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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

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A user-customizable hybrid framework for targeted medical decision-making.

M Gabriela Sava¹, Luis G Vargas², Jerrold H May²

¹Allen W. and Carol M. Schmidthorst College of Business, Bowling Green State University, Bowling Green, OH, 43403, USA.

Journal of Multi-Criteria Decision Analysis

|March 5, 2025

Summary

This study introduces a customizable framework for targeted medical decisions, balancing provider time and prediction accuracy. It groups patients by preferences to optimize resource allocation in healthcare.

Keywords:

Analytic Hierarchy Process (AHP)machine learning stability analysis user-customizable hybrid framework

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Area of Science:

Health Informatics
Decision Analysis
Medical Economics

Background:

Patient heterogeneity necessitates targeted medical decision-making.
Incorporating patient preferences is crucial for effective healthcare strategies.
Current methods may be time-consuming when modeling individual patient preferences.

Purpose of the Study:

To propose a user-customizable hybrid framework for targeted medical decision-making.
To balance the trade-off between reduced provider time and prediction accuracy through group customization.
To apply the framework to colorectal cancer screening for resource allocation optimization.

Main Methods:

A hybrid framework combining descriptive and predictive processes.
Descriptive process: Groups patients based on preference-based subjective features.
Predictive process: Matches new patients to groups using objective features.

Main Results:

The framework offers flexibility in adjusting group customization levels.
Demonstrated application to colorectal cancer screening decision-making.
The provider can select the desired trade-off based on acceptable error levels.

Conclusions:

The proposed framework enables efficient resource allocation in healthcare.
Group customization can shorten the process of modeling patient preferences.
The framework is adaptable to various medical decisions and provider-user scenarios.