The use of deep neural models for diagnosis prediction from clinical text has shown promising results.
Outcome prediction from clinical text can prevent doctors from overlooking possible risks and help hospitals to plan capacities.
Ranked #1 on Medical Diagnosis on Clinical Admission Notes from MIMIC-III (using extra training data)
At the same time, they are difficult to incorporate into the large, black-box models that achieve state-of-the-art results in a multitude of NLP tasks.
Our model leverages a dual encoder architecture with hierarchical LSTM layers and multi-task training to encode the position of clinical entities and aspects alongside the document discourse.
In order to better understand BERT and other Transformer-based models, we present a layer-wise analysis of BERT's hidden states.
From our extensive evaluation of 20 architectures, we report a highest score of 71. 6% F1 for the segmentation and classification of 30 topics from the English city domain, scored by our SECTOR LSTM model with bloom filter embeddings and bidirectional segmentation.
We present a new methodology for high-quality labeling in the fashion domain with crowd workers instead of experts.
We present a novel architecture, In-Database Entity Linking (IDEL), in which we integrate the analytics-optimized RDBMS MonetDB with neural text mining abilities.
We report results on benchmarking Open Information Extraction (OIE) systems using RelVis, a toolkit for benchmarking Open Information Extraction systems.