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.
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.
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.
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.
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.
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.
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.
Our Latest Publications
A Novel Single Cell RNA-seq Analysis of Non-Myeloid Circulating Cells in Late Sepsis
CONCLUSION: Circulating non-myeloid cells display a unique transcriptomic pattern late after sepsis. Non-myeloid leukocytes in particular reveal a host endotype of inflammation, immunosuppression, and dysfunction, suggesting a role for precision…
Characterization and Outcomes of Hospitalized Children With Coronavirus Disease 2019: A Report From a Multicenter, Viral Infection and Respiratory Illness Universal Study (Coronavirus Disease 2019) Registry
CONCLUSIONS: In this observational, multicenter registry of children with coronavirus disease 2019, ICU admission was common. Older age, fever, multisystem inflammatory syndrome in children, and seizure disorder were independently associated…
Chronic Critical Illness Elicits a Unique Circulating Leukocyte Transcriptome in Sepsis Survivors
Surgical sepsis has evolved into two major subpopulations: patients who rapidly recover, and those who develop chronic critical illness (CCI). Our primary aim was to determine whether CCI sepsis survivors…
Aligning Patient Acuity with Resource Intensity after Major Surgery: A Scoping Review
CONCLUSIONS: To optimize value, postoperative resource intensity decisions should align with precise, data-driven patient acuity assessments augmented by models that accurately represent complex, non-linear relationships among risk factors.