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Learning the progression patterns of treatments using a probabilistic generative model.

Onintze Zaballa¹, Aritz Pérez¹, Elisa Gómez Inhiesto²

¹BCAM-Basque Center for Applied Mathematics, Bilbao 48009, Spain.

Journal of Biomedical Informatics

|December 18, 2022

Summary

This summary is machine-generated.

This study introduces a novel probabilistic model for analyzing patient treatment sequences from electronic health records. The model identifies distinct treatment subtypes and their progression, aiding in personalized medicine and clinical research.

Keywords:

Disease progression modeling Electronic health records Markov model Probabilistic generative model Unsupervised machine learning

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Published on: October 10, 2018

Area of Science:

Medical Informatics
Computational Biology
Machine Learning

Background:

Electronic Health Records (EHRs) contain vast patient data, driving interest in disease and treatment modeling.
Analyzing treatment sequences is crucial for understanding disease progression and patient outcomes.

Purpose of the Study:

To develop a probabilistic generative model for variable-length treatment sequences.
To identify distinct treatment subtypes and their temporal development and progression.
To classify treatments and determine their stages.

Main Methods:

A probabilistic generative model with a hierarchical structure of latent variables.
Utilizing the Expectation-Maximization algorithm for model learning.
Employing dynamic programming for efficient solving of latent variable configurations.

Main Results:

Demonstrated recovery of the underlying generative model using synthetic data.
Assessed the model's capability for treatment classification and staging on real-world data.
Validated the model's effectiveness in identifying distinct treatment subtypes and progression patterns.

Conclusions:

The developed model offers a robust tool for analyzing complex treatment sequences in EHRs.
Potential applications include classification, simulation, data augmentation, and missing data imputation.
Facilitates deeper insights into treatment variations and disease management strategies.