13 citations · 23 across the 8 of their papers we have counts for
8 papers
Sequential algorithmic modification with test data reuse
Jean Feng, Gene Pennello, Nicholas Petrick +3
After initial release of a machine learning algorithm, the model can be fine-tuned by retraining on subsequently gathered data, adding newly discovered features, or more. Each modi…
Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees
Jean Feng, Alexej Gossmann, Berkman Sahiner +1
After deploying a clinical prediction model, subsequently collected data can be used to fine-tune its predictions and adapt to temporal shifts. Because model updating carries risks…
Learning how to approve updates to machine learning algorithms in non-stationary settings
Jean Feng
Machine learning algorithms in healthcare have the potential to continually learn from real-world data generated during healthcare delivery and adapt to dataset shifts. As such, th…
Ensembled sparse-input hierarchical networks for high-dimensional datasets
Jean Feng, Noah Simon
Neural networks have seen limited use in prediction for high-dimensional data with small sample sizes, because they tend to overfit and require tuning many more hyperparameters tha…
Approval policies for modifications to Machine Learning-Based Software as a Medical Device: A study of bio-creep
Jean Feng, Scott Emerson, Noah Simon
Successful deployment of machine learning algorithms in healthcare requires careful assessments of their performance and safety. To date, the FDA approves locked algorithms prior t…
Estimation of cell lineage trees by maximum-likelihood phylogenetics
Jean Feng, William S DeWitt, Aaron McKenna +3
CRISPR technology has enabled large-scale cell lineage tracing for complex multicellular organisms by mutating synthetic genomic barcodes during organismal development. However, th…