  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 utilizes data analysis methods to retrieve medical
information from YouTube videos concerning colonoscopy to manage health
conditions. 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 annotation and
recommendation. We develop a bidirectional long short-term memory (BLSTM) model
to identify medical terms in videos and build three classifiers to group videos
based on the level of encoded medical information, video understandability
level and whether the videos are recommended. Our study provides healthcare
practitioners and patients with guidelines for generating new educational video
content and enabling management of health conditions.
