A5053650135- Artificial Intelligence in Healthcare and Education
- Sepsis Diagnosis and Treatment
- Machine Learning in Healthcare
- Child and Adolescent Health
- COVID-19 Clinical Research Studies
- COVID-19 diagnosis using AI
- Vaccine Coverage and Hesitancy
- Heart Failure Treatment and Management
- COVID-19 and healthcare impacts
- COVID-19 epidemiological studies
- Healthcare Policy and Management
Johns Hopkins University
2020-2024
Merck & Co., Inc., Rahway, NJ, USA (United States)
2022
The COVID-19 pandemic has disrupted healthcare, including immunization practice and well child visit attendance. Maintaining vaccination coverage is important to prevent disease outbreaks morbidity. We assessed the impact of on pediatric adolescent administration attendance in United States. This cross-sectional study used IBM MarketScan Commercial Database (IMC) with Early View (healthcare claims database) TriNetX Dataworks Global Network (electronic medical records from January 2018–March...
The COVID-19 pandemic has sparked unprecedented public health and social measures (PHSM) by national local governments, including border restrictions, school closures, mandatory facemask use stay at home orders. Quantifying the effectiveness of these interventions in reducing disease transmission is key to rational policy making response current future pandemics. In order estimate interventions, detailed descriptions their timelines, scale scope are needed. Health Intervention Tracking for...
Background The adoption of predictive algorithms in health care comes with the potential for algorithmic bias, which could exacerbate existing disparities. Fairness metrics have been proposed to measure but their application real-world tasks is limited. Objective This study aims evaluate bias associated common 30-day hospital readmission models and assess usefulness interpretability selected fairness metrics. Methods We used 10.6 million adult inpatient discharges from Maryland Florida 2016...
<sec> <title>BACKGROUND</title> The adoption of predictive algorithms in health care comes with the potential for algorithmic bias, which could exacerbate existing disparities. Fairness metrics have been proposed to measure but their application real-world tasks is limited. </sec> <title>OBJECTIVE</title> This study aims evaluate bias associated common 30-day hospital readmission models and assess usefulness interpretability selected fairness metrics. <title>METHODS</title> We used 10.6...