AIMC Topic: Electronic Health Records

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Functional evaluation of out-of-the-box text-mining tools for data-mining tasks.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The trade-off between the speed and simplicity of dictionary-based term recognition and the richer linguistic information provided by more advanced natural language processing (NLP) is an area of active discussion in clinical informatics. ...

Comparison of a semi-automatic annotation tool and a natural language processing application for the generation of clinical statement entries.

Journal of the American Medical Informatics Association : JAMIA
BACKGROUND AND OBJECTIVE: Electronic medical records with encoded entries should enhance the semantic interoperability of document exchange. However, it remains a challenge to encode the narrative concept and to transform the coded concepts into a st...

A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data.

Journal of the American Medical Informatics Association : JAMIA
BACKGROUND: Venous thromboembolisms (VTEs), which include deep vein thrombosis (DVT) and pulmonary embolism (PE), are associated with significant mortality, morbidity, and cost in hospitalized patients. To evaluate the success of preventive measures,...

Quantifying care coordination using natural language processing and domain-specific ontology.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This research identifies specific care coordination activities used by Aging in Place (AIP) nurse care coordinators and home healthcare (HHC) nurses when coordinating care for older community-dwelling adults and suggests a method to quanti...

Prediction of hospitalization due to heart diseases by supervised learning methods.

International journal of medical informatics
BACKGROUND: In 2008, the United States spent $2.2 trillion for healthcare, which was 15.5% of its GDP. 31% of this expenditure is attributed to hospital care. Evidently, even modest reductions in hospital care costs matter. A 2009 study showed that n...

Support Vector Feature Selection for Early Detection of Anastomosis Leakage From Bag-of-Words in Electronic Health Records.

IEEE journal of biomedical and health informatics
The free text in electronic health records (EHRs) conveys a huge amount of clinical information about health state and patient history. Despite a rapidly growing literature on the use of machine learning techniques for extracting this information, li...

Evaluating the state of the art in disorder recognition and normalization of the clinical narrative.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The ShARe/CLEF eHealth 2013 Evaluation Lab Task 1 was organized to evaluate the state of the art on the clinical text in (i) disorder mention identification/recognition based on Unified Medical Language System (UMLS) definition (Task 1a) a...

A machine learning approach to create blocking criteria for record linkage.

Health care management science
Record linkage, a part of data cleaning, is recognized as one of most expensive steps in data warehousing. Most record linkage (RL) systems employ a strategy of using blocking filters to reduce the number of pairs to be matched. A blocking filter con...

Electronic health record-enhanced signal detection using tree-based scan statistic methods.

American journal of epidemiology
Tree-based scan statistics (TBSS) are data mining methods that screen thousands of hierarchically related health outcomes to detect unsuspected adverse drug effects. TBSS traditionally analyze claims data with outcomes defined via diagnosis codes. TB...

Comparative effectiveness of anti-seizure medications in emulated trials using medical informatics.

Brain : a journal of neurology
Anti-seizure medications (ASMs) are often prescribed using a trial-and-error approach with a similar sequence for many patients. Comparative effectiveness data beyond the first ASM prescription are limited. Artificial intelligence can automatically e...