Modeling and Visualization of Clinical Texts to Enhance Meaningful and User-Friendly Information Retrieval

Abstract

Access to digital health data collections such as clinical notes, discharge summaries, or medical charts has increased in the last few years due to the increased use of electronic health records, which provide instant access to patients’ clinical information. The volume and the unstructured nature of these datasets present great challenges in analyses and subsequent applications to healthcare. The growing volume of clinical data generated and stored in electronic health records creates challenges for physicians when reviewing patients’ records with the aim of understanding individual patients’ health histories. Electronic healthcare records contain large volumes of unstructured data, which require one to read through to get the required information. This is a challenging task due to lack of suitable techniques to quickly extract the needed information. Information processing tools in the clinical domain that provide support to users in seeking needed information are lacking. The use of data visualization has been introduced in an attempt to solve this problem; however, no single approach has been widely adopted. In this paper, we propose a unique approach for modeling clinical notes using the semantics of various units of a clinical text document to aid doctors in reviewing electronic clinical notes. This is achieved by applying the supervised machine learning technique to identify and present semantically similar information together, facilitating the identification of relevant information to users.

Keywords

• electronic health record
• classification
• clinical notes
• visualization