@inproceedings{c406decfd0b24c698dc5cd4ae55a87cc,
title = "Mining Concepts for a COVID Interface Terminology for Annotation of EHRs",
abstract = "The COVID-19 pandemic has overwhelmed the healthcare services of many countries with increased number of patients and also with a deluge of medical data. Furthermore, the emergence and global spread of new infectious diseases are highly likely to continue in the future. Incomplete data about presentations, signs, and symptoms of COVID-19 has had adverse effects on healthcare delivery. The EHRs of US hospitals have ingested huge volumes of relevant, up-to-date data about patients, but the lack of a proper system to annotate this data has greatly reduced its usefulness. We propose to design a COVID interface terminology for the annotation of EHR notes of COVID-19 patients. The initial version of this interface terminology was created by integrating COVID concepts from existing ontologies. Further enrichment of the interface terminology is performed by mining high granularity concepts from EHRs, because such concepts are usually not present in the existing reference terminologies. We use the techniques of concatenation and anchoring iteratively to extract high granularity phrases from the clinical text. In addition to increasing the conceptual base of the COVID interface terminology, this will also help in generating training data for large scale concept mining using machine learning techniques. Having the annotated clinical notes of COVID-19 patients available will help in speeding up research in this field.",
keywords = "COVID-19 ontologies, COVID-19 patient EHRs, EHR annotation, concept mining, interface terminology",
author = "Keloth, {Vipina K.} and Shuxin Zhou and Luke Lindemann and Gai Elhanan and Einstein, {Andrew J.} and James Geller and Yehoshua Perl",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 8th IEEE International Conference on Big Data, Big Data 2020 ; Conference date: 10-12-2020 Through 13-12-2020",
year = "2020",
month = dec,
day = "10",
doi = "10.1109/BigData50022.2020.9377981",
language = "English (US)",
series = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3753--3760",
editor = "Xintao Wu and Chris Jermaine and Li Xiong and Hu, {Xiaohua Tony} and Olivera Kotevska and Siyuan Lu and Weijia Xu and Srinivas Aluru and Chengxiang Zhai and Eyhab Al-Masri and Zhiyuan Chen and Jeff Saltz",
booktitle = "Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020",
address = "United States",
}