Samantha Kleinberg, associate professor in the Department of Computer Science, recently received a grant of $1,139,620 from the National Institutes of Health (NIH) for her project “BIG DATA: Causal Inference in Large-Scale Time Series.”
Large datasets generated by hospitals could have a transformative effect on medical knowledge and patient care. Yet currently the volume of data is more likely to overwhelm clinicians, and the challenges of the data can overwhelm machine learning algorithms. Intensive care units (ICUs) generate data at a resolution of seconds, for the entirety of a patient’s stay. Kleinberg’s long-term goal is to turn this data into actionable knowledge, like risk factors for a disease, early intervention targets, and real-time information to support clinical decisions. This is a broad problem, but particularly important in ICUs, which involve high stakes decisions being made in a complex environment under time pressure.
Kleinberg focuses in particular on understanding consciousness in adults, and neurologic status in neonates. While 7% of ICU admissions are due to loss of consciousness — and degree of consciousness is critical to evaluating prognosis, making difficult choices such as when to withdraw care, and providing early interventions to improve quality of life — there are no objective or automated assessments for consciousness (adults) or neurologic status (neonates). Research has shown that unresponsive patients with brain activation were twice as likely to regain the ability to follow commands compared to unresponsive patients without such activation, yet these assessments are too time-consuming for regular clinical use. However, Kleinberg’s work has also shown that physiological data routinely collected in ICUs can be used as a proxy to classify consciousness. Her work in this area earned the Homer R. Warner Award at the 2019 AMIA Annual Symposium.
There have been two key barriers preventing a causal understanding of consciousness. Variables measured for each ICU patient differ, and can differ within a patient over the course of their admission, leading to confounding when attempting to infer causal models.This has prevented learning a single model for all patients, which limits generalizability. Additionally, while the challenges of medical data require new methods, researchers are rarely able to rigorously evaluate and compare them, since real-world data lacks ground truth and often cannot be shared for privacy reasons. Giuseppe Ateniese, professor of computer science at Stevens, will contribute to privacy aspects of this research.
To address these challenges, Kleinberg and her team, comprised of collaborators from Columbia and McGill Universities, will develop methods that learn generalizable causal models with latent variables (by intelligently sharing and combining information across patients), develop data-driven simulations methods for testing machine learning algorithms while preserving privacy, and apply these methods to neonatal and neurological ICU data. She aims to create better indicators for consciousness and to uncover causes of both neurological status in ICU and its link to long-term functional outcomes. Her work turns potential weaknesses of medical data (different variables measured across individuals) into a strength, and will enable the better use of large-scale observational biomedical data for real-time treatment decisions.
Kleinberg said, “It was surprising that we were able to use physiological data to classify each patient's degree of consciousness, as prior work has relied mainly on fMRI and EEG. Using this data, though, lets us develop methods that can be used continuously around the clock. I'm excited to build on this to figure out why consciousness changes and to expand our work to a new population (neonates) with McGill.”
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