Publications by authors named "Yaoyong Tai"
Background: Timely and accurate outcome prediction is essential for clinical decision-making for ischemic stroke patients in the intensive care unit (ICU). However, the interpretation and translation of predictive models into clinical applications are equally crucial. This study aims to develop an interpretable machine learning (IML) model that effectively predicts in-hospital mortality for ischemic stroke patients.
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- Stroke recurrence readmission can heavily impact patients and healthcare systems, necessitating effective risk stratification to enable targeted interventions.* -
- Researchers analyzed over 339,000 stroke admission episodes from 2015 to 2019, developing the Score for Stroke Recurrence Readmission Prediction (SSRRP) tool based on six key factors to predict readmission risks.* -
- The SSRRP tool demonstrated a reliable predictive capability with an AUC of 0.730, offering a practical scoring method to help mitigate the chances of patients facing early readmission after a stroke.*
Purpose: To investigate in-hospital mortality and hospital length of stay (LOS) in infants requiring tracheostomy with bronchopulmonary dysplasia (BPD).
Methods: We explored the correlation between tracheostomy with in-hospital mortality and LOS in infant patients hospitalized with BPD, using the data from Nationwide Inpatient Sample between 2008 and 2017 in the United States. In-hospital mortality and LOS was compared in patients who underwent tracheostomy with those patients who did not after propensity-score matching.