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An Advanced Machine Learning Model for a Web-Based Artificial Intelligence-Based Clinical Decision Support System

Tai-Han Lin1, Hsing-Yi Chung1, Ming-Jr Jian1

  • 1Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.

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Summary
This summary is machine-generated.

This study developed an AI-CDSS using ChatGPT to predict breast cancer recurrence, achieving an AUC of 0.80 with the light gradient-boosting machine model. The tool enhances personalized treatment planning and patient involvement.

Keywords:
ChatGPTartificial intelligence–based clinical decision support systembreast cancer recurrencemachine learningpersonalized treatment planningpredictive model accuracy

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Area of Science:

  • Oncology
  • Artificial Intelligence
  • Machine Learning

Background:

  • Breast cancer recurrence prediction is a critical challenge in oncology.
  • Artificial intelligence clinical decision support systems (AI-CDSS) offer potential for improved prediction accuracy and accessibility.
  • ChatGPT integration aims to enhance AI-CDSS development and usability.

Purpose of the Study:

  • To develop and validate an advanced machine learning model for a web-based AI-CDSS.
  • To leverage ChatGPT for improved data preprocessing and model development in breast cancer recurrence prediction.
  • To enhance the accuracy and accessibility of breast cancer recurrence prediction tools.

Main Methods:

  • Utilized a dataset of 3577 breast cancer patients (2004-2016) from Tri-Service General Hospital.
  • Employed ChatGPT for data preprocessing tasks including categorization, binning, and encoding.
  • Trained and validated models using algorithms like light gradient-boosting machine, gradient boosting, and extreme gradient boosting, evaluating performance with AUC, accuracy, sensitivity, and F1-score.

Main Results:

  • The light gradient-boosting machine model achieved the highest performance with an Area Under the Curve (AUC) of 0.80.
  • Gradient boosting and extreme gradient boosting models also showed strong predictive capabilities.
  • The AI-CDSS web interface demonstrated effectiveness in clinical scenarios for personalized treatment planning.

Conclusions:

  • The AI-CDSS, enhanced by ChatGPT, represents a significant advancement in predicting breast cancer recurrence.
  • The system offers a more individualized and accessible approach for clinicians and patients.
  • Further validation in diverse clinical settings is recommended to confirm efficacy and broaden application.