Innovative Methods for Real-time Risk Modeling of Postoperative Complications

About

About


Background

This research will address the current critical challenge of post-operative complication prediction using real time intraoperative data and novel modeling methods. At a broader level, this work will improve the greater clinical research infrastructure at University of Minnesota for other use cases of real time clinical data, as well as provide infrastructure for contemporaneous feedback needed to realize a learning healthcare system and real time patient care improvement.

Goals & Aims

Funding

This work is funded by NIH National Institute of General Medical Sciences grant R01GM120079.

People

People


Principal Investigator

Gyorgy Simon, PhD

Co-Investigators

Genevieve Melton-Meaux, MD, PhD, FACS, FASCRS, FACMI

Michael Steinbach, PhD

Research Staff

Roshan Tourani

Publications

Publications

2017

 

Resources

Resources

  • BioMedICUS
    The BioMedical Information Collection and Understanding System (BioMedICUS) leverages open source solutions for text analysis and provides new analytic tools for processing and analyzing text of biomedical and clinical reports.