IJCAI 2020poster0 citations
Inspection of Blackbox Models for Evaluating Vulnerability in Maternal, Newborn, and Child Health
William Ogallo, Skyler Speakman, Victor Akinwande, Kush R Varshney, Aisha Walcott-Bryant, Charity Wayua, Komminist Weldemariam
Abstract
Improving maternal, newborn, and child health (MNCH) outcomes is a critical target for global sustainable development. Our research is centered on building predictive models, evaluating their interpretability, and generating actionable insights about the markers (features) and triggers (events) associated with vulnerability in MNCH. In this work, we demonstrate how a tool for inspecting "black box" machine learning models can be used to generate actionable insights from models trained on demographic health survey data to predict neonatal mortality.
Machine Learning: general
BibTeX
@inproceedings{ijcai2020p770,
title = {Inspection of Blackbox Models for Evaluating Vulnerability in Maternal, Newborn, and Child Health},
author = {Ogallo, William and Speakman, Skyler and Akinwande, Victor and Varshney, Kush R and Walcott-Bryant, Aisha and Wayua, Charity and Weldemariam, Komminist},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Christian Bessiere},
pages = {5282--5284},
year = {2020},
month = {7},
note = {Demos},
doi = {10.24963/ijcai.2020/770},
url = {https://doi.org/10.24963/ijcai.2020/770},
}