IFFLC: An Integrated Framework of Feature Learning and Classification for Multiple Diagnosis Codes Assignment

The International Classification of Diseases, Version 9 (ICD-9) is often used to identify patients with specific diagnoses. However, certain conditions may not be accurate reflected by the ICD-9 codes, and diagnoses code assignments are complex time-consuming processes. Although there are existing methods for automotive disease diagnostic assignment techniques, they have limitations on the descriptiveness and interpretability of diseases based on features. More importantly, they ignored the importance of different features with respect to different diseases. To address the above-mentioned challenges, we propose a novel framework, namely IFFLC, which can select the most relevant features, learn disease-specific features for each disease, and perform multiple diagnosis codes' assignment. Specifically, we first develop feature selection based on disease information entropy to remove redundant and irrelevant features in both medical chart data and medical laboratory data. Then, we build a novel multiple diagnosis codes' classifier by learning the disease-specific features and exploring the intra-correlations between diseases. We employ an alternating direction method of multipliers to iteratively solve the related optimization problem. The extensive experiments on a real-world ICU database verify the superiority of the proposed method over state-of-the-art approaches.