4,180 research outputs found
A Neural Attention Model for Categorizing Patient Safety Events
Medical errors are leading causes of death in the US and as such, prevention
of these errors is paramount to promoting health care. Patient Safety Event
reports are narratives describing potential adverse events to the patients and
are important in identifying and preventing medical errors. We present a neural
network architecture for identifying the type of safety events which is the
first step in understanding these narratives. Our proposed model is based on a
soft neural attention model to improve the effectiveness of encoding long
sequences. Empirical results on two large-scale real-world datasets of patient
safety reports demonstrate the effectiveness of our method with significant
improvements over existing methods.Comment: ECIR 201
Named Entity Recognition in Electronic Health Records Using Transfer Learning Bootstrapped Neural Networks
Neural networks (NNs) have become the state of the art in many machine
learning applications, especially in image and sound processing [1]. The same,
although to a lesser extent [2,3], could be said in natural language processing
(NLP) tasks, such as named entity recognition. However, the success of NNs
remains dependent on the availability of large labelled datasets, which is a
significant hurdle in many important applications. One such case are electronic
health records (EHRs), which are arguably the largest source of medical data,
most of which lies hidden in natural text [4,5]. Data access is difficult due
to data privacy concerns, and therefore annotated datasets are scarce. With
scarce data, NNs will likely not be able to extract this hidden information
with practical accuracy. In our study, we develop an approach that solves these
problems for named entity recognition, obtaining 94.6 F1 score in I2B2 2009
Medical Extraction Challenge [6], 4.3 above the architecture that won the
competition. Beyond the official I2B2 challenge, we further achieve 82.4 F1 on
extracting relationships between medical terms. To reach this state-of-the-art
accuracy, our approach applies transfer learning to leverage on datasets
annotated for other I2B2 tasks, and designs and trains embeddings that
specially benefit from such transfer.Comment: 11 pages, 4 figures, 8 table
Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective
This paper presents a Lisp architecture for a portable NLP system, termed
LAPNLP, for processing clinical notes. LAPNLP integrates multiple standard,
customized and in-house developed NLP tools. Our system facilitates portability
across different institutions and data systems by incorporating an enriched
Common Data Model (CDM) to standardize necessary data elements. It utilizes
UMLS to perform domain adaptation when integrating generic domain NLP tools. It
also features stand-off annotations that are specified by positional reference
to the original document. We built an interval tree based search engine to
efficiently query and retrieve the stand-off annotations by specifying
positional requirements. We also developed a utility to convert an inline
annotation format to stand-off annotations to enable the reuse of clinical text
datasets with inline annotations. We experimented with our system on several
NLP facilitated tasks including computational phenotyping for lymphoma patients
and semantic relation extraction for clinical notes. These experiments
showcased the broader applicability and utility of LAPNLP.Comment: 6 pages, accepted by IEEE BIBM 2018 as regular pape
Graph Convolutional Networks for Text Classification
Text classification is an important and classical problem in natural language
processing. There have been a number of studies that applied convolutional
neural networks (convolution on regular grid, e.g., sequence) to
classification. However, only a limited number of studies have explored the
more flexible graph convolutional neural networks (convolution on non-grid,
e.g., arbitrary graph) for the task. In this work, we propose to use graph
convolutional networks for text classification. We build a single text graph
for a corpus based on word co-occurrence and document word relations, then
learn a Text Graph Convolutional Network (Text GCN) for the corpus. Our Text
GCN is initialized with one-hot representation for word and document, it then
jointly learns the embeddings for both words and documents, as supervised by
the known class labels for documents. Our experimental results on multiple
benchmark datasets demonstrate that a vanilla Text GCN without any external
word embeddings or knowledge outperforms state-of-the-art methods for text
classification. On the other hand, Text GCN also learns predictive word and
document embeddings. In addition, experimental results show that the
improvement of Text GCN over state-of-the-art comparison methods become more
prominent as we lower the percentage of training data, suggesting the
robustness of Text GCN to less training data in text classification.Comment: Accepted by 33rd AAAI Conference on Artificial Intelligence (AAAI
2019
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