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Predicting the risk of stroke in patients with late-onset epilepsy: A machine learning approach
CONCLUSION: The stroke risk in patients with epilepsy was relatively high and could be predicted based on comorbidities such as diabetes mellitus, hypertension, heart failure, and alcohol dependence. Knowing and addressing these factors may help reduce the risk of stroke in patients with epilepsy.PMID:34325155 | DOI:10.1016/j.yebeh.2021.108211
Source: Epilepsy and Behaviour - July 29, 2021 Category: Neurology Authors: Karel Kostev Tong Wu Yue Wang Kal Chaudhuri Christian Tanislav Source Type: research

Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing
CONCLUSION: PIP successfully predicted readmissions for most diseases, opening the possibility for interventions to improve these modifiable risk factors. Machine-learning methods appear promising for future modeling of PIP predictors in complex older patients with many underlying diseases.PMID:35144291 | DOI:10.1055/s-0042-1742671
Source: Methods of Information in Medicine - February 10, 2022 Category: Information Technology Authors: Alexander Gerharz Carmen Ruff Lucas Wirbka Felicitas Stoll Walter E Haefeli Andreas Groll Andreas D Meid Source Type: research