Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classification.

Objective: Automated clinical phenotyping is challenging because word-based features quickly turn it into a high-dimensional problem, in which the small, privacy-restricted, training datasets might lead to overfitting. Pretrained embeddings might solve this issue by reusing input representation schemes trained on a larger dataset. We sought to evaluate shallow and deep learning text classifiers and the impact of pretrained embeddings in a small clinical dataset.

Building an Experimental German User Interface Terminology Linked to SNOMED CT.

We describe the process of creating a User Interface Terminology (UIT) with the goal to generate a maximum of German language interface terms that are mapped to the reference terminology SNOMED CT. The purpose is to offer a high coverage of medical jargon in order to optimise semantic annotations of clinical documents by text mining systems. The first step consisted in the creation of an n-gram table to which words and short phrases from the English SNOMED CT description table were automatically extracted and entered.

Analysis of Historical Medical Phenomena Using Large N-Gram Corpora.

Historically, numerous indirect references to real world phenomena have been conserved in literature. High-quality libraries of digitized books and their derivatives (like the Google NGram Viewer) have proliferated. These tools simplify the visualization of trends in phrase usage within the collective memory of language groups.