Description
TNM stage is an important classification system for predicting prognosis and planning treatments in lung cancer, but not available in ICD-9/10 systems. We aimed to develop computable phenotype algorithms to classify the TNM cancer stages of lung cancer patients using common data elements, including diagnosis, medication and procedure codes. We discuss opportunities and challenges in developing computational phenotyping algorithm using treatment guidelines.
Learning Objective: 1. To apply the TNM stage-based U.S. National Comprehensive Cancer Network (NCCN) algorithm for treatments in lung cancer for a computational phenotyping algorithm.
2. To understand opportunities and challenges in developing computational phenotyping algorithm to classify lung cancer stages using common data elements.
Authors:
Min-hyung Kim (Presenter)
Harvard School of Public Health
Sojung Park, University of Ulsan College of Medicine
Yu Rang Park, Yonsei University College of Medicine
Wonjun Ji, University of Ulsan College of Medicine
Seul-gi Kim, University of Ulsan College of Medicine
Minji Choo, University of Ulsan College of Medicine
Seung-sik Hwang, Seoul National University Graduate School of Public Health
Jae Cheol Lee, University of Ulsan College of Medicine
Hyeong Ryul Kim, University of Ulsan College of Medicine
Chang-Min Choi, University of Ulsan College of Medicine
Presentation Materials: