![]() ![]() Secondly, the complex ways in which users interact and spread information (or at times, unfortunately, misinformation) on a forum can be tracked using network analysis techniques that trace the chain of replies across threads, highlighting user communities. One aspect of our work uses natural language processing techniques to mine individual posts, with the aim of interpreting the meaning of words and sentences automatically. Technically, OHF data presents a multi-layered challenge. In our work at the Turing, in cooperation with colleagues at Queen Mary University of London (QMUL), Barts Cancer Institute and King’s College London, we have applied a blend of machine learning techniques to OHF data in order to map the impact of disease on patients’ daily lives. uses that haven’t been approved by regulators). The millions of OHF threads and posts that chart personal experience of disease contain a wealth of information on interaction with health systems, paths to diagnosis, comorbidities, patient concerns, and side effects and emerging off-label uses of medications (i.e. ![]() Often run by charities and patient advocacy groups, OHFs allow patients, and at times healthcare providers, to post about health conditions and concerns in search of advice, practical information and emotional support. This increasing focus on the patient voice has led to a greater interest in online health forums (OHFs) – public message boards such as HealthBoards and Inspire that have been quietly chronicling the experiences, anxieties, suffering and resilience of millions of patients worldwide over the past 20 years. For this reason, regulators worldwide, including the US Food and Drug Administration and the European Medicines Agency, are pushing for patients and their experience to be put at the centre of clinical research. The development of new and effective drugs, health services and policies requires close cooperation between clinical researchers, the pharmaceutical industry and patients. Research involving the Turing is using AI to extract invaluable information about patients’ individual journeys ![]()
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