Seizure prediction in stroke survivors who experienced an infection at skilled nursing facilities-a machine learning approach.

Autor:	Stanik M; Purdue University, Department of Engineering, Weldon School of Biomedical Engineering, West Lafayette, IN, United States., Hass Z; Purdue University, Schools of Industrial Engineering and Nursing, West Lafayette, IN, United States., Kong N; Purdue University, Department of Engineering, Weldon School of Biomedical Engineering, West Lafayette, IN, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in physiology [Front Physiol] 2024 May 30; Vol. 15, pp. 1399374. Date of Electronic Publication: 2024 May 30 (Print Publication: 2024).
DOI:	10.3389/fphys.2024.1399374
Abstrakt:	Background: Infections and seizures are some of the most common complications in stroke survivors. Infections are the most common risk factor for seizures and stroke survivors that experience an infection are at greater risk of experiencing seizures. A predictive model to determine which stroke survivors are at the greatest risk for a seizure after an infection can be used to help providers focus on prevention of seizures in higher risk residents that experience an infection. Methods: A predictive model was generated from a retrospective study of the Long-Term Care Minimum Data Set (MDS) 3.0 (2014-2018, n = 262,301). Techniques included three data balancing methods (SMOTE for up sampling, ENN for down sampling, and SMOTEENN for up and down sampling) and three feature selection methods (LASSO, Recursive Feature Elimination, and Principal Component Analysis). One balancing and one feature selection technique was applied, and the resulting dataset was then trained on four machine learning models (Logistic Regression, Random Forest, XGBoost, and Neural Network). Model performance was evaluated with AUC and accuracy, and interpretation used SHapley Additive exPlanations. Results: Using data balancing methods improved the prediction performances of the machine learning models, but feature selection did not remove any features and did not affect performance. With all models having a high accuracy (76.5%-99.9%), interpretation on all four models yielded the most holistic view. SHAP values indicated that therapy (speech, physical, occupational, and respiratory), independence (activities of daily living for walking, mobility, eating, dressing, and toilet use), and mood (severity score, anti-anxiety medications, antidepressants, and antipsychotics) features contributed the most. Meaning, stroke survivors who received fewer therapy hours, were less independent, had a worse overall mood were at a greater risk of having a seizure after an infection. Conclusion: The development of a tool to predict seizure following an infection in stroke survivors can be interpreted by providers to guide treatment and prevent complications long term. This promotes individualized treatment plans that can increase the quality of resident care. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2024 Stanik, Hass and Kong.)
Databáze:	MEDLINE
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