Building a Predictive Model for Post-operative Complications and Survival after Lung Transplantation
Using ML to better predict post-transplant mortality.
AI/ML Implementation & Enabling Infrastructure
DIHI has developed a diverse portfolio of Augmented Intelligence & machine learning models and is responsible for the underlying infrastructure to facilitate their development and implementation into Duke Health. DIHI applies devops principles to the entirety of its infrastructure design to achieve reproducible, monitored, automated, and tested deployments of innovations at-scale for Duke.
Predictive Model for Assisted Triage of Traumatic Brain Injury
A model to aid actionable mild to moderate TBI triage decisions in the ER.
A Unified Framework for Predicting Bacteremia and Interpreting Blood Culture Results
Estimates the pre-test probability of bacteremia and post-test probability of blood culture results in hospitalized patients for EHR-based clinical decision support.
Transforming Epilepsy Surgery with Augmented Reality
Placing patient-specific 3D images in the Neurosurgeon’s field of view using augmented reality and advanced imaging to increase precision for epilepsy surgery.
A Population Health Strategy for NAFLD using an E-Consult Platform and NLP
A population health solution for NAFLD with an ultimate goal to optimize health care resources by improving access for high risk patients and minimizing unnecessary referrals.
Dermatology Clinical Decision Support in Primary Care
Improving dermatology access, care delivery and cost via machine learning assisted risk stratification.
Machine Learning for Early Identification and Management of Pulmonary Embolism
A machine learning risk stratification model to improve recognition and management of high risk PE while also reducing hospital utilization for low risk patients.
Using a Mortality Risk Predictor Model for Advance Care Planning via a Transitions of Care Toolkit
Identify high risk mortality inpatients to provide them with a Transition of Care Toolkit to help with advance care planning.
Development of a Maternal Early Warning System Using Machine Learning
Identify obstetric patients at risk for clinical deterioration by using a variety of patient clinical parameters obtained from multiple data modalities and predictive modeling of pregnancy specific, patient related changes.