AIMC Topic: Electronic Health Records

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A Framework for Identifying Genotypic Information from Clinical Records: Exploiting Integrated Ontology Structures to Transfer Annotations between ICD Codes and Gene Ontologies.

IEEE/ACM transactions on computational biology and bioinformatics
Although some methods are proposed for automatic ontology generation, none of them address the issue of integrating large-scale heterogeneous biomedical ontologies. We propose a novel approach for integrating various types of ontologies efficiently a...

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...

Accessing complex patient data from Arden Syntax Medical Logic Modules.

Artificial intelligence in medicine
OBJECTIVE: Arden Syntax is a standard for representing and sharing medical knowledge in form of independent modules and looks back on a history of 25 years. Its traditional field of application is the monitoring of clinical events such as generating ...

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...

Coronary artery disease risk assessment from unstructured electronic health records using text mining.

Journal of biomedical informatics
Coronary artery disease (CAD) often leads to myocardial infarction, which may be fatal. Risk factors can be used to predict CAD, which may subsequently lead to prevention or early intervention. Patient data such as co-morbidities, medication history,...

Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus.

Journal of biomedical informatics
The 2014 i2b2/UTHealth natural language processing shared task featured a track focused on the de-identification of longitudinal medical records. For this track, we de-identified a set of 1304 longitudinal medical records describing 296 patients. Thi...

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...