TY - GEN
T1 - Unsupervised learning of disease progression models
AU - Wang, Xiang
AU - Sontag, David
AU - Wang, Fei
PY - 2014
Y1 - 2014
N2 - Chronic diseases, such as Alzheimer's Disease, Diabetes, and Chronic Obstructive Pulmonary Disease, usually progress slowly over a long period of time, causing increasing burden to the patients, their families, and the healthcare system. A better understanding of their progression is instrumental in early diagnosis and personalized care. Modeling disease progression based on real-world evidence is a very challenging task due to the incompleteness and irregularity of the observations, as well as the heterogeneity of the patient conditions. In this paper, we propose a probabilistic disease progression model that address these challenges. As compared to existing disease progression models, the advantage of our model is three-fold: 1) it learns a continuous-time progression model from discrete-time observations with non-equal intervals; 2) it learns the full progression trajectory from a set of incomplete records that only cover short segments of the progression; 3) it learns a compact set of medical concepts as the bridge between the hidden progression process and the observed medical evidence, which are usually extremely sparse and noisy. We demonstrate the capabilities of our model by applying it to a real-world COPD patient cohort and deriving some interesting clinical insights.
AB - Chronic diseases, such as Alzheimer's Disease, Diabetes, and Chronic Obstructive Pulmonary Disease, usually progress slowly over a long period of time, causing increasing burden to the patients, their families, and the healthcare system. A better understanding of their progression is instrumental in early diagnosis and personalized care. Modeling disease progression based on real-world evidence is a very challenging task due to the incompleteness and irregularity of the observations, as well as the heterogeneity of the patient conditions. In this paper, we propose a probabilistic disease progression model that address these challenges. As compared to existing disease progression models, the advantage of our model is three-fold: 1) it learns a continuous-time progression model from discrete-time observations with non-equal intervals; 2) it learns the full progression trajectory from a set of incomplete records that only cover short segments of the progression; 3) it learns a compact set of medical concepts as the bridge between the hidden progression process and the observed medical evidence, which are usually extremely sparse and noisy. We demonstrate the capabilities of our model by applying it to a real-world COPD patient cohort and deriving some interesting clinical insights.
KW - bayesian network
KW - disease progression modeling
KW - markov jump process
KW - medical informatics
UR - http://www.scopus.com/inward/record.url?scp=84907021735&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84907021735&partnerID=8YFLogxK
U2 - 10.1145/2623330.2623754
DO - 10.1145/2623330.2623754
M3 - Conference contribution
AN - SCOPUS:84907021735
SN - 9781450329569
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 85
EP - 94
BT - KDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014
Y2 - 24 August 2014 through 27 August 2014
ER -