AIMC Topic: Natural Language Processing

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Can Natural Language Processing Improve the Efficiency of Vaccine Adverse Event Report Review?

Methods of information in medicine
BACKGROUND: Individual case review of spontaneous adverse event (AE) reports remains a cornerstone of medical product safety surveillance for industry and regulators. Previously we developed the Vaccine Adverse Event Text Miner (VaeTM) to offer autom...

A study of active learning methods for named entity recognition in clinical text.

Journal of biomedical informatics
OBJECTIVES: Named entity recognition (NER), a sequential labeling task, is one of the fundamental tasks for building clinical natural language processing (NLP) systems. Machine learning (ML) based approaches can achieve good performance, but they oft...

An Unsupervised Graph Based Continuous Word Representation Method for Biomedical Text Mining.

IEEE/ACM transactions on computational biology and bioinformatics
In biomedical text mining tasks, distributed word representation has succeeded in capturing semantic regularities, but most of them are shallow-window based models, which are not sufficient for expressing the meaning of words. To represent words usin...

Feasibility of 30-day hospital readmission prediction modeling based on health information exchange data.

International journal of medical informatics
INTRODUCTION: Unplanned 30-day hospital readmission account for roughly $17 billion in annual Medicare spending. Many factors contribute to unplanned hospital readmissions and multiple models have been developed over the years to predict them. Most r...

Textual inference for eligibility criteria resolution in clinical trials.

Journal of biomedical informatics
Clinical trials are essential for determining whether new interventions are effective. In order to determine the eligibility of patients to enroll into these trials, clinical trial coordinators often perform a manual review of clinical notes in the e...

Exploiting Language Models to Classify Events from Twitter.

Computational intelligence and neuroscience
Classifying events is challenging in Twitter because tweets texts have a large amount of temporal data with a lot of noise and various kinds of topics. In this paper, we propose a method to classify events from Twitter. We firstly find the distinguis...

Comparison of UMLS terminologies to identify risk of heart disease using clinical notes.

Journal of biomedical informatics
The second track of the 2014 i2b2 challenge asked participants to automatically identify risk factors for heart disease among diabetic patients using natural language processing techniques for clinical notes. This paper describes a rule-based system ...

A hybrid model for automatic identification of risk factors for heart disease.

Journal of biomedical informatics
Coronary artery disease (CAD) is the leading cause of death in both the UK and worldwide. The detection of related risk factors and tracking their progress over time is of great importance for early prevention and treatment of CAD. This paper describ...

An automatic system to identify heart disease risk factors in clinical texts over time.

Journal of biomedical informatics
Despite recent progress in prediction and prevention, heart disease remains a leading cause of death. One preliminary step in heart disease prediction and prevention is risk factor identification. Many studies have been proposed to identify risk fact...

Extracting Biomedical Event with Dual Decomposition Integrating Word Embeddings.

IEEE/ACM transactions on computational biology and bioinformatics
Extracting biomedical event from literatures has attracted much attention recently. By now, most of the state-of-the-art systems have been based on pipelines which suffer from cascading errors, and the words encoded by one-hot are unable to represent...