2 citations · 4 across the 4 of their papers we have counts for
4 papers
Performance metrics for intervention-triggering prediction models do not reflect an expected reduction in outcomes from using the model
Alejandro Schuler, Aashish Bhardwaj, Vincent Liu
Clinical researchers often select among and evaluate risk prediction models using standard machine learning metrics based on confusion matrices. However, if these models are used t…
A Causal Machine Learning Framework for Predicting Preventable Hospital Readmissions
Ben J. Marafino, Alejandro Schuler, Vincent X. Liu +2
Clinical predictive algorithms are increasingly being used to form the basis for optimal treatment policies--that is, to enable interventions to be targeted to the patients who wil…
Nonstationary Multivariate Gaussian Processes for Electronic Health Records
Rui Meng, Braden Soper, Herbert Lee +3
We propose multivariate nonstationary Gaussian processes for jointly modeling multiple clinical variables, where the key parameters, length-scales, standard deviations and the corr…
Modeling sepsis progression using hidden Markov models
Brenden K. Petersen, Michael B. Mayhew, Kalvin O. E. Ogbuefi +3
Characterizing a patient's progression through stages of sepsis is critical for enabling risk stratification and adaptive, personalized treatment. However, commonly used sepsis dia…