TY - GEN
T1 - Clustering longitudinal clinical marker trajectories from electronic health data
T2 - 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
AU - Schulam, Peter
AU - Wigley, Fredrick
AU - Saria, Suchi
N1 - Publisher Copyright:
Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - Diseases such as autism, cardiovascular disease, and the autoimmune disorders are difficult to treat because of the remarkable degree of variation among affected individuals. Subtyping research seeks to refine the definition of such complex, multi-organ diseases by identifying homogeneous patient subgroups. In this paper, we propose the Probabilistic Subtyping Model (PSM) to identify subgroups based on clustering individual clinical severity markers. This task is challenging due to the presence of nuisance variability-variations in measurements that are not due to disease subtype-which, if not accounted for, generate biased estimates for the group-level trajectories. Measurement sparsity and irregular sampling patterns pose additional challenges in clustering such data. PSM uses a hierarchical model to account for these different sources of variability. Our experiments demonstrate that by accounting for nuisance variability, PSM is able to more accurately model the marker data. We also discuss novel subtypes discovered using PSM and the resulting clinical hypotheses that are now the subject of follow up clinical experiments.
AB - Diseases such as autism, cardiovascular disease, and the autoimmune disorders are difficult to treat because of the remarkable degree of variation among affected individuals. Subtyping research seeks to refine the definition of such complex, multi-organ diseases by identifying homogeneous patient subgroups. In this paper, we propose the Probabilistic Subtyping Model (PSM) to identify subgroups based on clustering individual clinical severity markers. This task is challenging due to the presence of nuisance variability-variations in measurements that are not due to disease subtype-which, if not accounted for, generate biased estimates for the group-level trajectories. Measurement sparsity and irregular sampling patterns pose additional challenges in clustering such data. PSM uses a hierarchical model to account for these different sources of variability. Our experiments demonstrate that by accounting for nuisance variability, PSM is able to more accurately model the marker data. We also discuss novel subtypes discovered using PSM and the resulting clinical hypotheses that are now the subject of follow up clinical experiments.
UR - http://www.scopus.com/inward/record.url?scp=84960123355&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84960123355&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84960123355
T3 - Proceedings of the National Conference on Artificial Intelligence
SP - 2956
EP - 2964
BT - Proceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
PB - AI Access Foundation
Y2 - 25 January 2015 through 30 January 2015
ER -