AIMC Topic: Risk Assessment

Clear Filters Showing 2281 to 2290 of 2930 articles

A context-aware approach for progression tracking of medical concepts in electronic medical records.

Journal of biomedical informatics
Electronic medical records (EMRs) for diabetic patients contain information about heart disease risk factors such as high blood pressure, cholesterol levels, and smoking status. Discovering the described risk factors and tracking their progression ov...

Myocardial Fractional Flow Reserve Measurement Using Contrast Media as a First-Line Assessment of Coronary Lesions in Current Practice.

The Canadian journal of cardiology
BACKGROUND: Fractional flow reserve (FFR) measurement requires adenosine injection. However, adenosine can induce conductive and rhythmic complications, or be contraindicated in some patients. Contrast-induced hyperemia could provide a simple first-l...

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...

Finding Risk Groups by Optimizing Artificial Neural Networks on the Area under the Survival Curve Using Genetic Algorithms.

PloS one
We investigate a new method to place patients into risk groups in censored survival data. Properties such as median survival time, and end survival rate, are implicitly improved by optimizing the area under the survival curve. Artificial neural netwo...

Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences.

Artificial intelligence in medicine
OBJECTIVES: Inspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model,...

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,...

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...