8 citations · 11 across the 8 of their papers we have counts for
8 papers
Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination
Zhiyao Luo, Yangchen Pan, Peter Watkinson +1
In the rapidly changing healthcare landscape, the implementation of offline reinforcement learning (RL) in dynamic treatment regimes (DTRs) presents a mix of unprecedented opportun…
Learning using granularity statistical invariants for classification
Ting-Ting Zhu, Yuan-Hai Shao, Chun-Na Li +1
Learning using statistical invariants (LUSI) is a new learning paradigm, which adopts weak convergence mechanism, and can be applied to a wider range of classification problems. Ho…
Understanding Missingness in Time-series Electronic Health Records for Individualized Representation
Ghadeer O. Ghosheh, Jin Li, Tingting Zhu
With the widespread of machine learning models for healthcare applications, there is increased interest in building applications for personalized medicine. Despite the plethora of…
A Perspective on Individualized Treatment Effects Estimation from Time-series Health Data
Ghadeer O. Ghosheh, Moritz Gögl, Tingting Zhu
The burden of diseases is rising worldwide, with unequal treatment efficacy for patient populations that are underrepresented in clinical trials. Healthcare, however, is driven by…
Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities
Munib Mesinovic, Peter Watkinson, Tingting Zhu
Recent advancements in AI applications to healthcare have shown incredible promise in surpassing human performance in diagnosis and disease prognosis. With the increasing complexit…
All models are local: time to replace external validation with recurrent local validation
Alex Youssef, Michael Pencina, Anshul Thakur +3
External validation is often recommended to ensure the generalizability of ML models. However, it neither guarantees generalizability nor equates to a model's clinical usefulness (…