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Found 6 papers, 4 papers with code
Prediction of Hereditary Cancers Using Neural Networks
We validate the models using data from the Cancer Genetics Network.
Extending Models Via Gradient Boosting: An Application to Mendelian Models
Via simulations we show that integration of gradient boosting with an existing Mendelian model can produce an improved model that outperforms both that model and the model built using gradient boosting alone.
Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes
We developed and evaluated two machine learning models to classify abstracts as relevant to the penetrance (risk of cancer for germline mutation carriers) or prevalence of germline genetic mutations.
Matching on Generalized Propensity Scores with Continuous Exposures
We apply our proposed method to estimate the average causal exposure-response function between long-term PM$_{2. 5}$ exposure and all-cause mortality among 68. 5 million Medicare enrollees, 2000-2016.
Methodology Applications
airpred: A Flexible R Package Implementing Methods for Predicting Air Pollution
Fine particulate matter (PM$_{2. 5}$) is one of the criteria air pollutants regulated by the Environmental Protection Agency in the United States.
Causal inference in the context of an error prone exposure: air pollution and mortality
We propose a new approach for estimating causal effects when the exposure is measured with error and confounding adjustment is performed via a generalized propensity score (GPS).
Methodology Applications
