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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Predicting treatment process steps from events.

Jens Meier¹, Andreas Dietz², Andreas Boehm³

¹Innovation Center Computer Assisted Surgery (ICCAS), Universität Leipzig, Semmelweisstrasse 14, 04103 Leipzig, Germany.

Journal of Biomedical Informatics

|December 17, 2014

Summary

This summary is machine-generated.

This study introduces a novel method to add clinical workflow metadata to electronic patient records, improving data accessibility. This approach enhances clinical information systems by predicting workflow steps, aiding clinical decision-making and pathway analysis.

Keywords:

Digital patient model Hidden Markov Model Tumor therapy Workflow recognition

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Area of Science:

Medical Informatics
Clinical Workflow Analysis
Health Data Management

Background:

Current electronic patient documentation is primarily economy-driven, lacking crucial metadata on clinical workflows.
Physicians spend excessive time searching for patient data due to missing workflow context.
Intelligent support systems require metadata on underlying clinical processes for effective operation.

Purpose of the Study:

To develop a novel approach for enriching clinical information systems with workflow-specific metadata.
To enable direct access to relevant patient information based on the current process step.
To lay the foundation for improved clinical workflow support and analysis.

Main Methods:

Utilized Hidden Markov Models (HMMs) to mathematically represent clinical workflows.
Developed models from anonymized patient data for head and neck cancer therapy processes.
Implemented two methodologies to enhance workflow recognition accuracy.

Main Results:

Achieved promising workflow recognition rates of up to 90% in a cross-validation study.
Reported a standard deviation of 6.4% for the recognition rates.
Demonstrated the feasibility of predicting clinical workflow steps from patient data.

Conclusions:

The presented method enables prediction of clinical workflow steps from patient-specific information.
This forms a basis for clinical workflow support systems.
Facilitates the analysis and improvement of clinical pathways.