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A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset
Conclusion: The approach proposed in this paper has effectively reduced the false negative rate with a relatively high overall accuracy, which means a successful decrease in the misdiagnosis rate for stroke prediction. The results are more reliable and valid as the reference in stroke prognosis, and also can be acquired conveniently at a low cost.
Source: Artificial Intelligence in Medicine - October 25, 2019 Category: Bioinformatics Source Type: research

Predicting discharge mortality after acute ischemic stroke using balanced data.
Authors: Ho KC, Speier W, El-Saden S, Liebeskind DS, Saver JL, Bui AA, Arnold CW Abstract Several models have been developed to predict stroke outcomes (e.g., stroke mortality, patient dependence, etc.) in recent decades. However, there is little discussion regarding the problem of between-class imbalance in stroke datasets, which leads to prediction bias and decreased performance. In this paper, we demonstrate the use of the Synthetic Minority Over-sampling Technique to overcome such problems. We also compare state of the art machine learning methods and construct a six-variable support vector machine (SVM) model ...
Source: AMIA Annual Symposium Proceedings - September 25, 2015 Category: Bioinformatics Tags: AMIA Annu Symp Proc Source Type: research

A new approach to distinguish migraine from stroke by mining structured and unstructured clinical data-sources
In this study, we utilized text and data mining methods to extract the most important predictors from clinical reports in order to establish a migraine detection model and distinguish migraine patients from stroke or other types of mimic (non-stroke) cases. The available data for this study was a heterogeneous mix of free-text fields, such as triage main-complaints and specialist final-impressions, as well as numeric data about patients, such as age, blood-pressure, and so on. After a careful combination of these sources, we obtained a highly imbalanced dataset where the migraine cases were only about 6  % of the dataset....
Source: Network Modeling Analysis in Health Informatics and Bioinformatics - October 5, 2016 Category: Bioinformatics Source Type: research

Using Machine Learning to Improve the Prediction of Functional Outcome in Ischemic Stroke Patients
Ischemic stroke is a leading cause of disability and death worldwide among adults. The individual prognosis after stroke is extremely dependent on treatment decisions physicians take during the acute phase. In the last five years, several scores such as the ASTRAL, DRAGON, and THRIVE have been proposed as tools to help physicians predict the patient functional outcome after a stroke. These scores are rule-based classifiers that use features available when the patient is admitted to the emergency room. In this paper, we apply machine learning techniques to the problem of predicting the functional outcome of ischemic stroke ...
Source: IEEE/ACM Transactions on Computational Biology and Bioinformatics - December 11, 2018 Category: Bioinformatics Source Type: research

Classifying Acute Ischemic Stroke Onset Time using Deep Imaging Features.
Authors: Ho KC, Speier W, El-Saden S, Arnold CW Abstract Models have been developed to predict stroke outcomes (e.g., mortality) in attempt to provide better guidance for stroke treatment. However, there is little work in developing classification models for the problem of unknown time-since-stroke (TSS), which determines a patient's treatment eligibility based on a clinical defined cutoff time point (i.e., <4.5hrs). In this paper, we construct and compare machine learning methods to classify TSS<4.5hrs using magnetic resonance (MR) imaging features. We also propose a deep learning model to extract hidden rep...
Source: AMIA Annual Symposium Proceedings - March 20, 2019 Category: Bioinformatics Tags: AMIA Annu Symp Proc Source Type: research

Stroke Risk Stratification and its Validation using Ultrasonic Echolucent Carotid Wall Plaque Morphology: A Machine Learning Paradigm
Stroke risk stratification based on grayscale morphology of the ultrasound carotid wall has recently been shown to have a promise in classification of high risk versus low risk plaque or symptomatic versus asymptomatic plaques. In previous studies, this stratification has been mainly based on analysis of the far wall of the carotid artery. Due to the multifocal nature of atherosclerotic disease, the plaque growth is not restricted to the far wall alone. This paper presents a new approach for stroke risk assessment by integrating assessment of both the near and far walls of the carotid artery using grayscale morphology of the plaque.
Source: Computers in Biology and Medicine - November 25, 2016 Category: Bioinformatics Authors: Tadashi Araki, Pankaj K. Jain, Harman S. Suri, Narendra D. Londhe, Nobutaka Ikeda, Ayman El-Baz, Vimal K. Shrivastava, Luca Saba, Andrew Nicolaides, Shoaib Shafique, John R. Laird, Ajay Gupta, Jasjit S. Suri Source Type: research

Integrated Machine Learning Approaches for Predicting Ischemic Stroke and Thromboembolism in Atrial Fibrillation.
In this study, we used integrated machine learning and data mining approaches to build 2-year TE prediction models for AF from Chinese Atrial Fibrillation Registry data. We first performed data cleansing and imputation on the raw data to generate available dataset. Then a series of feature construction and selection methods were used to identify predictive risk factors, based on which supervised learning methods were applied to build the prediction models. The experimental results show that our approach can achieve higher prediction performance (AUC: 0.71~0.74) than previous TE prediction models for AF (AUC: 0.66~0.69), an...
Source: AMIA Annual Symposium Proceedings - August 8, 2017 Category: Bioinformatics Tags: AMIA Annu Symp Proc Source Type: research

A Remote Quantitative Fugl-Meyer Assessment Framework for Stroke Patients Based on Wearable Sensor Networks
To extend the use of wearable sensor networks for stroke patients training and assessment in non-clinical settings, this paper proposes a novel remote quantitative Fugl-Meyer assessment (FMA) framework, in which two accelerometer and seven flex sensors were used to monitoring the movement function of upper limb, wrist and fingers. The extreme learning machine based ensemble regression model was established to map the sensor data to clinical FMA scores while the RRelief algorithm was applied to find the optimal features subset.
Source: Computer Methods and Programs in Biomedicine - March 2, 2016 Category: Bioinformatics Authors: Lei Yu, Daxi Xiong, Liquan Guo, Jiping Wang Source Type: research

Improving Anticoagulant Treatment Strategies of Atrial Fibrillation Using Reinforcement Learning
AMIA Annu Symp Proc. 2021 Jan 25;2020:1431-1440. eCollection 2020.ABSTRACTIn this paper, we developed a personalized anticoagulant treatment recommendation model for atrial fibrillation (AF) patients based on reinforcement learning (RL) and evaluated the effectiveness of the model in terms of short-term and long-term outcomes. The data used in our work were baseline and follow-up data of 8,540 AF patients with high risk of stroke, enrolled in the Chinese Atrial Fibrillation Registry (CAFR) study during 2011 to 2018. We found that in 64.98% of patient visits, the anticoagulant treatment recommended by the RL model were conc...
Source: AMIA Annual Symposium Proceedings - May 3, 2021 Category: Bioinformatics Authors: Lei Zuo Xin Du Wei Zhao Chao Jiang Shijun Xia Liu He Rong Liu Ribo Tang Rong Bai Jianzeng Dong Xingzhi Sun Gang Hu Guotong Xie Changsheng Ma Source Type: research

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