Dr. Samantha Kleinberg and Dr. Adriana Compagnoni of the Department of Computer Science at Stevens Institute of Technology, in collaboration with Columbia University Professor Jan Claassen, have received a grant from the National Institutes of Health (NIH) to help researchers more efficiently analyze unwieldy time-series datasets. The work has a wide range of applications, with the most direct implications for neurological intensive care unit (NICU) data streams where large volumes of data generated from continuous recording of patients’ brain activity and physiological signs can overwhelm clinicians’ ability to find complex patterns that can inform treatment in real time. The collaborators are developing systems and algorithms to give clinicians actionable information soon enough to have a substantial impact on patient outcomes. To enable rigorous validation of the algorithms, the researchers are developing a new computational platform for generating simulated NICU time series data.
The massive datasets made possible by the explosion of modern information storage capacity provide researchers with tremendous opportunities to gain significant insights into how crucial aspects and factors of human experience work over time, often in situations where experiments are infeasible or unethical. However, these complex, multifaceted datasets do not easily translate into actionable knowledge. Under traditional modes of evaluation, hidden variables obscure understanding, and significant but rare events render probabilistic methods ineffective.
Among patients who are recovering from a stroke in an NICU setting, a seizure is a rare event which can have a significant impact on outcomes. According to Dr. Kleinberg, “Doctors need to know not just that an ICU patient being treated for a stroke is having a seizure but whether it is causing further injury before they can determine how to treat it.” Existing data mining techniques detect rare events without telling doctors about the effect on the larger system. Probabilistic techniques provide the latter but fail to detect rare events. “We want to develop a way to give more timely and robust alerts so that doctors can quickly formulate a course of action,” says Dr. Kleinberg.
“The methods developed by Dr. Kleinberg, Dr. Compagnoni, and Dr. Claassen will make it possible to go from complex datasets to knowledge to policy more quickly and aptly than ever before possible by identifying actionable information on causes,” says Dr. Dan Duchamp, Department Director for Computer Science at Stevens. Graduate students working with each of the professors will enter the workforce with crucial knowledge in computational methods and their application to real-world datasets. The team will also make realistic simulations freely available to the research community in order to enable algorithmic development by computational researchers outside of medical centers, thus creating a framework for the validation and comparison of algorithms.
Dr. Kleinberg is an expert on causality, inference from complex data and biomedical informatics. Her work combines these areas, uniting temporal logic and tools from computer science with philosophical theories of causality to solve biomedical problems. She has previously applied these methods to stock return time series as well as political speeches and popularity ratings. Before her appointment at Stevens, she served as a postdoctoral Computing Innovation Fellow at Columbia University, in the Department of Biomedical Informatics. Her book, Causality, Probability, and Time, is now available in print and electronically.