Research Project · Closed

COVID-19 phenotyping & clinical decision support

Past project

Machine learning models for COVID-19 deployed during the pandemic: clinical phenotyping, deterioration prediction, and prognostic models studied across multiple validation cohorts and updated as the pandemic evolved.

6 researchers12 publications

ResearchDivision of Health AI

People · 6

On this project

Theodoros Zanos, PhD

Professor & AVP

Todd Levy, MS

Senior Biomedical Engineer

Shubham Debnath, PhD

Senior Research Scientist

Lucas Cang

Visiting Scholar

Soroush Arabshahi, PhD

Senior Biomedical Engineer

Rohan Sen

Intern

Publications · 12

On this project

Theodoros Zanos, PhD

Todd Levy, MS

Shubham Debnath, PhD

Lucas Cang

Soroush Arabshahi, PhD

Rohan Sen

Related papers

Detection of Common Respiratory Infections, Including COVID-19, Using Consumer Wearable Devices in Health Care Workers: Prospective Model Validation Study

Factors associated with immune responses to SARS-CoV-2 vaccination in autoimmune disease individuals

Longitudinal dynamic clinical phenotypes of in-hospital COVID-19 patients across three dominant virus variants in New York

The association of clinically relevant variables with chest radiograph lung disease burden quantified in real-time by radiologists upon initial presentation in individuals hospitalized with COVID-19

US and Dutch Perspectives on the Use of COVID-19 Clinical Prediction Models: Findings from a Qualitative Analysis

Prognostic models for COVID-19 needed updating to warrant transportability over time and space

Development and Validation of Self-Monitoring Auto-Updating Prognostic Models of Survival for Hospitalized COVID-19 Patients

High Frequency of COVID-19 Vaccine Hesitancy among Canadians Immunized for Influenza: A Cross-Sectional Survey

A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation

External validation demonstrates limited clinical utility of the interpretable mortality prediction model for patients with COVID-19

Machine learning to assist clinical decision-making during the COVID-19 pandemic

Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area

On this project

Theodoros Zanos, PhD

Todd Levy, MS

Shubham Debnath, PhD

Lucas Cang

Soroush Arabshahi, PhD

Rohan Sen

Related papers

Detection of Common Respiratory Infections, Including COVID-19, Using Consumer Wearable Devices in Health Care Workers: Prospective Model Validation Study

Factors associated with immune responses to SARS-CoV-2 vaccination in autoimmune disease individuals

Longitudinal dynamic clinical phenotypes of in-hospital COVID-19 patients across three dominant virus variants in New York

The association of clinically relevant variables with chest radiograph lung disease burden quantified in real-time by radiologists upon initial presentation in individuals hospitalized with COVID-19

US and Dutch Perspectives on the Use of COVID-19 Clinical Prediction Models: Findings from a Qualitative Analysis

Prognostic models for COVID-19 needed updating to warrant transportability over time and space

Development and Validation of Self-Monitoring Auto-Updating Prognostic Models of Survival for Hospitalized COVID-19 Patients

High Frequency of COVID-19 Vaccine Hesitancy among Canadians Immunized for Influenza: A Cross-Sectional Survey

A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation

External validation demonstrates limited clinical utility of the interpretable mortality prediction model for patients with COVID-19

Machine learning to assist clinical decision-making during the COVID-19 pandemic

Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area