Sijia Liu

TL;DR: There is a considerable gap between clinical studies using EHR data and studies using clinical IE, so a more concrete understanding of the gap is gained and potential solutions to bridge this gap are provided.

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: Li et al. as discussed by the authors proposed an adaptive random gradient estimation strategy to balance query counts and distortion, and an autoencoder that is either trained offline with unlabeled data or a bilinear resizing operation for attack acceleration.

TL;DR: A novel gradient-based attack method is presented that facilitates the difficulty of tackling discrete graph data and yields higher robustness against both different gradient based and greedy attack methods without sacrificing classification accuracy on original graph.

TL;DR: In this article, a clinical text classification paradigm using weak supervision and deep representation was proposed to reduce human efforts of labeled training data creation and feature engineering for applying machine learning to clinical text classi cation.