Multitask Recalibrated Aggregation Network for Medical Code Prediction

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Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2021
Major/Subject
Mcode
Degree programme
Language
en
Pages
17
367-383
Series
Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 12978 LNAI
Abstract
Medical coding translates professionally written medical reports into standardized codes, which is an essential part of medical information systems and health insurance reimbursement. Manual coding by trained human coders is time-consuming and error-prone. Thus, automated coding algorithms have been developed, building especially on the recent advances in machine learning and deep neural networks. To solve the challenges of encoding lengthy and noisy clinical documents and capturing code associations, we propose a multitask recalibrated aggregation network. In particular, multitask learning shares information across different coding schemes and captures the dependencies between different medical codes. Feature recalibration and aggregation in shared modules enhance representation learning for lengthy notes. Experiments with a real-world MIMIC-III dataset show significantly improved predictive performance.
Description
| openaire: EC/H2020/101016775/EU//INTERVENE Funding Information: Acknowledgments. This work was supported by the Academy of Finland (grant 336033) and EU H2020 (grant 101016775). We acknowledge the computational resources provided by the Aalto Science-IT project. The authors wish to acknowledge CSC - IT Center for Science, Finland, for computational resources. Publisher Copyright: © 2021, The Author(s).
Keywords
Medical code prediction, Multitask learning, Recalibrated aggregation network
Other note
Citation
Sun , W , Ji , S , Cambria , E & Marttinen , P 2021 , Multitask Recalibrated Aggregation Network for Medical Code Prediction . in Y Dong , N Kourtellis , B Hammer & J A Lozano (eds) , Machine Learning and Knowledge Discovery in Databases : Applied Data Science Track - European Conference, ECML PKDD 2021, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 12978 LNAI , Springer , pp. 367-383 , European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases , Virtual, Online , 13/09/2021 . https://doi.org/10.1007/978-3-030-86514-6_23