YOHEI OKADA

Assistant Professor

Duke-NUS Medical School

Singapore

Research Interests

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Yohei Okada is an Assistant Professor at Duke-NUS Medical School, National University of Singapore. His research areas include emergency medicine, trauma care, and the application of machine learning in clinical settings. Recent articles authored by Dr. Okada focus on topics such as trauma center practices, clinical prediction scores for cardiac arrest patients, and the efficiency of semi-automated citation screening software.

Articles (10)

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Collaborators (3)

Fahad Javaid Siddiqui

Assistant Professor

Duke-NUS Medical School

SINGAPORE

View details

Tetsuhisa Kitamura

Osaka University

JAPAN

View details

Taka‐aki Nakada

JAPAN

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

YOHEI OKADA

Research Interests

Contact this professor

About

Articles (10)

Variation Among Trauma Centers in the Use of Angioembolization and Splenectomy Rate in Isolated High‐Grade Blunt Splenic Injuries

Clinical Prediction Score for Patients With Initial Nonshockable Rhythm Receiving Extracorporeal Cardiopulmonary Resuscitation for Out‐of‐Hospital Cardiac Arrest: Development and Internal Validation

Association between age and length of stay in the emergency department in a tertiary care hospital: a retrospective observational study

Pragmatic Risk Stratification Method to Identify Emergency Department Presentations for Alternative Care Service Pathways: Registry-Based Retrospective Study Over 5 Years

GPT-3.5 Turbo and GPT-4 Turbo in Title and Abstract Screening for Systematic Reviews

Association Between No‐Flow Time, Prehospital Low‐Flow Time, and Conversion to Nonshockable Rhythm in Patients With Out‐of‐Hospital Cardiac Arrest Presenting With Initial Shockable Rhythm: A Nationwide Prospective Study in Japan

Medical validity and layperson interpretation of emergency visit recommendations by the GPT model: A cross‐sectional study

Efficiency and Workload Reduction of Semi-automated Citation Screening Software for Creating Clinical Practice Guidelines: A Prospective Observational Study

Machine learning prediction of refractory ventricular fibrillation in out-of-hospital cardiac arrest using features available to EMS

Machine learning prediction of refractory ventricular fibrillation in out-of-hospital cardiac arrest using features available to EMS

Collaborators (3)

Fahad Javaid Siddiqui

Tetsuhisa Kitamura

Taka‐aki Nakada

Make your next move.