Academic

Interpretable Multiple Myeloma Prognosis with Observational Medical Outcomes Partnership Data

arXiv:2603.20341v1 Announce Type: new Abstract: Machine learning (ML) promises better clinical decision-making, yet opaque model behavior limits the adoption in healthcare. We propose two novel regularization techniques for ensuring the interpretability of ML models trained on real-world data. In particular, we consider the prediction of five-year survival for multiple myeloma patients using clinical data from Helsinki University Hospital. To ensure the interpretability of the trained models, we use two alternative constructions for a penalty term used for regularization. The first one penalizes deviations from the predictions obtained from an interpretable logistic regression method with two manually chosen features. The second construction requires consistency of model predictions with the revised international staging system (R-ISS). We verify the usefulness of the proposed regularization techniques in numerical experiments using data from 812 patients. They achieve an accuracy up

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Salma Rachidi, Aso Bozorgpanah, Eric Fey, Alexander Jung · · 1 min read · 5 views
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arXiv:2603.20341v1 Announce Type: new Abstract: Machine learning (ML) promises better clinical decision-making, yet opaque model behavior limits the adoption in healthcare. We propose two novel regularization techniques for ensuring the interpretability of ML models trained on real-world data. In particular, we consider the prediction of five-year survival for multiple myeloma patients using clinical data from Helsinki University Hospital. To ensure the interpretability of the trained models, we use two alternative constructions for a penalty term used for regularization. The first one penalizes deviations from the predictions obtained from an interpretable logistic regression method with two manually chosen features. The second construction requires consistency of model predictions with the revised international staging system (R-ISS). We verify the usefulness of the proposed regularization techniques in numerical experiments using data from 812 patients. They achieve an accuracy up to 0.721 on a test set and SHAP values show that the models rely on the selected important features.

Executive Summary

This study proposes novel regularization techniques to ensure the interpretability of machine learning models in the context of multiple myeloma prognosis prediction. The authors utilize two alternative constructions for a penalty term used for regularization, which are designed to penalize deviations from interpretable logistic regression predictions or require consistency with the revised international staging system. The results demonstrate the effectiveness of the proposed techniques in numerical experiments using data from 812 patients, achieving an accuracy of up to 0.721 on a test set and highlighting the importance of selected features. The study contributes to the development of more transparent and explainable machine learning models in healthcare, which is crucial for clinical decision-making and patient trust.

Key Points

  • ▸ The study proposes novel regularization techniques to ensure the interpretability of machine learning models.
  • ▸ The authors utilize two alternative constructions for a penalty term used for regularization.
  • ▸ The results demonstrate the effectiveness of the proposed techniques in numerical experiments.

Merits

Strength of Interpretability

The proposed regularization techniques provide transparent and explainable machine learning models, which is essential for clinical decision-making and patient trust.

Accuracy and Reliability

The results demonstrate the effectiveness of the proposed techniques in achieving high accuracy (up to 0.721) and highlighting the importance of selected features.

Demerits

Limited Generalizability

The study's results may not be generalizable to other medical conditions or datasets, limiting the applicability of the proposed techniques.

Dependence on Data Quality

The accuracy and reliability of the proposed techniques depend on the quality and availability of the data, which may not always be the case in real-world medical settings.

Expert Commentary

This study makes a significant contribution to the development of interpretable machine learning models in healthcare. The proposed regularization techniques provide a novel and effective approach to ensuring the transparency and explainability of machine learning models. However, the study's limitations, such as limited generalizability and dependence on data quality, highlight the need for further research and development. The implications of the study are significant, and it has the potential to impact the field of medical decision-making and the development of AI systems in healthcare. As the use of machine learning models becomes increasingly prevalent in healthcare, the need for interpretable and reliable models is becoming more pressing.

Recommendations

  • ✓ Future studies should aim to develop and evaluate more advanced regularization techniques that can handle complex medical datasets.
  • ✓ Regulatory bodies should develop guidelines and standards for the development and deployment of interpretable machine learning models in healthcare.

Sources

Original: arXiv - cs.LG

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