<p dir="ltr">Recurrent stroke, infection, and seizure are some of the most common complications in stroke survivors. Recurrent stroke leads to death in 38.6% of survivors, and infections are the most common risk factor for seizures, with stroke survivors that experience an infection being at greater risk of experiencing a seizure. Two predictive models were generated, recurrent stroke and post-infection seizure, to determine stroke survivors at greatest risk to help providers focus on prevention in higher risk residents.</p><p dir="ltr">Predictive models were 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, RFE, and PCA). The resulting datasets were 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 Addictive exPlanations.</p><p dir="ltr">Using data balancing methods improved the prediction performances of the machine learning models, but feature selection did not remove any features or affect performance. With all models having a high accuracy (78.6% to 99.9%), interpretation on all four models yielded the most holistic view. For recurrent stroke, SHAP values indicated that treatment combinations of occupational therapy, physical therapy, antidepressants, non-medical intervention for pain, therapeutic diet, anticoagulants, and diuretics contributed more to reducing recurrent stroke risk in the model when compared to individual treatments. For post-infection seizure, 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, and had a worse overall mood were at a greater risk of having a post-infection seizure.</p><p dir="ltr">The development of a tool to predict recurrent stroke and post-infection seizure in stroke survivors can be interpreted by providers to guide treatment and rehabilitation to prevent complications long-term. This promotes individualized plans that can increase the quality of resident care.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/25664682 |
| Date | 22 April 2024 |
| Creators | Madeleine Gwynn Stanik (18422118) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/_b_Predicting_The_Risks_of_Recurrent_Stroke_and_Post-Infection_Seizure_in_Residents_of_Skilled_Nursing_Facilities_-_A_Machine_Learning_Approach_b_/25664682 |
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