AIMC Topic: Confidentiality

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

Journal of biomedical informatics Jul 28, 2015
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...
Confidentiality Data Mining Pattern Recognition, Automated Computer Security Vocabulary, Controlled Electronic Health Records Cohort Studies Longitudinal Studies Narration Natural Language Processing
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Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2.

Journal of biomedical informatics Jul 22, 2015
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...
Pattern Recognition, Automated Computer Security Vocabulary, Controlled Diabetes Complications Boston Narration Natural Language Processing Coronary Artery Disease Confidentiality Data Mining Humans Electronic Health Records Female Aged Male Middle Aged Risk Assessment Comorbidity Incidence Cohort Studies Longitudinal Studies
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Combining knowledge- and data-driven methods for de-identification of clinical narratives.

Journal of biomedical informatics Jul 22, 2015
A recent promise to access unstructured clinical data from electronic health records on large-scale has revitalized the interest in automated de-identification of clinical notes, which includes the identification of mentions of Protected Health Infor...
Machine Learning Natural Language Processing Confidentiality Data Mining United Kingdom Pattern Recognition, Automated Computer Security Computer Simulation Electronic Health Records Cohort Studies Models, Statistical Narration Vocabulary, Controlled
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Agile text mining for the 2014 i2b2/UTHealth Cardiac risk factors challenge.

Journal of biomedical informatics Jul 22, 2015
This paper describes the use of an agile text mining platform (Linguamatics' Interactive Information Extraction Platform, I2E) to extract document-level cardiac risk factors in patient records as defined in the i2b2/UTHealth 2014 challenge. The appro...
Cohort Studies Longitudinal Studies Narration Natural Language Processing Confidentiality Data Mining Humans Male United Kingdom Electronic Health Records Cardiovascular Diseases Pattern Recognition, Automated Female Middle Aged Computer Security Aged Risk Assessment Vocabulary, Controlled Comorbidity Diabetes Complications Incidence
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Combining glass box and black box evaluations in the identification of heart disease risk factors and their temporal relations from clinical records.

Journal of biomedical informatics Jul 2, 2015
BACKGROUND: The determination of risk factors and their temporal relations in natural language patient records is a complex task which has been addressed in the i2b2/UTHealth 2014 shared task. In this context, in most systems it was broadly decompose...
Male Cardiovascular Diseases Middle Aged Risk Assessment Comorbidity Incidence Humans Algorithms Electronic Health Records Female Aged Data Mining Pattern Recognition, Automated France Cohort Studies Computer Security Longitudinal Studies Vocabulary, Controlled Narration Diabetes Complications Natural Language Processing Confidentiality
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Using local lexicalized rules to identify heart disease risk factors in clinical notes.

Journal of biomedical informatics Jun 29, 2015
Heart disease is the leading cause of death globally and a significant part of the human population lives with it. A number of risk factors have been recognized as contributing to the disease, including obesity, coronary artery disease (CAD), hyperte...
Semantics Humans Pattern Recognition, Automated Narration Middle Aged Computer Security Natural Language Processing Risk Assessment Vocabulary, Controlled Confidentiality Comorbidity Diabetes Complications Electronic Health Records Data Mining Incidence United Kingdom Female Cohort Studies Longitudinal Studies Aged Male Cardiovascular Diseases
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The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs.

Journal of biomedical informatics Jun 26, 2015
This paper describes a supervised machine learning approach for identifying heart disease risk factors in clinical text, and assessing the impact of annotation granularity and quality on the system's ability to recognize these risk factors. We utiliz...
Humans Electronic Health Records Female Aged Male Middle Aged Risk Assessment Comorbidity Incidence Data Mining Cohort Studies Longitudinal Studies Pattern Recognition, Automated Narration Computer Security Natural Language Processing Vocabulary, Controlled Coronary Artery Disease Diabetes Complications Confidentiality Maryland Supervised Machine Learning
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Automatic de-identification of electronic medical records using token-level and character-level conditional random fields.

Journal of biomedical informatics Jun 26, 2015
De-identification, identifying and removing all protected health information (PHI) present in clinical data including electronic medical records (EMRs), is a critical step in making clinical data publicly available. The 2014 i2b2 (Center of Informati...
Electronic Health Records China Cohort Studies Data Interpretation, Statistical Narration Confidentiality Natural Language Processing Data Mining Pattern Recognition, Automated Computer Security Vocabulary, Controlled
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Annotating risk factors for heart disease in clinical narratives for diabetic patients.

Journal of biomedical informatics May 21, 2015
The 2014 i2b2/UTHealth natural language processing shared task featured a track focused on identifying risk factors for heart disease (specifically, Cardiac Artery Disease) in clinical narratives. For this track, we used a "light" annotation paradigm...
Pattern Recognition, Automated Computer Security Vocabulary, Controlled Diabetes Complications Boston Female Coronary Artery Disease Aged Confidentiality Male Data Mining Comorbidity Middle Aged Humans Documentation Risk Assessment Incidence Electronic Health Records New York Cohort Studies Longitudinal Studies Narration Natural Language Processing
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Evaluation of PHI Hunter in Natural Language Processing Research.

Perspectives in health information management Jan 1, 2015
OBJECTIVES: We introduce and evaluate a new, easily accessible tool using a common statistical analysis and business analytics software suite, SAS, which can be programmed to remove specific protected health information (PHI) from a text document. Re...
Natural Language Processing Humans Confidentiality Software United States Department of Veterans Affairs Electronic Health Records Biomedical Research United States
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