Research

innovation in medicine

Making New Discoveries

There is always exciting research being conducted at PRISMAp labs. Find out more about our exciting research or read one of our many published articles below.

someone injecting liquid into vials in a lab

Current Projects

  • Motion Analysis of Delirium in the Intensive Care Unit (ICU)
  • Intelligent ICU of the Future
  • Integrating Data, Algorithms and Clinical Reasoning for Surgical Risk Assessment (IDEALIST)
  • Automated Algorithm Identifies and Communicates Risk of Acute Kidney Injury among Health Care Providers and Patients (AKI-EPIC)
  • Network Analysis of Urinary Molecular Signature Complements Clinical Data to Predict Postoperative Acute Kidney Injury (NavigateAKI) clinicaltrials.gov
  • Digital Rehabilitation Environment Augmenting Medical System (DREAMS) clinicaltrials.gov

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Featured Publications

Human Activity Recognition Using Inertial, Physiological, and Environmental Sensors

This survey focuses on critical role of machine learning in developing Human Activity Recognition applications based on inertial sensors in conjunction with physiological and environmental sensors.

diagram showing a person with a walker

Discovery and Validation of Urinary Molecular Signature of Early Sepsis

The first ever study to use urinary cellular gene expression to study sepsis. Researchers identified alterations in gene expression unique to systemic and kidney-specific pathophysiologic processes using whole-genome analyses of RNA isolated from the urinary cells of sepsis patients.

diagram of kidney

Clinical Trajectories of Acute Kidney Injury in Surgical Sepsis: A Prospective Observational Study 

Among critically ill surgical sepsis patients, persistent AKI and the absence of renal recovery are associated with distinct early and sustained immunologic and endothelial biomarker signatures and decreased long-term physical function and survival.

graph showing survival probability versus months after sepsis onset

Audiovisual Modules to Enhance Informed Consent in the ICU: A Pilot Study

Audiovisual modules may improve knowledge and comprehension of commonly performed ICU procedures among critically ill patients and caregivers who have no healthcare background.

Critical Care Explorations journal cover

Extended vertical lists for temporal pattern mining from multivariate time series

In this paper, the problem of mining complex temporal patterns in the context of multivariate time series is considered. A new method called the Fast Temporal Pattern Mining with Extended Vertical Lists.

graph showing pattern length versus computational time

Reinforcement learning in surgery

Patients and physicians make essential decisions regarding diagnostic and therapeutic interventions. These actions should be performed or deferred under time constraints and uncertainty regarding patients’ diagnoses and predicted response to treatment. This may lead to cognitive and judgment errors.

diagram showing reinforcement learning framework and challenges in development of reinforcement learning models

Our Latest Publications

Early combination therapy with immunoglobulin and steroids is associated with shorter ICU length of stay in Multisystem Inflammatory Syndrome in Children (MIS-C) associated with COVID19: A retrospective cohort analysis from 28 U.S Hospitals

CONCLUSIONS: Combination therapy with IVIG and steroids initiated in the first 2 days of admission favorably impacts ICU but not the overall hospital LOS in children with MIS-C.

Gamification for Machine Learning in Surgical Patient Engagement

Patients and their surgeons face a complex and evolving set of choices in the process of shared decision making. The plan of care must be tailored to individual patient risk…

Potentials and Challenges of Pervasive Sensing in the Intensive Care Unit

Patients in critical care settings often require continuous and multifaceted monitoring. However, current clinical monitoring practices fail to capture important functional and behavioral indices such as mobility or agitation. Recent…

Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative Complications and Report on a Mobile Platform

CONCLUSIONS AND RELEVANCE: In this study, automated real-time predictions of postoperative complications with mobile device outputs had good performance in clinical settings with prospective validation, matching surgeons’ predictive accuracy.