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

Author:

Jeong Inyong1,Kim Yeongmin1ORCID,Cho Nam-Jun2,Gil Hyo-Wook2ORCID,Lee Hwamin1ORCID

Affiliation:

1. Department of Biomedical Informatics, Korea University College of Medicine, Seoul 02708, Republic of Korea

2. Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan 31151, 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.

Funder

MSIT (Ministry of Science and ICT), Korea

Basic Science Research Program of the National Research Foundation

ICAN (ICT Challenge and Advanced Network of HRD) program

IITP

Korea University

Publisher

MDPI AG

Subject

General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

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