Multimodal Machine Learning for Automated ICD Coding
Abstract
This study presents a multimodal machine learning model to predict ICD-10 diagnostic codes. We developed separate machine learning models that can handle data from different modalities, including unstructured text, semi-structured text and structured tabular data. We further employed an ensemble method to integrate all modality-specific models to generate ICD-10 codes. Key evidence was also extracted to make our prediction more convincing and explainable. We used the Medical Information Mart for Intensive Care III (MIMIC -III) dataset to validate our approach. For ICD code prediction, our best-performing model (micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability, our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780 and 0.5002 respectively.
Cite
@article{arxiv.1810.13348,
title = {Multimodal Machine Learning for Automated ICD Coding},
author = {Keyang Xu and Mike Lam and Jingzhi Pang and Xin Gao and Charlotte Band and Piyush Mathur and Frank Papay and Ashish K. Khanna and Jacek B. Cywinski and Kamal Maheshwari and Pengtao Xie and Eric Xing},
journal= {arXiv preprint arXiv:1810.13348},
year = {2022}
}
Comments
Machine Learning for Healthcare 2019