AIMC Journal:
Journal of biomedical informatics

Showing 591 to 600 of 657 articles

Adapting existing natural language processing resources for cardiovascular risk factors identification in clinical notes.

Journal of biomedical informatics
The 2014 i2b2 natural language processing shared task focused on identifying cardiovascular risk factors such as high blood pressure, high cholesterol levels, obesity and smoking status among other factors found in health records of diabetic patients...

CRFs based de-identification of medical records.

Journal of biomedical informatics
De-identification is a shared task of the 2014 i2b2/UTHealth challenge. The purpose of this task is to remove protected health information (PHI) from medical records. In this paper, we propose a novel de-identifier, WI-deId, based on conditional rand...

Mining heart disease risk factors in clinical text with named entity recognition and distributional semantic models.

Journal of biomedical informatics
We present the design, and analyze the performance of a multi-stage natural language processing system employing named entity recognition, Bayesian statistics, and rule logic to identify and characterize heart disease risk factor events in diabetic p...

Risk factor detection for heart disease by applying text analytics in electronic medical records.

Journal of biomedical informatics
In the United States, about 600,000 people die of heart disease every year. The annual cost of care services, medications, and lost productivity reportedly exceeds 108.9 billion dollars. Effective disease risk assessment is critical to prevention, ca...

A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases.

Journal of biomedical informatics
Automated phenotype identification plays a critical role in cohort selection and bioinformatics data mining. Natural Language Processing (NLP)-informed classification techniques can robustly identify phenotypes in unstructured medical notes. In this ...

Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1.

Journal of biomedical informatics
The 2014 i2b2/UTHealth Natural Language Processing (NLP) shared task featured four tracks. The first of these was the de-identification track focused on identifying protected health information (PHI) in longitudinal clinical narratives. The longitudi...

Using distant supervised learning to identify protein subcellular localizations from full-text scientific articles.

Journal of biomedical informatics
Databases of curated biomedical knowledge, such as the protein-locations reflected in the UniProtKB database, provide an accurate and useful resource to researchers and decision makers. Our goal is to augment the manual efforts currently used to cura...

Combining expert knowledge and knowledge automatically acquired from electronic data sources for continued ontology evaluation and improvement.

Journal of biomedical informatics
INTRODUCTION: A common bottleneck during ontology evaluation is knowledge acquisition from domain experts for gold standard creation. This paper contributes a novel semi-automated method for evaluating the concept coverage and accuracy of biomedical ...

Using Linked Data for polarity classification of patients' experiences.

Journal of biomedical informatics
Polarity classification is the main subtask of sentiment analysis and opinion mining, well-known problems in natural language processing that have attracted increasing attention in recent years. Existing approaches mainly rely on the subjective part ...

Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2.

Journal of biomedical informatics
The second track of the 2014 i2b2/UTHealth natural language processing shared task focused on identifying medical risk factors related to Coronary Artery Disease (CAD) in the narratives of longitudinal medical records of diabetic patients. The risk f...