  Over the last decade, health communities (known as forums) have evolved into
platforms where more and more users share their medical experiences, thereby
seeking guidance and interacting with people of the community. The shared
content, though informal and unstructured in nature, contains valuable medical
and/or health-related information and can be leveraged to produce structured
suggestions to the common people. In this paper, at first we propose a stacked
deep learning model for sentiment analysis from the medical forum data. The
stacked model comprises of Convolutional Neural Network (CNN) followed by a
Long Short Term Memory (LSTM) and then by another CNN. For a blog classified
with positive sentiment, we retrieve the top-n similar posts. Thereafter, we
develop a probabilistic model for suggesting the suitable treatments or
procedures for a particular disease or health condition. We believe that
integration of medical sentiment and suggestion would be beneficial to the
users for finding the relevant contents regarding medications and medical
conditions, without having to manually stroll through a large amount of
unstructured contents.
