New Machine Learning Approaches for Modeling Time-to-Event Data
National Science Foundation Award # 1527827
Project Duration: 09/01/15-08/31/18
Due to the advancements in recent data collection technologies, different disciplines have attained the ability to not only accumulate a wide variety of data but also to monitor observations over longer periods of time. In many real-world applications, the primary goal of monitoring these observations is to better estimate the time for a particular event of interest to occur. Examples of these events include disease recurrence in healthcare, time to default in finance, device failure in engineering, etc. A major challenge with such time-to-event data is that it is often incomplete; some data instances are either removed or become unobservable over a period of time before the event occurs. Due to this missing piece of information, standard statistical and machine learning tools cannot readily be applied to analyze such data. Survival analysis methods, primarily developed by the statistics community, aim to model time-to-event data and are usually more effective compared to the standard prediction algorithms as they directly model the probability of occurrence of an event in contrast to assigning a nominal label to the data instance. More importantly, they can implicitly handle missing data. However, in many practical scenarios, the missing data challenges are compounded by several other related complexities such as the presence of correlations within the data, temporal dependencies across multiple instances (collected over a period of time), lack of available information from a single source, and difficulty in acquiring sufficient event data in a reasonable amount of time. Such data poses unique challenges to the field of predictive analytics and thus creates opportunities to develop new algorithms to tackle these issues. This project provides innovative computational methods to assist novel scientific discoveries and bring practical transformational impact to the analysis and exploration of various time-to-event datasets and applications. The proposed methods are primarily being evaluated in the context of biomedical data, but are applicable to various other forms of time-to-event data that is often seen in other disciplines such as social science, engineering, finance, and economics. This project builds novel computational and analytical algorithms that can efficiently and accurately capture the underlying predictive patterns in time-to-event data. The project aims at building new algorithms for longitudinal data analysis, integrate multiple sources while building time-to-event models, and predict temporal events with limited amount of training data. The progress of the project and the research findings are disseminated via the project website (http://dmkd.cs.vt.edu/projects/survival/).
Journal Articles and Conference Papers:
Will be made available soon.
Point of Contact: Chandan Reddy : reddy (at) cs (dot) vt (dot) edu
Date of Last Update: 9/30/16