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Chapman AB, Mowery DL, Swords DS, Chapman WW, Bucher BT. Detecting Evidence of Intra-abdominal Surgical Site Infections from Radiology Reports Using Natural Language Processing. AMIA ... Annual Symposium proceedings. AMIA Symposium. 2017 Jan 1; 2017:515-524.
Dimensions for VA is a web-based tool available to VA staff that enables detailed searches of published research and research projects. Free-text reports in electronic health records (EHRs) contain medically significant information - signs, symptoms, findings, diagnoses - recorded by clinicians during patient encounters. These reports contain rich clinical information which can be leveraged for surveillance of disease and occurrence of adverse events. In order to gain meaningful knowledge from these text reports to support surveillance efforts, information must first be converted into a structured, computable format. Traditional methods rely on manual review of charts, which can be costly and inefficient. Natural language processing (NLP) methods offer an efficient, alternative approach to extracting the information and can achieve a similar level of accuracy. We developed an NLP system to automatically identify mentions of surgical site infections in radiology reports and classify reports containing evidence of surgical site infections leveraging these mentions. We evaluated our system using a reference standard of reports annotated by domain experts, administrative data generated for each patient encounter, and a machine learning-based approach.
