The success of foundation models such as ChatGPT and AlphaFold has spurred significant
interest in building similar models for electronic medical records (EMRs) to improve patient …

The use of machine learning (ML) in healthcare raises numerous ethical concerns,
especially as models can amplify existing health inequities. Here, we outline ethical …

While the general machine learning (ML) community has benefited from public datasets,
tasks, and models, the progress of ML in healthcare has been hampered by a lack of such …

Generative, pre-trained transformers (GPTs, a type of" Foundation Models") have reshaped
natural language processing (NLP) through their versatility in diverse downstream tasks …

Early prediction of patient outcomes is important for targeting preventive care. This protocol
describes a practical workflow for develo** deep-learning risk models that can predict …

As a longstanding learning paradigm, multi-task learning has been widely applied into a
variety of machine learning applications. Nonetheless, identifying which tasks should be …

Health conditions among patients in intensive care units (ICUs) are monitored via electronic
health records (EHRs), composed of numerical time series and lengthy clinical note …

Embedding algorithms are increasingly used to represent clinical concepts in healthcare for
improving machine learning tasks such as clinical phenoty** and disease prediction …

Recent work on deep learning for tabular data demonstrates the strong performance of deep
tabular models, often bridging the gap between gradient boosted decision trees and neural …

Irregularly sampled multivariate time series are ubiquitous in various fields, particularly in
healthcare, and exhibit two key characteristics: intra-series irregularity and inter-series …