Multi-channel, convolutional attention based neural model for automated diagnostic coding of unstructured patient discharge summaries

• Published in: Future generation computer systems Vol. 118; pp. 374 - 391
• Main Authors: Mayya, Veena, S., Sowmya Kamath, Krishnan, Gokul S., Gangavarapu, Tushaar
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2021
• Subjects: Disease prediction; Explainability; Healthcare informatics; Interpretability; Predictive analytics; Unstructured text modeling; Healthcare informatics; Unstructured text modeling; Interpretability; Explainability; Predictive analytics; Disease prediction
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2021.01.013

Effective coding of patient records in hospitals is an essential requirement for epidemiology, billing, and managing insurance claims. The prevalent practice of manual coding, carried out by trained medical coders, is error-prone and time-consuming. Mitigating this labor-intensive process by developing diagnostic coding systems built on patients’ Electronic Medical Records (EMRs) is vital. However, developing nations with low digitization rates have limited availability of structured EMRs, thereby necessitating a need for systems that leverage unstructured data sources. Despite the rich clinical information available in such unstructured data, modeling them is complex, owing to the variety and sparseness of diagnostic codes, complex structural and temporal nature of summaries, and prolific use of medical jargon. This work proposes a context-attentive network to facilitate automatic diagnostic code assignment as a multi-label classification problem. The proposed model facilitates information aggregation across a patient’s discharge summary via multi-channel, variable-sized convolutional filters to extract multi-granular snippets. The attention mechanism enables selecting vital segments in those snippets that map to the clinical codes. The model’s superior performance underscores its effectiveness compared to the state-of-the-art on the MIMIC-III database. Additionally, experimental validation using the CodiEsp dataset exhibited the model’s interpretability and explainability. •Design of a multi-channel convolutional attention network to model raw clinical data.•Extracting rich patient-specific information from unstructured discharge summaries.•Eliminating dependency on structured EMRs by utilizing clinical text.•Enhancing the model interpretability to facilitate explainable intelligent healthcare.