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Deep Representation Learning-Based Dynamic Trajectory Phenotyping for Acute Respiratory Failure in Medical Intensive Care Units
The study demonstrates the utility of our deep representation learning-based approach in unraveling phenotypes that reflect the heterogeneity in sepsis-induced ARF in terms of different mortality outcomes and severity.
Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine
Causal structure learning refers to a process of identifying causal structures from observational data, and it can have multiple applications in biomedicine and health care.
Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmark
We also expect to provide the feasibility of HTE for personalized drug effectiveness.
CodNN -- Robust Neural Networks From Coded Classification
By our approach, either the data or internal layers of the DNN are coded with error correcting codes, and successful computation under noise is guaranteed.
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