Nature

Interpretable machine learning model for digital lung cancer prescreening in Chinese populations with missing data

We developed an interpretable model, BOUND (Bayesian netwOrk for large-scale lUng caNcer Digital prescreening), using a comprehensive EHR dataset from the China to improve lung cancer detection rates.
Frontiers

Numerical foundations for scalable statistical and machine learning methods

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...
Nature

Integrated machine learning-bayesian receptor modelling framework for uncertainty-aware source apportionment in a non-stationary fluoride-contaminated coastal aquifer

Fluoride contamination in coastal aquifers is difficult to evaluate because its mobilisation is controlled by overlapping and seasonally dynamic non-stationary hydrogeochemical processes influenced by ...
ascopubs.org

Bayesian Counterfactual Machine Learning Individualizes Radiation Modality Selection to Mitigate Immunosuppression

Tumor Site–Specific Radiation-Induced Lymphocyte Depletion Models After Fractionated Radiotherapy: Considerations of Model Structure From an Aggregate Data Meta-Analysis Lymphocytes play critical ...