Predicting COPD Failure by Modeling Hazard in Longitudinal Clinical Data

• Main Authors: Jianfei Zhang, Shengrui Wang, Courteau, Josiane, Lifei Chen, Bach, Aurelien, Vanasse, Alain
• Format: Conference Proceeding
• Language: English
• Subjects: Analytical models; Clinical Data; Data models; Failure Prediction; Hazards; Hospitals; Indexes; Learning; Mathematical model; Predictive models; Regression Model; Survival Analysis
• ISSN: 2374-8486
• DOI: 10.1109/ICDM.2016.0075

Chronic obstructive pulmonary disease (COPD) accounts for the highest rate of hospital readmissions and is the third leading cause of death in Canada, the United States and worldwide. Predicting COPD failure provides a prognostic warning of death or readmission, and is crucial to early intervention and decision-making. The aim of this study is to perform COPD failure prediction on longitudinal data. To address the inappropriate estimation of Cox hazard in current approaches, we propose a new representation of hazard to capture the relationship between survival probability and time-varying risk factors in a concise but effective way. To optimize model parameters, we design and maximize a new joint likelihood that comprises two components used to estimate survival status separately for failure and censored patients. A regularized optimization is performed on the joint likelihood to prevent overfitting arising from model learning. Our approach is applied to a real-life COPD data set and outperforms the current state-of-the-art prediction models in terms of the survival AUC, concordance index and Birer score metrics, this reveals that the great promise of our approach for clinical prediction.