A Review of Existing Applications and Techniques for Narrative Text Analysis in Electronic Medical Records

Pomares-Quimbaya, Alexandra; González, Rafael A.; Quintero, Santiago; Velandía, Oscar Mauricio Muñoz; Bohórquez, Wilson Ricardo; García, Olga Milena; Londoño, Darı́o

doi:10.4018/978-1-5225-1759-7.ch064

Cited by 1 publication

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“…Digital means of medical informatics, especially when applying natural language processing (NLP), are indispensable in the application of e-health and digitalization of medical records and processes [1]. The use of NLP has proved as a lower-cost alternative to traditional medical methods in many cases such as to forecast stress symptoms and suicide risk in free-text responses sent via a mobile phone [2], or to detect seasonal disease outbreaks by monitoring search engine queries [3], and discovery of healthcare knowledge from social media [4, 5].…”

Section: Introductionmentioning

confidence: 99%

Minimalistic Approach to Coreference Resolution in Lithuanian Medical Records

Žitkus

Butkienė

Butleris

et al. 2019

Computational and Mathematical Methods in Medicine

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Coreference resolution is a challenging part of natural language processing (NLP) with applications in machine translation, semantic search and other information retrieval, and decision support systems. Coreference resolution requires linguistic preprocessing and rich language resources for automatically identifying and resolving such expressions. Many rarer and under-resourced languages (such as Lithuanian) lack the required language resources and tools. We present a method for coreference resolution in Lithuanian language and its application for processing e-health records from a hospital reception. Our novelty is the ability to process coreferences with minimal linguistic resources, which is important in linguistic applications for rare and endangered languages. The experimental results show that coreference resolution is applicable to the development of NLP-powered online healthcare services in Lithuania.

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