A roadmap to fair and trustworthy prediction model validation in healthcare

no code implementations • 7 Apr 2023 • Yilin Ning, Victor Volovici, Marcus Eng Hock Ong, Benjamin Alan Goldstein, Nan Liu

A prediction model is most useful if it generalizes beyond the development data with external validations, but to what extent should it generalize remains unclear.

Shapley variable importance clouds for interpretable machine learning

1 code implementation • 6 Oct 2021 • Yilin Ning, Marcus Eng Hock Ong, Bibhas Chakraborty, Benjamin Alan Goldstein, Daniel Shu Wei Ting, Roger Vaughan, Nan Liu

Interpretable machine learning has been focusing on explaining final models that optimize performance.

BIG-bench Machine Learning Interpretable Machine Learning

AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data

1 code implementation • 13 Jul 2021 • Han Yuan, Feng Xie, Marcus Eng Hock Ong, Yilin Ning, Marcel Lucas Chee, Seyed Ehsan Saffari, Hairil Rizal Abdullah, Benjamin Alan Goldstein, Bibhas Chakraborty, Nan Liu

All scoring models were evaluated on the basis of their area under the curve (AUC) in the receiver operating characteristic analysis and balanced accuracy (i. e., mean value of sensitivity and specificity).

Decision Making Interpretable Machine Learning +1

AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data

1 code implementation • 13 Jun 2021 • Feng Xie, Yilin Ning, Han Yuan, Benjamin Alan Goldstein, Marcus Eng Hock Ong, Nan Liu, Bibhas Chakraborty

We illustrated our method in a real-life study of 90-day mortality of patients in intensive care units and compared its performance with survival models (i. e., Cox) and the random survival forest.

BIG-bench Machine Learning Interpretable Machine Learning +1