Autonomous detection, grading, and reporting of postoperative complications using natural language processing.

Introduction: Natural language processing, a computer science technique that allows interpretation of narrative text, is infrequently used to identify surgical complications. We designed a natural language processing algorithm to identify and grade the severity of deep venous thrombosis and pulmonary embolism (together: venous thromboembolism).

Methods: Patients from our 2011-2014 American College of Surgeons National Surgical Quality Improvement Project cohorts with a duplex ultrasound or a computerized tomography angiography of the chest performed within 30 days of surgery were divided into training and validation datasets. A "bag of words" approach classified the reports; other electronic health record data classified the venous thromboembolism's severity.

Results: Of the 10,295 American College of Surgeons National Surgical Quality Improvement Project patients, 251 were used in our deep venous thromboses validation cohort (273 total ultrasounds) and 506 in our pulmonary embolisms cohort (552 total computerized tomography angiographies). For deep venous thromboses the sensitivity and specificity were 85.1% and 94.6%, while for pulmonary embolisms they were 90% and 98.7%. Most discordances were due to lack of imaging documentation of a deep venous thrombosis (28/41, 68.3%) or pulmonary embolism (6/6, 100%). Most deep venous thromboses (28 patients, 54.6%) and pulmonary embolisms (25 patients, 75.8%) required administration of therapeutic intravenous or subcutaneous anticoagulation.

Conclusion: Natural language processing can reliably detect the presence of postoperative venous thromboembolisms, and its use should be expanded for the detection of other conditions from narrative documentation.