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Sequential decision tree using the analytic hierarchy process for decision support in rectal cancer.

Aslı Suner¹, Can Cengiz Çelikoğlu, Oğuz Dicle

¹Dokuz Eylül University, Faculty of Science, Department of Statistics, 35160 Tınaztepe, Buca, İzmir, Turkey. asli.suner@deu.edu.tr

Artificial Intelligence in Medicine

|July 11, 2012

Summary

The analytic hierarchy process (AHP) effectively prioritizes variables for constructing rectal cancer treatment decision trees. Surgeon experience and disease stage are key factors in optimizing patient care pathways.

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Area of Science:

Oncology
Surgical Decision-Making
Health Informatics

Background:

Rectal cancer management requires complex decision-making involving multiple patient-specific factors and treatment options.
Developing a standardized and effective decision-making process is crucial for optimizing patient outcomes.

Purpose of the Study:

To determine the most appropriate method for constructing a sequential decision tree for rectal cancer management.
To identify and prioritize key patient-specific criteria and treatment variables.

Main Methods:

Analytic Hierarchy Process (AHP) was employed to establish variable priorities.
A panel of five general surgeons participated in two decision steps using a web-based application.
Expert Choice software was used for data analysis and priority determination.

Main Results:

The AHP method yielded consistent judgments across decision steps.
In the first step, "presence of perforation" (tumor-related) and "surgeon's experience" (patient-surgeon-related) were highest priorities.
In the second step, "stage of the disease" (tumor-related) and "surgeon's experience" (patient-surgeon-related) were paramount.

Conclusions:

The quality of a decision tree significantly impacts the consistency of decision support systems.
The AHP method is effective for prioritizing variables in complex decision-making scenarios.
The developed decision algorithm offers a more realistic approach to improving rectal cancer treatment decision trees.