Work

Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression.

Year: 2015

Type: article

Abstract: The Continuous-Time Hidden Markov Model (CT-HMM) is an attractive approach to modeling disease progression due to its ability to describe noisy observations arriving irregularly in time. However, the lack of an efficient parameter learning algorithm for CT-HMM restricts its use to very small models or requires unrealistic constraints on the state transitions. In this paper, we present the first complete characterization of efficient EM-based learning methods for CT-HMM models. We demonstrate that the learning problem consists of two challenges: the estimation of posterior state probabilities and the computation of end-state conditioned statistics. We solve the first challenge by reformulating the estimation problem in terms of an equivalent discrete time-inhomogeneous hidden Markov model. The second challenge is addressed by adapting three approaches from the continuous time Markov chain literature to the CT-HMM domain. We demonstrate the use of CT-HMMs with more than 100 states to visualize and predict disease progression using a glaucoma dataset and an Alzheimer's disease dataset.

Source: PubMed

Authors: Yu-Ying Liu, Shuang Li, Fuxin Li, Le Song, James M Rehg

Institution: Georgia Institute of Technology

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Eye Institute, National Institute of General Medical Sciences, National Institute of Biomedical Imaging and Bioengineering

Awards: Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K) - OVERALL, BIGDATA: Small DA Social Behavior Driven Modeling and Optimization of Information, Alzheimer's Disease Neuroimaging Initiative, Novel Glaucoma Diagnostics for Structure and Function.