  There exists an invisible barrier between healthcare professionals'
perception of a patient's clinical experience and the reality. This barrier may
be induced by the environment that hinders patients from sharing their
experiences openly with healthcare professionals. As patients are observed to
discuss and exchange knowledge more candidly on social media, valuable insights
can be leveraged from these platforms. However, the abundance of non-patient
posts on social media necessitates filtering out such irrelevant content to
distinguish the genuine voices of patients, a task we refer to as patient voice
classification. In this study, we analyse the importance of linguistic
characteristics in accurately classifying patient voices. Our findings
underscore the essential role of linguistic and statistical text similarity
analysis in identifying common patterns among patient groups. These results
allude to even starker differences in the way patients express themselves at a
disease level and across various therapeutic domains. Additionally, we
fine-tuned a pre-trained Language Model on the combined datasets with similar
linguistic patterns, resulting in a highly accurate automatic patient voice
classification. Being the pioneering study on the topic, our focus on
extracting authentic patient experiences from social media stands as a crucial
step towards advancing healthcare standards and fostering a patient-centric
approach.
