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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

Context is Key: Using the Audit Log to Capture Contextual Factors Affecting Stroke Care Processes
AMIA Annu Symp Proc. 2021 Jan 25;2020:953-962. eCollection 2020.ABSTRACTHigh quality patient care through timely, precise and efficacious management depends not only on the clinical presentation of a patient, but the context of the care environment to which they present. Understanding and improving factors that affect streamlined workflow, such as provider or department busyness or experience, are essential to improving these care processes, but have been difficult to measure with traditional approaches and clinical data sources. In this exploratory data analysis, we aim to determine whether such contextual factors can be ...
Source: AMIA Annual Symposium Proceedings - May 3, 2021 Category: Bioinformatics Authors: Morteza Noshad Christian C Rose Robert Thombley Jonathan Chiang Conor K Corbin Minh Nguyen Vincent X Liu Julia Adler-Milstein Jonathan H Chen Source Type: research

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