Multimodal Machine Learning for Automated ICD Coding

• DOI: 10.48550/arxiv.1810.13348
• Publication Date: 2018-01-01
• Authors: Xu, Keyang; Lam, Mike; Pang, Jingzhi; Gao, Xin; Band, Charlotte; Mathur, Piyush; Papay, Frank; Khanna, Ashish K.; Cywinski, Jacek B.; Maheshwari, Kamal; Xie, Pengtao; Xing, Eric
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; 03 medical and health sciences; 0302 clinical medicine; Statistics - Machine Learning; Machine Learning (stat.ML); 3. Good health; Machine Learning (cs.LG)

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.<br/>Machine Learning for Healthcare 2019<br/>