Zhu Hansheng
The Independent Schools Foundation Academy, Hong Kong, China.
Front Psychol. 2022 Jul 11;13:915653. doi: 10.3389/fpsyg.2022.915653. eCollection 2022.
According to the WHO, 15 million people worldwide suffer a stroke annually. Of these, 5 million die and another 5 million are left permanently disabled. Patients endure the impacts of strokes during their rehabilitation and afterward, placing economical and emotional burdens on family and community. Using data from the Health and Retirement Study (HRS) of the USA, the research performed a large-scale prospective analysis to examine how demographics, socioeconomic factors, cognition, emotion, and physical activity predict functional impairment and mortality. Multiple regression was employed to identify statistically significant variables that predict longitudinal Activities of Daily Life (ADLs). The least absolute shrinkage and selection operator (LASSO) logistic regression, a supervised machine learning approach, was deliberately chosen to obtain the subset of predictors that provide the best possible classification for the dependent variable. The LASSO regression produced a model with a fair mean Area Under the Curve (AUC) of 0.75 in predicting the risk of the patient's mortality. My findings also uncovered the important roles of BMI, mobility, muscle strength, memory, mental status, and socioeconomic status play in the long-term ADLs and survival of patients with stroke.
根据世界卫生组织的数据，全球每年有1500万人中风。其中，500万人死亡，另有500万人永久致残。患者在康复期间及之后都要承受中风的影响，给家庭和社区带来经济和情感负担。该研究利用美国健康与退休研究（HRS）的数据进行了大规模前瞻性分析，以研究人口统计学、社会经济因素、认知、情感和身体活动如何预测功能障碍和死亡率。采用多元回归来确定预测纵向日常生活活动（ADL）的统计学显著变量。特意选择了最小绝对收缩和选择算子（LASSO）逻辑回归这一监督式机器学习方法，以获得能为因变量提供最佳分类的预测变量子集。LASSO回归生成了一个模型，在预测患者死亡风险方面，其平均曲线下面积（AUC）为0.75，表现尚可。我的研究结果还揭示了体重指数（BMI）、活动能力、肌肉力量、记忆力、精神状态和社会经济地位在中风患者长期日常生活活动和生存中的重要作用。
