70,520 research outputs found
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
Classification of Radiology Reports Using Neural Attention Models
The electronic health record (EHR) contains a large amount of
multi-dimensional and unstructured clinical data of significant operational and
research value. Distinguished from previous studies, our approach embraces a
double-annotated dataset and strays away from obscure "black-box" models to
comprehensive deep learning models. In this paper, we present a novel neural
attention mechanism that not only classifies clinically important findings.
Specifically, convolutional neural networks (CNN) with attention analysis are
used to classify radiology head computed tomography reports based on five
categories that radiologists would account for in assessing acute and
communicable findings in daily practice. The experiments show that our CNN
attention models outperform non-neural models, especially when trained on a
larger dataset. Our attention analysis demonstrates the intuition behind the
classifier's decision by generating a heatmap that highlights attended terms
used by the CNN model; this is valuable when potential downstream medical
decisions are to be performed by human experts or the classifier information is
to be used in cohort construction such as for epidemiological studies
Spanish named entity recognition in the biomedical domain
Named Entity Recognition in the clinical domain and in languages different from English has the difficulty of the absence of complete dictionaries, the informality of texts, the polysemy of terms, the lack of accordance in the boundaries of an entity, the scarcity of corpora and of other resources available. We present a Named Entity Recognition method for poorly resourced languages. The method was tested with Spanish radiology reports and compared with a conditional random fields system.Peer ReviewedPostprint (author's final draft
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