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

13 June 2021

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

5 / 5 papers shown

Title
Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data Siqi Li Yuqing Shang Ziwen Wang Qiming Wu Chuan Hong Yilin Ning Di Miao M. Ong Bibhas Chakraborty Nan Liu FedML 60 1 0 08 Mar 2024
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission Ziwen Wang Jin Wee Lee Tanujit Chakraborty Yilin Ning Mingxuan Liu F. Xie M. Ong Nan Liu 101 2 0 04 Mar 2024
A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study Yilin Ning Siqi Li M. Ong F. Xie Bibhas Chakraborty Daniel Ting Nan Liu FAtt 58 23 0 10 Jan 2022
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies F. Xie Han Yuan Yilin Ning M. Ong Mengling Feng Wynne Hsu B. Chakraborty Nan Liu 99 90 0 21 Jul 2021
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data Han Yuan F. Xie M. Ong Yilin Ning M. Chee S. Saffari H. Abdullah B. Goldstein B. Chakraborty Nan Liu 47 20 0 13 Jul 2021