T ree MAN : Tree-enhanced Multimodal Attention Network for ICD Coding (2022)

Abstract: ICD coding is designed to assign the disease codes to electronic health records (EHRs) upon discharge, which is crucial for billing and clinical statistics. In an attempt to improve the effectiveness and efficiency of manual coding, many methods have been proposed to automatically predict ICD codes from clinical notes. However, most previous works ignore the decisive information contained in structured medical data in EHRs, which is hard to be captured from the noisy clinical notes. In this paper, we propose a Tree-enhanced Multimodal Attention Network (TreeMAN) to fuse tabular features and textual features into multimodal representations by enhancing the text representations with tree-based features via the attention mechanism. Tree-based features are constructed according to decision trees learned from structured multimodal medical data, which capture the decisive information about ICD coding. We can apply the same multi-label classifier from previous text models to the multimodal representations to predict ICD codes. Experiments on two MIMIC datasets show that our method outperforms prior state-of-the-art ICD coding approaches. The code is available at https://github.com/liu-zichen/TreeMAN .

## introduction the international classification of diseases (icd), maintained by the world health organization, is a hierarchical classification of codes representing diseases, injuries, and so on. icd codes have been used in diverse areas, including insurance reimbursement, epidemiology, and clinical research @xcite . in the hospital, when patients discharge, their electronic health records (ehrs) and all associated data are transferred to the information management department, where clinical coders manually assign automatic multimodal icd coding model clinical text item value medicine insulin … … hematocrit abnormal icd code disease name 250.13 type i diabetes mellitus 403.90 hypertensive renal disease 584.9 acute renal failure 585.3 chronic kidney disease … … item value heart rate 120 bpm … … o2 flow 15 l/min the appropriate icd codes using rigid icd coding guidelines after reviewing records ( @xcite . the manual code assignment is expensive, labor-intensive, and error-prone due to the large volume of medical record information and high professional requirements @xcite . since deep learning has achieved great success in lots of healthcare applications @xcite , many neural methods have been proposed to automate the icd coding process by researchers @xcite . recent works formulate automated icd coding as a multi-label document classification task, using clinical notes as model input, predicting coding with a multi-label classifier, and learning text features through word embedding techniques and neural networks such as rnns and cnns @xcite @xcite . to improve the code representation learning, researchers further leverage features of icd codes such as hierarchical structures @xcite and descriptions @xcite . however, most previous methods ignore structured medical data, including physiological data collected by medical sensors and medical record information such as prescriptions and microbiology test results in ehrs. the few methods that leverage structured data are either ensemble-based approaches @xcite or data-mining methods that discard semantic information @xcite . in this work, we argue that structured medical data can improve coding accuracy by enhancing semantic representations and providing more information because clinical notes are noisy and ambiguous. for example, there are many different types of insulin, like "insulin aspart" and "insulin glargine", which are often written the same in notes but clearly distinguished by generic sequence numbers (gsns) in medical records. considering different writing styles and polysemous abbreviations, predicting icd codes from clinical notes is more complicated. however, automatic multimodal icd coding (as figure 1 shown) is challenging for the following reasons: 1) medical data is naturally heterogeneous, with data types including numerical quantities, categorical values, and derived time series such as perioperative vital sign signals @xcite ; 2) the feature selection method needs to be designed especially for multi-icd codes as it's a multi-label classification task; 3) decisive information for a code in the long clinical note may be contained in short segments that are likely different for different codes @xcite . in this paper, we propose a novel tree-enhanced multimodal attention network named treeman to address the aforementioned problems. since it's hard to do feature engineering for structured medical data, we construct tree-based features from the structured medical data through decision trees that require little data preparation @xcite instead of manually crafting features based on medical knowledge. inspired by previous works @xcite , we represent the tree-based features by embedding vectors. taking the tree-based embeddings and text representations as input, treeman applies an attention mechanism to select relevant tree-based features for text representations and output fused multimodal representations that contain richer information to benefit the downstream classifier. however, our method has limitations in handling long-tailed labels as it is difficult to build a decision tree from less than 10 positive samples. ## method in this section, we first give an overview of our framework (section 3.1), and then detail the key module in our framework: the tree-enhanced multimodal attention network treeman (section 3.2). finally, we introduce the processing of structured medical data and decision tree learning (section 3.3). ## conclusion in this paper, we proposed a tree-based multimodal method for the icd coding task, which constructs tree-based features by decision trees learned from structured medical data and fuses the tree-based features and text representation by a novel tree-enhanced multimodal attention network (treeman). experimental results on two mimic datasets show that our method outperforms stateof-the-art methods. further ablation studies demonstrate that structured medical data and the attention mechanism in treeman have improved the performance. for future work, we plan to investigate the interpretability of our method since tree-based methods are naturally interpretable. we are also interested in exploring a generalized and robust way to construct the tree-based features to capture more generalized medical information from structured medical data.