BACKGROUND
Widespread influence on social media has its ramifications on all walks of life over the last few decades. Interestingly enough, the healthcare sector is a significant beneficiary of the reports and pronouncements that appear on social media. Although medics and other health professionals are the final decision-makers, advice or recommendations from kindred patients has consequential role. In full appreciation of the current trend, the present paper explores the topics pertaining to the patients, diagnosed with breast cancer as well as the survivors, who are discussing on online fora.
OBJECTIVE
The study examines the online forum of Breast Cancer.org (BCO), automatically maps discussion entries to formal topics, and proposes a machine learning model to characterize the topics in the health-related discussion, so as to elicit meaningful deliberations. Therefore, the study of communication messages draws conclusions about what matters to the patients.
METHODS
Manual annotation was made in the posts of a few randomly selected forums. To explore the topics of breast cancer patients and survivors, 736 posts are selected for semantic annotation. The entire process was automated using machine learning model falling into category of supervised learning algorithms. The effectiveness of those algorithms used for above process has been compared.
RESULTS
The method could classify following 8-high level topics, such as writing medication reviews, explaining the adverse effects of medication, clinician knowledge, various treatment options, seeking and supporting various matters, diagnostic procedures, financial issues and implications in everyday life. The model viz. Ensembled Neural Network (ENN) achieved a promising predicted score of 83.4 % F1-score among four different models.
CONCLUSIONS
The research was able to segregate and name the posts all into a set of 8 classes and supported by the efficient scheme for encoding text to vectors, the current machine learning models are shown to give impressive performance in modelling the annotation process.