30 citations · 52 across the 5 of their papers we have counts for
5 papers
A roadmap to fair and trustworthy prediction model validation in healthcare
Yilin Ning, Victor Volovici, Marcus Eng Hock Ong +2
A prediction model is most useful if it generalizes beyond the development data with external validations, but to what extent should it generalize remains unclear. In practice, pre…
Using natural language processing and structured medical data to phenotype patients hospitalized due to COVID-19
Feier Chang, Jay Krishnan, Jillian H Hurst +4
To identify patients who are hospitalized because of COVID-19 as opposed to those who were admitted for other indications, we compared the performance of different computable pheno…
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data
Han Yuan, Feng Xie, Marcus Eng Hock Ong +7
Background: Medical decision-making impacts both individual and public health. Clinical scores are commonly used among a wide variety of decision-making models for determining the…
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data
Feng Xie, Yilin Ning, Han Yuan +4
Scoring systems are highly interpretable and widely used to evaluate time-to-event outcomes in healthcare research. However, existing time-to-event scores are predominantly created…
Detecting clinically meaningful biomarkers with repeated measurements in an Electronic Health Record
Benjamin A Goldstein, Themistocles Assimes, Wolfgang C. Winkelmayer +1
Electronic health record (EHR) data are becoming an increasingly common data source for understanding clinical risk of acute events. While their longitudinal nature presents opport…