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: TextHunter is presented, a tool for the creation of training data, construction of concept extraction machine learning models and their application to documents and achieved recall measurements as high as 99% in real world use cases.

TL;DR: The CLEF project as mentioned in this paper is an MRC sponsored project in the E-Science programme that aims to establish methodologies and a technical infrastructure for the next generation of integrated clinical and bioscience research.

TL;DR: The results show that rule-based NLP approaches for specific information extraction tasks are portable across institutions; however customization of the algorithm on the new dataset improves its performance.

TL;DR: A hybrid NLP and machine learning automated classification system shows promise in coding free-text electronic clinical data and is similar to, or better than, that of medical personnel in previous studies.