Brugnara et al.   STROKE

Machine‐learning‐based outcome prediction in stroke patients with middle cerebral artery‐M1 occlusions and early thrombectomy

(2020) Janne Hamann et al.   EUROPEAN JOURNAL OF NEUROLOGY

Application of Cooperative Game Theory Principles to Interpret Machine Learning Models of Nonhome Discharge Following Spine Surgery

(2020) Michael L. Martini et al.   SPINE

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

(2020) Erico Tjoa et al.   IEEE Transactions on Neural Networks and Learning Systems

Thinking About the Future: A Review of Prognostic Scales Used in Acute Stroke

(2019) Bogna A. Drozdowska et al.   Frontiers in Neurology

Machine Learning–Based Model for Prediction of Outcomes in Acute Stroke

(2019) JoonNyung Heo et al.   STROKE

Predicting Clinical Outcomes of Large Vessel Occlusion Before Mechanical Thrombectomy Using Machine Learning

(2019) Hidehisa Nishi et al.   STROKE

Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review

(2019) Haneen Arafat Abu Alfeilat et al.   Big Data

Predicting Clinical Outcome After Mechanical Thrombectomy: The GADIS (Gender, Age, Diabetes Mellitus History, Infarct Volume, and Sex) Score

(2019) Kyle P. O'Connor et al.   World Neurosurgery

Comparing different supervised machine learning algorithms for disease prediction

(2019) Shahadat Uddin et al.   BMC Medical Informatics and Decision Making

Thrombectomy for Stroke at 6 to 16 Hours with Selection by Perfusion Imaging

(2018) Gregory W. Albers et al.   NEW ENGLAND JOURNAL OF MEDICINE

Thrombectomy 6 to 24 Hours after Stroke with a Mismatch between Deficit and Infarct

(2018) Raul G. Nogueira et al.   NEW ENGLAND JOURNAL OF MEDICINE

Eligibility for Endovascular Trial Enrollment in the 6- to 24-Hour Time Window

(2018) Ashutosh P. Jadhav et al.   STROKE

Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI)

(2018) Amina Adadi et al.   IEEE Access

JOURNAL CLUB: Use of Gradient Boosting Machine Learning to Predict Patient Outcome in Acute Ischemic Stroke on the Basis of Imaging, Demographic, and Clinical Information

(2018) Yuan Xie et al.   AMERICAN JOURNAL OF ROENTGENOLOGY

Time to Treatment With Endovascular Thrombectomy and Outcomes From Ischemic Stroke: A Meta-analysis

(2016) Jeffrey L. Saver et al.   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION

Extreme Gradient Boosting as a Method for Quantitative Structure–Activity Relationships

(2016) Robert P. Sheridan et al.   Journal of Chemical Information and Modeling

Predicting the Future — Big Data, Machine Learning, and Clinical Medicine

(2016) Ziad Obermeyer et al.   NEW ENGLAND JOURNAL OF MEDICINE

Mechanical thrombectomy in acute ischemic stroke: Consensus statement by ESO-Karolinska Stroke Update 2014/2015, supported by ESO, ESMINT, ESNR and EAN

(2015) Nils Wahlgren et al.   International Journal of Stroke

Endovascular stent thrombectomy: the new standard of care for large vessel ischaemic stroke

(2015) Bruce C V Campbell et al.   LANCET NEUROLOGY

Cooperative Game Theory: Basic Concepts and Computational Challenges

(2012) Georgios Chalkiadakis et al.   IEEE INTELLIGENT SYSTEMS

Male Circumcision

(2012)   PEDIATRICS