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: An algorithm combining machine learning and natural language processing to detect patients with colorectal cancer (CRC) from entire EHRs at Vanderbilt University Hospital is described.

TL;DR: Statistical NLP can accurately identify VTE from narrative radiology reports from patients with a suspected DVT/PE in Montreal between 2008 and 2012.

TL;DR: NLP can correctly identify screening colonoscopies, accurately identify adenomas and SSAs in a pathology database, and provide real-time quality metrics for colonoscopy.