Rahim Afiq Izzudin A, Ibrahim Mohd Ismail, Chua Sook-Ling, Musa Kamarul Imran
Department of Community Medicine, School of Medical Science, Universiti Sains Malaysia, Kubang Kerian, Kota Bharu 16150, Kelantan, Malaysia.
Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Selangor, Malaysia.
Healthcare (Basel). 2021 Dec 3;9(12):1679. doi: 10.3390/healthcare9121679.
While experts have recognised the significance and necessity of social media integration in healthcare, no systematic method has been devised in Malaysia or Southeast Asia to include social media input into the hospital quality improvement process. The goal of this work is to explain how to develop a machine learning system for classifying Facebook reviews of public hospitals in Malaysia by using service quality (SERVQUAL) dimensions and sentiment analysis. We developed a Machine Learning Quality Classifier (MLQC) based on the SERVQUAL model and a Machine Learning Sentiment Analyzer (MLSA) by manually annotated multiple batches of randomly chosen reviews. Logistic regression (LR), naive Bayes (NB), support vector machine (SVM), and other methods were used to train the classifiers. The performance of each classifier was tested using 5-fold cross validation. For topic classification, the average F1-score was between 0.687 and 0.757 for all models. In a 5-fold cross validation of each SERVQUAL dimension and in sentiment analysis, SVM consistently outperformed other methods. The study demonstrates how to use supervised learning to automatically identify SERVQUAL domains and sentiments from patient experiences on a hospital's Facebook page. Malaysian healthcare providers can gather and assess data on patient care via the use of these content analysis technology to improve hospital quality of care.
虽然专家们已经认识到社交媒体融入医疗保健的重要性和必要性，但马来西亚或东南亚尚未设计出将社交媒体输入纳入医院质量改进过程的系统方法。这项工作的目标是解释如何通过使用服务质量（SERVQUAL）维度和情感分析来开发一个机器学习系统，用于对马来西亚公立医院的Facebook评论进行分类。我们基于SERVQUAL模型开发了一个机器学习质量分类器（MLQC），并通过人工标注多批次随机选择的评论开发了一个机器学习情感分析器（MLSA）。使用逻辑回归（LR）、朴素贝叶斯（NB）、支持向量机（SVM）等方法对分类器进行训练。使用5折交叉验证测试每个分类器的性能。对于主题分类，所有模型的平均F1分数在0.687至0.757之间。在每个SERVQUAL维度的5折交叉验证和情感分析中，SVM始终优于其他方法。该研究展示了如何使用监督学习从医院Facebook页面上的患者体验中自动识别SERVQUAL领域和情感。马来西亚的医疗保健提供者可以通过使用这些内容分析技术来收集和评估有关患者护理的数据，以提高医院的护理质量。
