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DASS Good: Explainable Data Mining of Spatial Cohort Data.

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This study introduces DASS, a system for developing clinical machine learning models using spatial data. It combines human expertise with AI to predict radiotherapy side effects in head and neck cancer patients.

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Area of Science:

  • Clinical informatics
  • Machine learning in healthcare
  • Radiotherapy research

Background:

  • Clinical machine learning models are challenging to develop with spatial data, such as radiation dose distributions.
  • Predicting long-term toxicities from radiotherapy requires integrating complex spatial information.

Purpose of the Study:

  • To describe the co-design of a hybrid human-machine modeling system, DASS.
  • To support the development and validation of predictive models for radiotherapy-induced toxicities.
  • To augment domain knowledge with data mining for oncology applications.

Main Methods:

  • DASS system co-designed with oncology and data mining experts.
  • Incorporates human-in-the-loop visual steering and spatial data.
  • Utilizes explainable AI to combine domain knowledge with automatic data mining.

Main Results:

  • Demonstrated DASS with two clinical stratification models for head and neck cancer.
  • Successfully integrated spatial data and human expertise in model development.
  • Received positive feedback from domain experts on the system's utility.

Conclusions:

  • DASS facilitates the creation of applicable clinical machine learning models with spatial data.
  • Hybrid human-machine approach enhances predictive model development for radiotherapy.
  • Design lessons learned offer insights for future collaborative AI system development in medicine.