Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department
Louise Deleger, Holly Brodzinski, Haijun Zhai, Qi Li, Todd Lingren, Eric S Kirkendall, Evaline Alessandrini, Imre Solti
J Am Med Inform Assoc Published Online First: 15 October 2013 doi:10.1136/amiajnl-2013-001962
Imre Solti
Cincinnati Children’s Hospital Medical Center
We analyzed the EHRs of a random sample of 2100 pediatric emergency department (ED) patients with abdominal pain, including all with a final diagnosis of appendicitis. We developed an automated system to extract relevant elements from ED physician notes and lab values and to automatically assign a risk category for acute appendicitis (high, equivocal, or low), based on the Pediatric Appendicitis Score. We evaluated the performance of the system against a manually created gold standard (chart reviews by ED physicians) for recall, specificity, and precision.
Speaker’s Biography:
Dr. Solti is an Assistant Professor on the tenure track at the Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center. He has a medical degree (Albert Szent-Gyorgyi Medical School, Szeged), a Ph.D. in Health Services Organization and Research (Medical College of Virginia, Richmond) and a Master of Arts in Computational Linguistics (University of Washington, Seattle). He is the recipient of the NLM's K99/R00 Career Development Award. His team is using natural language processing and machine learning algorithms for electronic health record text and data mining, and predictive modeling to facilitate health care quality improvement, patient safety and clinical research (https://research.cchmc.org/solti/).
« Hide
