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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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Outcome-driven Evaluation Metrics for Treatment Recommendation Systems.

Jing Mei¹, Haifeng Liu¹, Xiang Li¹

¹IBM Research - China.

Studies in Health Technology and Informatics

|May 21, 2015

Summary

Evaluating treatment recommendation systems requires new metrics due to limited ground truth. An outcome-driven approach using precision, recall, and accuracy showed guideline adherence leads to better patient outcomes.

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Area of Science:

Medical Informatics
Clinical Decision Support Systems
Health Services Research

Background:

Treatment recommendation systems offer clinical decision support, often integrated with Computerized Physician Order Entry (CPOE).
Quantifying the quality of these recommendations is crucial for physician acceptance.
Evaluating these systems is challenging due to partially observed ground truth.

Purpose of the Study:

To propose an outcome-driven evaluation methodology for treatment recommendation systems.
To introduce and define five statistical metrics (precision, recall, accuracy, relative risk, odds ratio) in a clinical context.
To compare the performance of knowledge-driven and data-driven treatment recommendation systems.

Main Methods:

Developed an outcome-driven evaluation methodology.
Applied five statistical metrics: precision, recall, accuracy, relative risk, and odds ratio.
Compared a knowledge-driven system (based on clinical guidelines) with a data-driven system (based on patient similarity).

Main Results:

Physicians show lower compliance with clinical guidelines.
Following guideline recommendations significantly increases the likelihood of positive patient outcomes.
The proposed metrics provide a quantitative comparison of different recommendation systems.

Conclusions:

An outcome-driven evaluation methodology is effective for assessing treatment recommendation systems.
Adherence to clinical guidelines, despite lower initial compliance, is associated with improved patient outcomes.
The developed metrics offer valuable insights into the performance and clinical utility of recommendation systems.