  The medical domain is often subject to information overload. The digitization
of healthcare, constant updates to online medical repositories, and increasing
availability of biomedical datasets make it challenging to effectively analyze
the data. This creates additional work for medical professionals who are
heavily dependent on medical data to complete their research and consult their
patients. This paper aims to show how different text highlighting techniques
can capture relevant medical context. This would reduce the doctors' cognitive
load and response time to patients by facilitating them in making faster
decisions, thus improving the overall quality of online medical services. Three
different word-level text highlighting methodologies are implemented and
evaluated. The first method uses TF-IDF scores directly to highlight important
parts of the text. The second method is a combination of TF-IDF scores and the
application of Local Interpretable Model-Agnostic Explanations to
classification models. The third method uses neural networks directly to make
predictions on whether or not a word should be highlighted. The results of our
experiments show that the neural network approach is successful in highlighting
medically-relevant terms and its performance is improved as the size of the
input segment increases.
