electronic health record | Digital Health

Liu J, Capurro D, Nguyen A, Verspoor K. “Note Bloat” impacts deep learning-based NLP models for clinical prediction tasks. Journal of biomedical informatics. 2022 Sep 1;133:104149. https://doi.org/10.1016/j.jbi.2022.104149

“Note Bloat” impacts deep learning-based NLP models for clinical prediction tasks One unintended consequence of the Electronic Health Records (EHR) implementation is the overuse of content-importing technology, such as copy-and-paste, that creates “bloated” notes containing large amounts of textual redundancy. Despite the rising interest in applying machine learning models to learn from real-patient data, it

Lederman A, Lederman R, Verspoor K. Tasks as needs: reframing the paradigm of clinical natural language processing research for real-world decision support, Journal of the American Medical Informatics Association, Volume 29, Issue 10, October 2022, Pages 1810–1817, https://doi.org/10.1093/jamia/ocac121

Tasks as needs: reframing the paradigm of clinical natural language processing research for real-world decision support. Electronic medical records are increasingly used to store patient information in hospitals and other clinical settings. There has been a corresponding proliferation of clinical natural language processing (cNLP) systems aimed at using text data in these records to improve clinical

Akhtyamova L, Martínez P, Verspoor K, Cardiff J. (2020) Testing Contextualized Word Embeddings to Improve NER in Spanish Clinical Case Narratives. IEEE Access.

In the Big Data era, there is an increasing need to fully exploit and analyze the huge quantity of information available about health. Natural Language Processing (NLP) technologies can contribute by extracting relevant information from unstructured data contained in Electronic Health Records (EHR) such as clinical notes, patients’ discharge summaries and radiology reports. The extracted

Kocaballi AB, Coiera E, Tong HL, White S, Quiroz JC, Rezazadegan F, Willcock S, Laranjo L. A network model of activities in primary care consultations. Journal of the American Medical Informatics Association. 2019; 26(10):1074-82.

Objective The objective of this study is to characterize the dynamic structure of primary care consultations by identifying typical activities and their inter-relationships to inform the design of automated approaches to clinical documentation using natural language processing and summarization methods. Materials and Methods This is an observational study in Australian general practice involving 31 consultations