Area of research
Epidemiology · Artificial Intelligence
Research interest
Research interests include Sepsis Diagnosis and Treatment, Machine Learning in Healthcare, Chronic Obstructive Pulmonary Disease (COPD) Research, and Heart Failure Treatment and Management.
Machine Learning for Predicting Critical Events Among Hospitalized Children
A common longitudinal intensive care unit data format (CLIF) for critical illness research
Dysregulated Tricarboxylic Acid Cycle Metabolism Is Associated With Right Ventricular Maladaptation in Pulmonary Vascular Disease
Comparison of Multimodal Deep Learning Approaches for Predicting Clinical Deterioration in Ward Patients: Observational Cohort Study
Early Warning Scores With and Without Artificial Intelligence
Development and external validation of deep learning clinical prediction models using variable-length time series data
Comparison of early warning scores for predicting clinical deterioration and infection in obstetric patients
Development and External Validation of a Machine Learning Model for Prediction of Potential Transfer to the PICU
Using Machine Learning to Predict Likelihood and Cause of Readmission After Hospitalization for Chronic Obstructive Pulmonary Disease Exacerbation
Temperature Trajectory Subphenotypes in Oncology Patients with Neutropenia and Suspected Infection
Less is more: Detecting clinical deterioration in the hospital with machine learning using only age, heart rate, and respiratory rate
Identifying High-Risk Subphenotypes and Associated Harms From Delayed Antibiotic Orders and Delivery*
Multicenter Validation of the Neonatal Sequential Organ Failure Assessment Score for Prognosis in the Neonatal Intensive Care Unit
Determining the Electronic Signature of Infection in Electronic Health Record Data
Comparison of Machine Learning Methods for Predicting Outcomes After In-Hospital Cardiac Arrest
Comparison of Early Warning Scoring Systems for Hospitalized Patients With and Without Infection at Risk for In-Hospital Mortality and Transfer to the Intensive Care Unit
Internal and External Validation of a Machine Learning Risk Score for Acute Kidney Injury
Evaluating the Need to Address Digital Literacy Among Hospitalized Patients: Cross-Sectional Observational Study
Temperature Trajectory Subphenotypes Correlate With Immune Responses in Patients With Sepsis
Effectiveness of Virtual vs In-Person Inhaler Education for Hospitalized Patients With Obstructive Lung Disease
Predicting clinical deterioration with Q-ADDS compared to NEWS, Between the Flags, and eCART track and trigger tools
Variation in Best Practice Measures in Patients With Severe Hospital-Acquired Acute Kidney Injury: A Multicenter Study
Measuring eHealth Literacy in Urban Hospitalized Patients: Implications for the Post-COVID World
Identifying Novel Sepsis Subphenotypes Using Temperature Trajectories
Characteristics of Rapid Response Calls in the United States: An Analysis of the First 402,023 Adult Cases From the Get With the Guidelines Resuscitation-Medical Emergency Team Registry
Combining patient visual timelines with deep learning to predict mortality
Validation of Early Warning Scores at Two Long-Term Acute Care Hospitals
The Development of a Machine Learning Inpatient Acute Kidney Injury Prediction Model*
Predicting Intensive Care Unit Readmission with Machine Learning Using Electronic Health Record Data
Association Between Survival and Time of Day for Rapid Response Team Calls in a National Registry