Clinical information extraction applications: A literature review.

TL;DR: The qualitative evaluation shows that the word embeddings trained from EHR and MedLit can find more similar medical terms than those trained from GloVe and Google News, and the intrinsic quantitative evaluation verifies that the semantic similarity captured by the wordEmbedded is closer to human experts' judgments on all four tested datasets.

TL;DR: Future NLP studies should concentrate on the investigation of symptoms and symptom documentation in EHR free-text narratives, and efforts should be undertaken to examine patient characteristics and make symptom-related NLP algorithms or pipelines and vocabularies openly available.

TL;DR: In this article, the authors present a review of the use of machine learning methods compared to rule-based approaches in clinical NLP, showing that the majority of works focus on classification of disease phenotype with only a handful of papers addressing extraction of comorbidities from free text or integration of clinical notes with structured data.

TL;DR: A comprehensive overview of the development and uptake of NLP methods applied to free-text clinical notes related to chronic diseases is provided, including the investigation of challenges faced by NLP methodologies in understanding clinical narratives.

TL;DR: The system was able to extract most physician-asserted drug side effects and can be trained to identify sentences with complex side effect descriptions that can be submitted to a human expert for further abstraction.

TL;DR: UIMA Ruta is compared to related rule-based systems especially concerning the compactness of the rule representation, the expressiveness, and the provided tooling support and the competitiveness of the runtime performance is shown.

TL;DR: A system developed for the i2b2 obesity challenge, whose aim was to automatically identify the status of obesity and 15 related co-morbidities in patients using their clinical discharge summaries, achieved performance in line with the agreement between human annotators, indicating the potential of text mining for accurate and efficient prediction of disease statuses.

TL;DR: The findings highlight the potential of NLP tools for rapid and efficient ascertainment of PAD cases from electronic health records to facilitate clinical investigation and eventually improve care by clinical decision support.