3 research outputs found
Clinical Information Extraction via Convolutional Neural Network
We report an implementation of a clinical information extraction tool that
leverages deep neural network to annotate event spans and their attributes from
raw clinical notes and pathology reports. Our approach uses context words and
their part-of-speech tags and shape information as features. Then we hire
temporal (1D) convolutional neural network to learn hidden feature
representations. Finally, we use Multilayer Perceptron (MLP) to predict event
spans. The empirical evaluation demonstrates that our approach significantly
outperforms baselines.Comment: arXiv admin note: text overlap with arXiv:1408.5882 by other author
A Study of Recent Contributions on Information Extraction
This paper reports on modern approaches in Information Extraction (IE) and
its two main sub-tasks of Named Entity Recognition (NER) and Relation
Extraction (RE). Basic concepts and the most recent approaches in this area are
reviewed, which mainly include Machine Learning (ML) based approaches and the
more recent trend to Deep Learning (DL) based methods
Multiple Sclerosis Severity Classification From Clinical Text
Multiple Sclerosis (MS) is a chronic, inflammatory and degenerative
neurological disease, which is monitored by a specialist using the Expanded
Disability Status Scale (EDSS) and recorded in unstructured text in the form of
a neurology consult note. An EDSS measurement contains an overall "EDSS" score
and several functional subscores. Typically, expert knowledge is required to
interpret consult notes and generate these scores. Previous approaches used
limited context length Word2Vec embeddings and keyword searches to predict
scores given a consult note, but often failed when scores were not explicitly
stated. In this work, we present MS-BERT, the first publicly available
transformer model trained on real clinical data other than MIMIC. Next, we
present MSBC, a classifier that applies MS-BERT to generate embeddings and
predict EDSS and functional subscores. Lastly, we explore combining MSBC with
other models through the use of Snorkel to generate scores for unlabelled
consult notes. MSBC achieves state-of-the-art performance on all metrics and
prediction tasks and outperforms the models generated from the Snorkel
ensemble. We improve Macro-F1 by 0.12 (to 0.88) for predicting EDSS and on
average by 0.29 (to 0.63) for predicting functional subscores over previous
Word2Vec CNN and rule-based approaches.Comment: EMNLP 2020 Clinical NLP worksho