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Use of Machine Learning Algorithms to Predict the Understandability of Health Education Materials: Development and Evaluation Study

机译：使用机器学习算法预测健康教育材料的可理解性：发展与评估研究

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Background Improving the understandability of health information can significantly increase the cost-effectiveness and efficiency of health education programs for vulnerable populations. There is a pressing need to develop clinically informed computerized tools to enable rapid, reliable assessment of the linguistic understandability of specialized health and medical education resources. This paper fills a critical gap in current patient-oriented health resource development, which requires reliable and accurate evaluation instruments to increase the efficiency and cost-effectiveness of health education resource evaluation. Objective We aimed to translate internationally endorsed clinical guidelines to machine learning algorithms to facilitate the evaluation of the understandability of health resources for international students at Australian universities. Methods Based on international patient health resource assessment guidelines, we developed machine learning algorithms to predict the linguistic understandability of health texts for Australian college students (aged 25-30 years) from non-English speaking backgrounds. We compared extreme gradient boosting, random forest, neural networks, and C5.0 decision tree for automated health information understandability evaluation. The 5 machine learning models achieved statistically better results compared to the baseline logistic regression model. We also evaluated the impact of each linguistic feature on the performance of each of the 5 models. Results We found that information evidentness, relevance to educational purposes, and logical sequence were consistently more important than numeracy skills and medical knowledge when assessing the linguistic understandability of health education resources for international tertiary students with adequate English skills (International English Language Testing System mean score 6.5) and high health literacy (mean 16.5 in the Short Assessment of Health Literacy-English test). Our results challenge the traditional views that lack of medical knowledge and numerical skills constituted the barriers to the understanding of health educational materials. Conclusions Machine learning algorithms were developed to predict health information understandability for international college students aged 25-30 years. Thirteen natural language features and 5 evaluation dimensions were identified and compared in terms of their impact on the performance of the models. Health information understandability varies according to the demographic profiles of the target readers, and for international tertiary students, improving health information evidentness, relevance, and logic is critical.
机译：背景技术提高健康信息的可理解性可以显着提高弱势群体健康教育计划的成本效益和效率。迫切需要开发临床知识的计算机化工具，以实现对专业健康和医学教育资源的语言可理解性的快速，可靠地评估。本文填补了当前面向患者卫生资源开发的临界差距，需要可靠和准确的评估仪器，以提高健康教育资源评估的效率和成本效益。目标我们旨在将国际认可的临床指南转化为机器学习算法，促进评估澳大利亚大学国际学生健康资源的可理解性。基于国际患者健康资源评估指南的方法，我们开发了机器学习算法，以预测非英语语言背景的澳大利亚大学生（25 - 30岁）的健康文本的语言可理解性。我们比较了极端渐变升压，随机森林，神经网络和C5.0决策树的自动化健康信息可易懂性评估。与基线逻辑回归模型相比，5种机器学习模型实现了统计上更好的结果。我们还评估了每个语言特征对5种型号中的每一个的性能的影响。结果我们发现信息明智，与教育目的的相关性，逻辑顺序与数值技能和医学知识相比，在评估具有足够英语技能的国际高级学生的健康教育资源的语言理论（国际英语语言测试系统均值）时始终如一地更重要6.5）和高健康素养（平均16.5在短期评估健康识字术 - 英语考试中）。我们的成果挑战了传统的观点，即缺乏医学知识和数值技能构成了对健康教育材料的理解的障碍。结论制定了机器学习算法，以预测25 - 30岁的国际大学生的健康信息易于理解。根据其对模型性能的影响，确定了十三个自然语言特征和5个评估尺寸。健康信息可理解性根据目标读者的人口概况，以及国际高等教育学生，改善健康信息的明示，相关性和逻辑至关重要。

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