Using hidden Markov models to characterize disease trajectories

Summary, in English

A novel approach is developed for predicting body trajectories for cancer progression, where conditional probabilities of clinical data are modeled using Hidden Markov Model techniques. Basically, each potential body site is encoded by an N-letter code, and a disease trajectory is described in terms of a string of letters. Patient data base records are then represented by such strings with different lengths, start points and end points. The approach is explored using pathology data for non-Hodgkin lymphoma augmented with an artificial data base generated according to observed distributions in the clinical data. For the Hidden Markov Models a Bayesian approach is taken using the Hybrid Monte Carlo method, producing an ensemble of models rather than a single one. Using a test set consisting of both real and random trajectories, we estimate the performance of our Hidden Markov Model models and also extract most probable profiles. Given the limited data set size the results are very encouraging.

• Author: Carsten Peterson; Mattias Ohlsson
• Editor: G. M. Papadourakis
• Department/s: Computational Biology and Biological Physics - Has been reorganised; Artificial Intelligence in CardioThoracic Sciences (AICTS)
• Publishing year: 2001
• Language: English
• Pages: 324-326
• Publication/Series: Proceedings of the Neural Networks and Expert Systems in Medicine and Healthcare Conference, 324-326 (2001), eds. G.M. Papadourakis
• Document type: Book chapter
• Topic: Probability Theory and Statistics; Other Medical Engineering
• Status: Published
• Research group: Artificial Intelligence in CardioThoracic Sciences (AICTS)