Abstract
Background/Aims Pneumonia is common and can be devastating in older adults. Health plan data hold promise for studying pneumonia, but ICD-9 codes have poor accuracy for this condition. Natural language processing (NLP) offers potential to accurately and efficiently identify pneumonia from electronic medical records (EMRs). Our aims were to train one NLP tool to identify pneumonia from electronic radiology reports and to assess its validity compared to manual review.
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