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.
Delirium prevention in the ICU is often achieved with non-pharmacological approaches, but some methods are difficult to implement due to their dependence on sporadic human observations. ADAPT, the Autonomous Delirium Monitoring and Adaptive Prevention system, will autonomously quantify two indicators of delirium (patient mobility and circadian desynchrony) and predict delirium trajectories, as well as prompt actions that decrease delirium risk factors (e.g., ambient light, nightly disruptions). Successful application of ADAPT would augment clinical decision-making and promote more targeted interventions.
While AI in critical care (AICC) has helped improve patient care, AICC research lacks a large generalized dataset, standards that support the exchange and use of health information (interoperability) between sites and researchers, and support from a workforce trained in AI and general public familiar with medical AI. CHoRUS, a multimillion dollar collaboration of 18 top medical research institutions, aims to solve these problems by creating a 100,000 patient dataset from a sample population with varied backgrounds, ethical standards that enable interoperability, and AI education for AICC researchers and the general public.
Although close monitoring and dynamic assessment of patient acuity are key aspects of ICU care, both are limited by time constraints imposed on healthcare providers. Dynamic and precise assessment of patient acuity relies almost exclusively on physicians’ clinical judgement and vigilance, and details indicating patient condition may be partially captured or completely missed by overburdened nurses. We aim to develop deep learning models for sensing, quantifying, and communicating any patient’s condition in an autonomous, precise, and interpretable manner. This tool could enhance clinical workflow and enable early intervention.
IDEALIST could advance the ability of healthcare providers to diagnose diseases by computing the risk of postoperative complications in real-time. Ultimately, the results are expected to improve patient outcomes, decrease hospitalization costs, and reduce lifelong complications. The proposed research is relevant to public health because it can result in enhanced perioperative care workflow and early intervention.
The Multi-Hospital Electronic Decision Support for Drug-Associated AKI (MEnD-AKI) project is aimed at assessing how effective a clinical surveillance system augmented with real-time predictive analytics can be in supporting pharmacist-led interventions to address drug-associated AKI (D-AKI).
- 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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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.
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.
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 may improve knowledge and comprehension of commonly performed ICU procedures among critically ill patients and caregivers who have no healthcare background.
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.
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
CONCLUSIONS: Among STARRT-AKI trial centers, significant regional practice variation exists regarding FB, timing of initiation of RRT, and initial use of continuous RRT. After adjustment, such practice variation was associated…
CONCLUSIONS: While a consistent definition for IOH remains elusive, the evidence suggests that MAP below 65 mmHg for over 5 min is strongly associated with postoperative AKI, with the risk…