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A Novel Method for Medical Predictive Models in Small Data Using Out-of-Distribution Data and Transfer Learning

Author

Listed:
  • Inyong Jeong

    (Department of Biomedical Informatics, Korea University College of Medicine, Seoul 02708, Republic of Korea)

  • Yeongmin Kim

    (Department of Biomedical Informatics, Korea University College of Medicine, Seoul 02708, Republic of Korea)

  • Nam-Jun Cho

    (Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Republic of Korea)

  • Hyo-Wook Gil

    (Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Republic of Korea)

  • Hwamin Lee

    (Department of Biomedical Informatics, Korea University College of Medicine, Seoul 02708, Republic of Korea)

Abstract

Applying deep learning to medical research with limited data is challenging. This study focuses on addressing this difficulty through a case study, predicting acute respiratory failure (ARF) in patients with acute pesticide poisoning. Commonly, out-of-distribution (OOD) data are overlooked during model training in the medical field. Our approach integrates OOD data and transfer learning (TL) to enhance model performance with limited data. We fine-tuned a pre-trained multi-layer perceptron model using OOD data, outperforming baseline models. Shapley additive explanation (SHAP) values were employed for model interpretation, revealing the key factors associated with ARF. Our study is pioneering in applying OOD and TL techniques to electronic health records to achieve better model performance in scenarios with limited data. Our research highlights the potential benefits of using OOD data for initializing weights and demonstrates that TL can significantly improve model performance, even in medical data with limited samples. Our findings emphasize the significance of utilizing context-specific information in TL to achieve better results. Our work has practical implications for addressing challenges in rare diseases and other scenarios with limited data, thereby contributing to the development of machine-learning techniques within the medical field, especially regarding health inequities.

Suggested Citation

  • Inyong Jeong & Yeongmin Kim & Nam-Jun Cho & Hyo-Wook Gil & Hwamin Lee, 2024. "A Novel Method for Medical Predictive Models in Small Data Using Out-of-Distribution Data and Transfer Learning," Mathematics, MDPI, vol. 12(2), pages 1-26, January.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:2:p:237-:d:1317149
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    References listed on IDEAS

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    1. Yeongmin Kim & Minsu Chae & Namjun Cho & Hyowook Gil & Hwamin Lee, 2022. "Machine Learning-Based Prediction Models of Acute Respiratory Failure in Patients with Acute Pesticide Poisoning," Mathematics, MDPI, vol. 10(24), pages 1-24, December.
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