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YouTube for Patient Education: A Deep Learning Approach for Understanding Medical Knowledge from User-Generated Videos

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arxiv 1807.03179 v1 pith:QAQBLWSY submitted 2018-07-06 cs.CV cs.LGstat.ML

classification cs.CVcs.LGstat.ML
keywords knowledgelearningvideosyoutubedeepmedicalmethodseducation
verification ladder T0 review T1 audit T2 compute T3 formal

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YouTube presents an unprecedented opportunity to explore how machine learning methods can improve healthcare information dissemination. We propose an interdisciplinary lens that synthesizes machine learning methods with healthcare informatics themes to address the critical issue of developing a scalable algorithmic solution to evaluate videos from a health literacy and patient education perspective. We develop a deep learning method to understand the level of medical knowledge encoded in YouTube videos. Preliminary results suggest that we can extract medical knowledge from YouTube videos and classify videos according to the embedded knowledge with satisfying performance. Deep learning methods show great promise in knowledge extraction, natural language understanding, and image classification, especially in an era of patient-centric care and precision medicine.

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Cited by 1 Pith paper

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  1. Towards Automatic Detection of Misinformation in Online Medical Videos

    cs.LG 2019-09 conditional novelty 6.0 of 10

    A multimodal classifier can flag likely misinformative prostate cancer videos on YouTube with roughly 74% accuracy, using a new expert-labeled dataset.

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