Negation Detection in Medical Texts

Negation detection refers to the automatic identification of linguistic expression that convey negation within a textual content. In medical and biomedical context, the negation detection plays a pivotal role in understanding clinical documentation and extracting meaningful insights. In this paper, we survey 16 articles published from 2005 to 2023 and focusing on negation detection within medical domain. Our evaluation framework encompass both methodological aspects and application-oriented considerations. Specifically, we discuss the used approaches, the employed methodology, the specific tasks addressed, the target language of textual analysis, and the evaluation metrics used. On the application front, for each reviewed study, we delineate the medical domains under investigation (e.g., cardiology, oncology), the types of data analyzed, and the availability of datasets. The majority of reviewed works are conducted in English, with a prevalence of machine learning and deep learning approaches, and classic classification evaluation metrics. Application domains exhibit heterogeneity, with a slight predominance in oncology, and diverse data sources including EHRs, abstracts, scientific papers, and web-derived information (e.g., Wikipedia or blog entries). Throughout this review, we will identify limitations and gaps in this research area, as well as examine the benefits it could bring to the scientific community and the methods currently employed.