3 citations · 4 across the 7 of their papers we have counts for
5 papers · 1 filter
Uncertainty of Vision Medical Foundation Models
Haoxu Huang, Narges Razavian
Accurate uncertainty estimation is essential for machine learning systems de- ployed in high-stakes domains such as medicine. Traditional approaches primarily rely on probability o…
Scaling Recurrence-aware Foundation Models for Clinical Records via Next-Visit Prediction
Haresh Rengaraj Rajamohan, Xiang Gao, Weicheng Zhu +10
While large-scale pretraining has revolutionized language modeling, its potential remains underexplored in healthcare with structured electronic health records (EHRs). We present R…
Foundation Models for Clinical Records at Health System Scale
Haresh Rengaraj Rajamohan, Xiang Gao, Weicheng Zhu +5
Large-scale pretraining has transformed modeling of language and other data types, but its potential remains underexplored in healthcare with structured electronic health records (…
Causal Effect Variational Autoencoder with Uniform Treatment
Daniel Jiwoong Im, Kyunghyun Cho, Narges Razavian
Domain adaptation and covariate shift are big issues in deep learning and they ultimately affect any causal inference algorithms that rely on deep neural networks. Causal effect va…
Towards Quantification of Bias in Machine Learning for Healthcare: A Case Study of Renal Failure Prediction
Josie Williams, Narges Razavian
As machine learning (ML) models, trained on real-world datasets, become common practice, it is critical to measure and quantify their potential biases. In this paper, we focus on r…