感染和癫痫是中风幸存者中最常见的一些并发症。感染是癫痫发作最常见的危险因素,经历感染的中风幸存者有更大的癫痫发作风险。确定哪些中风幸存者在感染后癫痫发作风险最大的预测模型可用于帮助提供者专注于预防经历感染的高风险居民的癫痫发作。
■从长期护理最低数据集(MDS)3.0(2014-2018年,n=262,301)的回顾性研究中生成了预测模型。技术包括三种数据平衡方法(用于上采样的SMOTE,ENN用于向下采样,和用于上下采样的SMOTEENN)和三种特征选择方法(LASSO,递归特征消除,和主成分分析)。应用了一种平衡和一种特征选择技术,然后在四个机器学习模型(Logistic回归,随机森林,XGBoost,和神经网络)。用AUC和准确性评估模型性能,和解释使用Shapley添加剂扩张。
■使用数据平衡方法提高了机器学习模型的预测性能,但是功能选择没有删除任何功能,也没有影响性能。所有模型都具有较高的精度(76.5%-99.9%),对所有四个模型的解释产生了最全面的观点。SHAP值表明治疗(言语,物理,职业,和呼吸),独立性(步行的日常生活活动,移动性,吃,敷料,和厕所使用),和情绪(严重程度评分,抗焦虑药物,抗抑郁药,和抗精神病药)的特征贡献最大。意思是,接受治疗时间较少的中风幸存者,不太独立,如果整体情绪较差,感染后癫痫发作的风险更大。
■提供者可以解释一种预测中风幸存者感染后癫痫发作的工具的开发,以指导治疗和预防长期并发症。这促进了可以提高住院医师护理质量的个性化治疗计划。
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.