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Applying Unsupervised Keyphrase Methods on Concepts Extracted from Discharge Sheets by Memarzadeh, Hoda; Ghadiri, Nasser; Samwald, Matthias; Shahreza, Maryam Lotfi is a scholarly article available to read on EtoBox.

What is Applying Unsupervised Keyphrase Methods on Concepts Extracted from Discharge Sheets about?

Clinical notes containing valuable patient information are written by different health care providers with various scientific levels and writing styles. It might be helpful for clinicians and researchers to understand what information is essential when dealing with extensive electronic medical records. Entities recognizing and mapping them to standard terminologies is crucial in reducing ambiguity in processing clinical notes. Although named entity recognition and entity linking are critical steps in clinical natural language processing, they can also result in the production of repetitive and low-value concepts. In other hand, all parts of a clinical text do not share the same importance or content in predicting the patient's condition. As a result, it is necessary to identify the section in which each content is recorded and also to identify key concepts to extract meaning from clinical texts. In this study, these challenges have been addressed by using clinical natural language processing techniques. In addition, in order to identify key concepts, a set of popular unsupervised key phrase extraction methods has been verified and evaluated. Considering that most of the clinical co

Author

Memarzadeh, Hoda; Ghadiri, Nasser; Samwald, Matthias; Shahreza, Maryam Lotfi

Published

2023

Language

EN