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Explainable text-tabular models for predicting mortality risk in companion animals.

James Burton1, Sean Farrell2, Peter-John Mäntylä Noble3

  • 1Department of Computer Science, Durham University, Durham, UK. james.burton@durham.ac.uk.

Scientific Reports
|June 20, 2024
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Summary

This study introduces a multimodal masking framework to explain machine learning models using veterinary electronic health records. The framework enhances trust by identifying key risk factors for companion animal mortality, with PetBERT showing strong performance on free-text data.

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Area of Science:

  • Veterinary Medicine
  • Machine Learning
  • Artificial Intelligence

Background:

  • Explainability is crucial for clinical decision-making using machine learning.
  • Clinical datasets are often multimodal, including text and tabular data.
  • Existing frameworks struggle to provide comprehensive explanations across diverse data types.

Purpose of the Study:

  • To develop a multimodal masking framework extending SHapley Additive exPlanations (SHAP) to text and tabular data.
  • To identify risk factors for companion animal mortality in veterinary electronic health records (EHRs).
  • To ensure consistent feature treatment across unimodal and multimodal contexts.

Main Methods:

  • Developed a multimodal masking framework for SHAP.
  • Applied the framework to UK-based veterinary EHRs.
  • Evaluated five multimodality approaches, including PetBERT and BERT-base.
  • Analyzed feature importance for predicting companion animal mortality.

Main Results:

  • The best-performing method utilized PetBERT, a language model pre-trained on veterinary data.
  • PetBERT showed a greater engagement with free-text narratives compared to BERT-base's tabular data emphasis.
  • Identified specific words and phrases significantly influencing animal mortality predictions.
  • Highlighted PetBERT's proficiency with veterinary clinical nomenclature.

Conclusions:

  • The multimodal masking framework effectively explains machine learning models across diverse data modalities in veterinary medicine.
  • PetBERT demonstrates superior performance, particularly with free-text clinical notes, for mortality risk prediction.
  • Enhanced pre-training of language models on domain-specific data is beneficial for clinical applications.