Project Details
Description
Project summary
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. Our long-term goal is to turn these 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. We focus 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). We have 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 we also
showed that physiological data routinely collected in ICUs can be used as a proxy to classify consciousness. It is
still not known why it changes and we must be sure that the patterns we find are in fact causal to avoid treating
symptoms instead of a disease or launching unsuccessful clinical trials. There have been two key barriers
preventing a causal understanding of consciousness. First, variables measured for each ICU patient differ, and
can differ within a patient over the course of their admission. This leads to confounding when attempting to
infer causal models, and has prevented learning a single model for all patients, which limits generalizability.
Second, 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. To address these challenges, we aim 1) to develop methods that learn generalizable causal models with
latent variables (by intelligently sharing and combining information across patients), 2) to develop data driven
simulations methods for testing machine learning algorithms while preserving privacy, and 3) to apply these
methods to neonatal and neurological ICU data. We aim to create better indicators for consciousness and to
uncover causes of both neurological status in ICU and its link to long-term functional outcomes. Our work
turns potential weaknesses of medical data (different variables measured across individuals) into a strength,
and will enable better use of large-scale observational biomedical data for real-time treatment decisions.
Status | Active |
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Effective start/end date | 1/06/13 → 28/02/25 |
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