American Statistical Association
Predicting time-to-event from longitudinal data where different events occur at different time points is an extremely important problem in several domains such as healthcare, economics, social networks and seismology, to name a few. A unique challenge in this problem involves building predictive models from right censored data. This is a phenomenon where instances whose event of interest are not yet observed within a given observation time window and are considered to be right censored. Effective models for predicting time-to-event labels from such right censored data with good accuracy can have a significant impact in these domains. However, existing methods in the literature cannot capture various complexities present in real-world survival data such as feature groups and intra and inter-event correlations. To address such challenges, we briefly summarize the major contributions of the methods proposed here as (i) modeling intra-event correlations in survival data using structured sparsity-based regularizers, (ii) learning novel representations for survival data by inferring inter-event and intra-event correlations, (iii) extending linear regression-based methods to learn predictive models from right censored data and (iv) identifying censored instances and events from the data which are contributing extensively to learning a model with lesser number of training instances using active learning. Our methods are tested on different real-world longitudinal datasets such as electronic health records (EHRs), crowdfunding data, gene-expression data and several publicly available synthetic survival datasets. The results demonstrate the goodness of these methods when compared to state-of-the-art survival analysis, classification and regression methods from the literature.
Bhanu Vinzamuri is a postdoctoral scientist at IBM Research AI, Yorktown Heights, NY. His primary research interests are machine learning, biostatistics and healthcare analytics. He has exhaustive experience in developing semi-supervised, transfer learning and causal inference-based algorithms for real-world problems in the healthcare domain. He has worked for reputed healthcare organizations in the U.S. such as Mayo Clinic in Rochester, Minn., and Nationwide Children's Research Institute in Columbus, Ohio. He received his PhD in computer science from Wayne State University and his MS in computer science from IIIT Hyderabad.