Title: Forecasting potential diabetes complications
Authors: Yang, Yang ×
Luyten, Walter
Liu, Lu
Moens, Marie-Francine
Tang, Jie
Li, Juanzi #
Issue Date: 2014
Publisher: AAAI
Host Document: Proceedings of the twenty-eighth AAAI conference on artificial intelligence (AAAI-14) pages:313-319
Conference: The twenty-eighth AAAI conference on artificial intelligence (AAAI-14) location:Québec city, Canada date:27-31 July 2014
Abstract: Diabetes complications often afflict diabetes patients seriously: over 68% of diabetes-related mortality is caused by diabetes complications. In this paper, we study the problem of automatically diagnosing diabetes complications from patients’ lab test results. The objective problem has two main challenges: 1) feature sparseness: a patient only undergoes 1:26% lab tests on average, and 65:5% types of lab tests are performed on samples from less than 10 patients; 2) knowledge skewness: it lacks comprehensive detailed domain knowledge of the association between diabetes complications and lab
tests. To address these challenges, we propose a novel probabilistic model called Sparse Factor Graph Model (SparseFGM). SparseFGM projects sparse features onto a lower-dimensional latent space, which alleviates the
problem of sparseness. SparseFGM is also able to capture the associations between complications and lab tests, which help handle the knowledge skewness. We evaluate the proposed model on a large collections of
real medical records. SparseFGM outperforms (+20% by F1) baselines significantly and gives detailed associations between diabetes complications and lab tests.
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
Department of Pharmaceutical & Pharmacological Sciences - miscellaneous
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
YangYangetalAAAI2014.pdf Published 398KbAdobe PDFView/Open


All items in Lirias are protected by copyright, with all rights reserved.