A corpus-based approach for automated LOINC mapping.

Objective: To determine whether the knowledge contained in a rich corpus of local terms mapped to LOINC (Logical Observation Identifiers Names and Codes) could be leveraged to help map local terms from other institutions.

Methods: We developed two models to test our hypothesis. The first based on supervised machine learning was created using Apache's OpenNLP Maxent and the second based on information retrieval was created using Apache's Lucene.

Practical challenges in the secondary use of real-world data: the notifiable condition detector.

The interoperability specifications for electronic laboratory reporting specify the use of HL7, LOINC, SNOMED CT and UCUM. We explored the degree to which health care transactions comply with these standards by evaluating laboratory data captured in a health information exchange to support automated detection of public health notifiable diseases. We studied the NCD's ability to detect and report Lead, Influenza and MRSA.