Missing Values and Imputation in Healthcare Data: Can Interpretable
Machine Learning Help?

Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?

23 April 2023

Zhi Chen

Urszula Chajewska

Papers citing "Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?"

6 / 6 papers shown

Title
GAMformer: In-Context Learning for Generalized Additive Models Andreas Mueller Julien N. Siems Harsha Nori David Salinas Arber Zela Rich Caruana Frank Hutter AI4CE 33 3 0 06 Oct 2024
Daily Physical Activity Monitoring -- Adaptive Learning from Multi-source Motion Sensor Data Haoting Zhang Donglin Zhan Yunduan Lin Jinghai He Qing Zhu Z. Shen Zeyu Zheng 40 0 0 26 May 2024
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework Elham Nasarian R. Alizadehsani U. Acharya Kwok-Leung Tsui 30 42 0 18 Nov 2023
Methods for Recovering Conditional Independence Graphs: A Survey H. Shrivastava Urszula Chajewska CML 22 11 0 13 Nov 2022
Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets Sreejita Ghosh E. Baranowski Michael Biehl W. Arlt Peter Tiño the United Kingdom Utrecht University 23 6 0 04 Jun 2022
MissForest - nonparametric missing value imputation for mixed-type data D. Stekhoven Peter Buhlmann 177 4,228 0 04 May 2011