Evidence record 18487 · automatically gathered

A Machine Learning Pipeline to Analyze Global Sentiment and Factors Influencing Retinoblastoma Treatment Hesitancy: Observational Infodemiology Study

Background: The use of social media in cancer research, patient support, and information sharing has been well documented. Objective: Using retinoblastoma as a model, we use the information provided from Twitter (subsequently rebranded X) to understand patients’ treatment-seeking behavior and barriers, as well as investigate its application in research and epidemiology for rare diseases. Methods: Posts on retinoblastoma were extracted from Twitter. We trained BERT (Bidirectional Encoder Represen

Record details

Published: 11 August 2026
Source: JMIR (Journal of Medical Internet Research)
Category: Research
Topics: Healthcare · Finance, VC & PE
Retrieved: 12 August 2026

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ethics.ai (11 August 2026), “A Machine Learning Pipeline to Analyze Global Sentiment and Factors Influencing Retinoblastoma Treatment Hesitancy: Observational Infodemiology Study,” evidence record 18487, https://ethics.ai/record/18487 (originally published by JMIR (Journal of Medical Internet Research)).

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