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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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Machine Learning Algorithms to Predict Breast Cancer Recurrence Using Structured and Unstructured Sources from

Lorena González-Castro¹, Marcela Chávez², Patrick Duflot²

¹School of Telecommunication Engineering, University of Vigo, 36310 Vigo, Spain.

|June 22, 2023

Summary

This summary is machine-generated.

Machine learning models can predict 5-year breast cancer recurrence. Structured clinical data, when used with XGBoost, provided the best prediction accuracy, outperforming combined data sources.

Keywords:

breast cancer machine learning patient stratification recurrence prediction secondary use structured data unstructured data

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Area of Science:

Oncology
Medical Informatics
Machine Learning

Background:

Breast cancer (BC) recurrence significantly impacts patient mortality.
Machine learning (ML) offers potential for improved patient risk stratification using healthcare data.
Combining structured and unstructured data sources is explored for enhanced BC recurrence prediction.

Purpose of the Study:

To evaluate the effectiveness of ML algorithms in predicting 5-year breast cancer recurrence.
To compare prediction performance using structured data, unstructured (free text) data, and a combination of both.
To identify the optimal data source and ML model for breast cancer recurrence risk stratification.

Main Methods:

Collected and preprocessed clinical data from 823 breast cancer patients.
Derived features from structured clinical information and unstructured free-text clinical notes.
Evaluated five ML algorithms, including XGBoost, for predicting 5-year recurrence.
Assessed model performance using precision, recall, F1-score, and AUROC.

Main Results:

The XGBoost model demonstrated the highest performance (AUROC = 0.807).
Structured data yielded the best prediction results, followed by unstructured data.
The combined dataset (structured and unstructured) performed the poorest.

Conclusions:

ML algorithms are valuable for breast cancer recurrence risk stratification and patient monitoring.
Structured clinical data provides superior performance for ML-based recurrence prediction.
Natural language processing approaches offer comparable results to structured data with potentially less preprocessing effort.