分析肠镜准备视频，帮医生优化患者教育内容。
YouTube Video Analytics for Patient Engagement: Evidence from Colonoscopy Preparation Videos
- 用API收集关键词视频，提取文本、画面和物体信息。
- 构建BiLSTM模型识别医疗术语，三类分类器评估内容与易懂度。
- 为医疗教育视频制作提供可复制的分析框架，适合临床研究者。
视频是向患者传递情境化、即时性医疗信息的有效方式。然而，从主题识别、信息检索到从患者视角提取和分析医疗信息及理解度，仍属极具挑战的任务。本研究构建了一个数据处理流程，分析针对肠镜检查准备的YouTube视频，该检查常被患者视为困难且不情愿完成。首先通过YouTube Data API获取指定关键词视频的元数据，并利用Google Video Intelligence API分析视频中的文字、图像帧和物体内容。随后对视频材料进行医学信息、可理解性和整体推荐度的人工标注。开发双向长短期记忆网络（BiLSTM）模型以识别视频中的医学术语，并建立三个分类器，按编码的医学信息量、视频可理解度以及是否被推荐进行分组。研究为医疗相关方提供了生成新型教育视频内容的指导原则和可扩展方法，有助于提升对大量健康状况的管理效率。
原文摘要 · Abstract (English)
Videos can be an effective way to deliver contextualized, just-in-time medical information for patient education. However, video analysis, from topic identification and retrieval to extraction and analysis of medical information and understandability from a patient perspective are extremely challenging tasks. This study demonstrates a data analysis pipeline that utilizes methods to retrieve medical information from YouTube videos on preparing for a colonoscopy exam, a much maligned and disliked procedure that patients find challenging to get adequately prepared for. We first use the YouTube Data API to collect metadata of desired videos on select search keywords and use Google Video Intelligence API to analyze texts, frames and objects data. Then we annotate the YouTube video materials on medical information, video understandability and overall recommendation. We develop a bidirectional long short-term memory (BiLSTM) model to identify medical terms in videos and build three classifiers to group videos based on the levels of encoded medical information and video understandability, and whether the videos are recommended or not. Our study provides healthcare stakeholders with guidelines and a scalable approach for generating new educational video content to enhance management of a vast number of health conditions.
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