• Publications
  • Influence
CLAMP – a toolkit for efficiently building customized clinical natural language processing pipelines
  • E. Soysal, J. Wang, +4 authors H. Xu
  • Computer Science, Medicine
  • J. Am. Medical Informatics Assoc.
  • 24 November 2017
TLDR
We present CLAMP (Clinical Language Annotation, Modeling, and Processing), a newly developed clinical NLP toolkit that provides not only state-of-the-art NLP components, but also a user-friendly graphic user interface that can help users quickly build customized NLP pipelines for their individual applications. Expand
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A hybrid system for temporal information extraction from clinical text
TLDR
We developed a comprehensive temporal information extraction system that can identify events, temporal expressions, and their temporal relations in clinical text. Expand
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Validating drug repurposing signals using electronic health records: a case study of metformin associated with reduced cancer mortality
TLDR
We evaluate the feasibility of using electronic health records (EHRs) and automated informatics methods to efficiently validate a recent drug repurposing association of metformin with reduced cancer mortality. Expand
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Research and applications: A comprehensive study of named entity recognition in Chinese clinical text
TLDR
We investigated the effects of different types of feature including bag-of-characters, word segmentation, part of speech, and section information, and different machine learning algorithms including conditional random fields (CRF), support vector machines (SVM), maximum entropy (ME), and structural SVM. Expand
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A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries
TLDR
We develop and evaluate machine-learning-based approaches to extracting clinical entities-including medical problems, tests, and treatments, as well as their asserted status-from hospital discharge summaries using natural language. Expand
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Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features
TLDR
In this study, we applied SSVMs to clinical entity recognition, and investigated the contribution of two different types of word representation features to this task. Expand
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Blue noise sampling using an SPH-based method
TLDR
We propose a novel algorithm for blue noise sampling inspired by the Smoothed Particle Hydrodynamics (SPH) method. Expand
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Optimal Components Selection for Analog Active Filters Using Clonal Selection Algorithms
TLDR
Clonal Selection Algorithms (CSA) is a widely used approach for handling optimization problems. Expand
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Integration of Global and Local Metrics for Domain Adaptation Learning Via Dimensionality Reduction
TLDR
In this paper, we present a kernelized local-global approach to solve domain adaptation problems via the kernel method. Expand
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A Study of Neural Word Embeddings for Named Entity Recognition in Clinical Text
TLDR
We show that the distributed word embedding features derived from a large unlabeled clinical corpus can be better than the widely used Brown clusters for clinical NER. Expand
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