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Application of Machine Learning Techniques to Identify Data Reliability and Factors Affecting Outcome After Stroke Using Electronic Administrative Records
Conclusion: Electronic administrative records from this cohort produced reliable outcome prediction and identified clinically appropriate factors negatively impacting most outcome variables following hospital admission with stroke. This presents a means of future identification of modifiable factors associated with patient discharge destination. This may potentially aid in patient selection for certain interventions and aid in better patient and clinician education regarding expected discharge outcomes.
Source: Frontiers in Neurology - September 27, 2021 Category: Neurology Source Type: research

Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients
Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems' resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.
Source: Frontiers in Neurology - March 31, 2021 Category: Neurology Source Type: research

Prediction of 30-Day Readmission After Stroke Using Machine Learning and Natural Language Processing
Conclusion: NLP-enhanced machine learning models potentially advance our ability to predict readmission after stroke. However, further improvement is necessary before being implemented in clinical practice given the weak discrimination.
Source: Frontiers in Neurology - July 13, 2021 Category: Neurology Source Type: research

Clinical Risk Score for Predicting Recurrence Following a Cerebral Ischemic Event
Conclusion: The clinical risk scores that currently exist for predicting short-term and long-term risk of recurrent cerebral ischemia are limited in their performance and clinical utilities. There is a need for a better predictive tool which can overcome the limitations of current predictive models. Application of machine learning methods in combination with electronic health records may provide platform for development of new-generation predictive tools.
Source: Frontiers in Neurology - November 11, 2019 Category: Neurology Source Type: research

More Research Is Needed on Lifestyle Behaviors That Influence Progression of Parkinson's Disease
This article highlights some of these challenges in the design of lifestyle studies in PD, and suggests a more coordinated international effort is required, including ongoing longitudinal observational studies. In combination with pharmaceutical treatments, healthy lifestyle behaviors may slow the progression of PD, empower patients, and reduce disease burden. For optimal care of people with PD, it is important to close this gap in current knowledge and discover whether such associations exist. Introduction Parkinson's disease (PD) is an age-related complex progressive neurodegenerative disorder, with key p...
Source: Frontiers in Neurology - April 29, 2019 Category: Neurology Source Type: research