Xiang Wang
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.
Supplemental Material
- F. Baty, P. M. Putora, B. Isenring, T. Blum, and M. Brutsche. Comorbidities and burden of COPD: a population based case-control study. PLoS ONE, 8(5):e63285, 2013.Google Scholar
Cross Ref
- M. Cohen, A. Grossman, D. Morabito, M. M. Knudson, A. Butte, and G. Manley. Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis. Critical Care, 14(1):R10, 2010.Google ScholarCross Ref
- W. De Winter, J. DeJongh, T. Post, B. Ploeger, R. Urquhart, I. Moules, D. Eckland, and M. Danhof. A mechanism-based disease progression model for comparison of long-term effects of pioglitazone, metformin and gliclazide on disease processes underlying type 2 diabetes mellitus. Journal of pharmacokinetics and pharmacodynamics, 33(3):313--343, 2006.Google Scholar
- M. Decramer, W. Janssens, and M. Miravitlles. Chronic obstructive pulmonary disease. Lancet, 379(9823):1341--1351, Apr 2012.Google ScholarCross Ref
- K. Exarchos, T. Exarchos, C. Bourantas, M. Papafaklis, K. Naka, L. Michalis, O. Parodi, and D. Fotiadis. Prediction of coronary atherosclerosis progression using dynamic bayesian networks. In EMBC, pages 3889--3892, 2013.Google ScholarCross Ref
- G. I. for Chronic Obstructive Lung Disease. Global strategy for the diagnosis, management, and prevention of copd, January 2014.Google Scholar
- Y. Halpern and D. Sontag. Unsupervised learning of noisy-or bayesian networks. In UAI, pages 272--281, 2013.Google Scholar
- K. Ito, S. Ahadieh, B. Corrigan, J. French, T. Fullerton, and T. Tensfeldt. Disease progression meta-analysis model in alzheimer's disease. Alzheimer's & Dementia, 6(1):39--53, 2010.Google ScholarCross Ref
- C. H. Jackson, L. D. Sharples, S. G. Thompson, S. W. Duffy, and E. Couto. Multistate markov models for disease progression with classification error. Journal of the Royal Statistical Society: Series D (The Statistician), 52(2):193--209, 2003.Google Scholar
- P. Metzner, I. Horenko, and C. Schütte. Generator estimation of markov jump processes based on incomplete observations nonequidistant in time. Physical Review E, 76(6):066702, 2007.Google ScholarCross Ref
- D. Mould. Models for disease progression: new approaches and uses. Clinical Pharmacology & Therapeutics, 92(1):125--131, 2012.Google ScholarCross Ref
- T. M. Post, J. I. Freijer, J. DeJongh, and M. Danhof. Disease system analysis: basic disease progression models in degenerative disease. Pharmaceutical research, 22(7):1038--1049, 2005.Google ScholarCross Ref
- L. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257--286, 1989.Google ScholarCross Ref
- M. A. Shwe, B. Middleton, D. Heckerman, M. Henrion, E. Horvitz, H. Lehmann, and G. Cooper. Probabilistic diagnosis using a reformulation of the internist-1/qmr knowledge base. Meth. Inform. Med, 30:241--255, 1991.Google ScholarCross Ref
- R. Sukkar, E. Katz, Y. Zhang, D. Raunig, and B. T. Wyman. Disease progression modeling using hidden markov models. In EMBC, pages 2845--2848. IEEE, 2012.Google ScholarCross Ref
- J. Yang, J. J. McAuley, J. Leskovec, P. LePendu, and N. Shah. Finding progression stages in time-evolving event sequences. In WWW, pages 783--794, 2014. Google ScholarDigital Library
- J. Zhou, J. Liu, V. A. Narayan, and J. Ye. Modeling disease progression via fused sparse group lasso. In KDD, pages 1095--1103. ACM, 2012. Google ScholarDigital Library
- J. Zhou, J. Liu, V. A. Narayan, and J. Ye. Modeling disease progression via multi-task learning. NeuroImage, 78(0):233--248, 2013.Google ScholarCross Ref
Index Terms
- Unsupervised learning of disease progression models
Recommendations
Modeling Disease Progression with Deep Neural Networks
ISICDM 2020: The Fourth International Symposium on Image Computing and Digital MedicineAlzheimer's disease (AD), the most common type of dementia, is a severe neurodegenerative disorder. Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. In this paper, we consider the problem of predicting ...
Learning Transition Times in Event Sequences: The Temporal Event-Based Model of Disease Progression
Information Processing in Medical ImagingAbstractProgressive diseases worsen over time and can be characterised by sequences of events that correspond to changes in observable features of disease progression. Here we connect ideas from two formerly separate methodologies – event-based and hidden ...
Quality and Cost Improvement of Healthcare via Complementary Measurement and Diagnosis of Patient General Health Outcome Using Electronic Health Record Data: Research Rationale and Design
In this evolving `third era of health', one of the US Health Care Reform Act's goals is to effectively facilitate the primary care physician's ability to better diagnose and manage the health outcome of the outpatient. That goal must include research on ...
Comments