Word-level Text Highlighting of Medical Texts for Telehealth Services

TL;DR

This paper explores three word-level text highlighting methods to help medical professionals quickly identify relevant information in healthcare texts, aiming to reduce cognitive load and improve decision-making speed.

Contribution

It introduces and evaluates three novel text highlighting techniques, including a neural network approach, for medical texts to enhance information retrieval efficiency.

Findings

Neural network method effectively highlights relevant medical terms.

Highlighting performance improves with larger input segments.

Combining TF-IDF with interpretability models offers a viable baseline.

Abstract

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.