image

Virtual Reality “DREAMS” Helps Patients Cope In ICU

Virtual Reality “DREAMS” Helps Patients Cope In ICU Home And Science Article DREAMs trail  Bold Voices The stress and isolation Dr.Bihorac witnessed as an attending physician at UF Health Shands […]

Read More

Cardiac and Vascular Surgery–Associated Acute Kidney Injury: A Fresh New Read!

Abstract Acute kidney injury (AKI) occurs in 7% to 18% of hospitalized patients and complicates the course of 50% to 60% of those admitted to the intensive care unit, carrying […]

Read More
featuredakirisk

Improved Predictive Models for Acute Kidney Injury with IDEAs: Intraoperative Data Embedded Analytics

Acute kidney injury (AKI) is a common and serious complication after a surgery which is associated with morbidity and mortality. The majority of existing perioperative AKI risk score prediction models […]

Read More
Surgery stock image

The impact of age on the innate immune response and outcomes after severe sepsis/septic shock in trauma and surgical intensive care unit patients.

Abstract Introduction Advancing age is a strong risk factor for adverse outcomes across multiple disease processes. However, septic surgical and trauma patients are unique in they incur two or more […]

Read More
Figure 4. Similarities and redundancies in the pathophysiology of patients with sepsis, cancer, and advanced age.

Innate Immunity in the Persistent Inflammation, Immunosuppression, and Catabolism Syndrome and Its Implications for Therapy.

Innate Immunity in the Persistent Inflammation, Immunosuppression, and Catabolism Syndrome and Its Implications for Therapy Clinical and technological advances promoting early hemorrhage control and physiologic resuscitation as well as early […]

Read More
DeepSofa Acuity Scores

DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning

  DeepSOFA: A Real-Time Continuous Acuity Score Framework using Deep Learning Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require manual, time-consuming, and error-prone […]

Read More
Prev Next

Announcements

SEMINAR: AZRA BIHORAC

SEMINAR: AZRA BIHORAC

UFL ece news “Augmented Decision Making – How to Implement Machine Learning Prediction into Research and Clinical Care” Thursday, Jan. 16 at 1:00 pm LAR 330 Abstract Clinical decision-making is dominated by […]

» more

Established in 2013, PrismaP is changing the face of medicine through the use of intelligent, data-driven algorithms.

Bringing together experts in Medicine, Health Policy Research, and Computer Engineering, our partnership combines modern approaches to modeling, signal processing, and machine learning in order to develop and refine data analysis methods. The predictive models, physiologic markers and clinical reasonings generated by Prisma P will serve to facilitate patient care in perioperative environments.




Archives