Real-world evidence from online patient forums can complement current medical perspectives: The example of gastrointestinal stromal tumor patients
by Anne Dirkson
Disease-specific internet discussion forums have the potential to provide real-time, uncensored and unsolicited information on both adverse drug effects (ADEs) and the advice patients give each other on how to cope with them.
The automatic extraction of ADEs could complement current post-market monitoring of drugs, which suffers from severe under-reporting. To this end, we have developed a text mining pipeline to automatically extract and aggregate side effects from messages on online discussion forums. We show that our automated approach has the potential to reveal side effects that were not found in the original clinical trial, as well as long-term side effects and the side effects that matter most to the patients on a patient forum for Gastrointestinal Stromal Tumor (GIST) patients.
The automatic extraction of self-reported coping strategies could empower patients by providing them with aggregate insights, as well as facilitating medical research into why certain strategies work. In fact, some strategies may work to the detriment of the medication efficacy. We will share some work in progress on this novel clinical NLP task that includes many interesting challenges such as fuzzy entities, a large and long-tailed label space, and cross-document relations.
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