Unsupervised learning of disease progression models

Xiang Wang, David Sontag, Fei Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationKDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages85-94
Number of pages10
ISBN (Print)9781450329569
DOIs
StatePublished - 2014
Event20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014 - New York, NY, United States
Duration: Aug 24 2014Aug 27 2014

Other

Other20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014
CountryUnited States
CityNew York, NY
Period8/24/148/27/14

Fingerprint

Unsupervised learning
Pulmonary diseases
Medical problems
Trajectories

Keywords

  • bayesian network
  • disease progression modeling
  • markov jump process
  • medical informatics

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Wang, X., Sontag, D., & Wang, F. (2014). Unsupervised learning of disease progression models. In KDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 85-94). Association for Computing Machinery. https://doi.org/10.1145/2623330.2623754

Unsupervised learning of disease progression models. / Wang, Xiang; Sontag, David; Wang, Fei.

KDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2014. p. 85-94.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, X, Sontag, D & Wang, F 2014, Unsupervised learning of disease progression models. in KDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, pp. 85-94, 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, New York, NY, United States, 8/24/14. https://doi.org/10.1145/2623330.2623754
Wang X, Sontag D, Wang F. Unsupervised learning of disease progression models. In KDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2014. p. 85-94 https://doi.org/10.1145/2623330.2623754
Wang, Xiang ; Sontag, David ; Wang, Fei. / Unsupervised learning of disease progression models. KDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2014. pp. 85-94
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