Area of research
Cardiology and Cardiovascular Medicine · Artificial Intelligence
Research interest
Research interests include Cardiac, Anesthesia and Surgical Outcomes, Machine Learning in Healthcare, Hemodynamic Monitoring and Therapy, and Sepsis Diagnosis and Treatment.
Strengthening Discovery and Application of Artificial Intelligence in Anesthesiology: A Report from the Anesthesia Research Council
Strengthening Discovery and Application of Artificial Intelligence in Anesthesiology: A Report from the Anesthesia Research Council
Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings
Methylation risk scores are associated with a collection of phenotypes within electronic health record systems
Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning
Development and validation of an interpretable neural network for prediction of postoperative in-hospital mortality
Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set
Realistically Integrating Machine Learning Into Clinical Practice: A Road Map of Opportunities, Challenges, and a Potential Future
Machine Learning Prediction of Postoperative Emergency Department Hospital Readmission
Successful Implementation of a Perioperative Data Warehouse Using Another Hospital’s Published Specification From Epic’s Electronic Health Record System
An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data
BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference
Impact of Enhanced Recovery After Surgery and Opioid-Free Anesthesia on Opioid Prescriptions at Discharge From the Hospital: A Historical-Prospective Study
Intraoperative Clinical Decision Support for Anesthesia: A Narrative Review of Available Systems
A Systematic Approach to Creation of a Perioperative Data Warehouse