Projekteja vuodessa
Abstrakti
Human coders assign standardized medical codes to clinical documents generated during patients’ hospitalization, which is error prone and labor intensive. Automated medical coding approaches have been developed using machine learning methods, such as deep neural networks. Nevertheless, automated medical coding is still challenging because of complex code association, noise in lengthy documents, and the imbalanced class problem. We propose a novel neural network, called the Multitask Balanced and Recalibrated Neural Network, to solve these issues. Significantly, the multitask learning scheme shares the relationship knowledge between different coding branches to capture code association. A recalibrated aggregation module is developed by cascading convolutional blocks to extract high-level semantic features that mitigate the impact of noise in documents. Also, the cascaded structure of the recalibrated module can benefit learning from lengthy notes. To solve the imbalanced class problem, we deploy focal loss to redistribute the attention on low- and high-frequency medical codes. Experimental results show that our proposed model outperforms competitive baselines on a real-world clinical dataset called the Medical Information Mart for Intensive Care (MIMIC-III).
Alkuperäiskieli | Englanti |
---|---|
Artikkeli | 17 |
Sivut | 1–20 |
Sivumäärä | 20 |
Julkaisu | ACM Transactions on Intelligent Systems and Technology |
Vuosikerta | 14 |
Numero | 1 |
DOI - pysyväislinkit | |
Tila | Julkaistu - 9 marrask. 2022 |
OKM-julkaisutyyppi | A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä |
Sormenjälki
Sukella tutkimusaiheisiin 'Multitask Balanced and Recalibrated Network for Medical Code Prediction'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.-
INTERVENE: International consortium for integrative genomics prediction
Kaski, S. (Vastuullinen tutkija)
01/01/2021 → 31/12/2025
Projekti: EU: Framework programmes funding
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DATALIT: Data Literacy for Responsible Decision-Making
Marttinen, P. (Vastuullinen tutkija), Ji, S. (Projektin jäsen), Gröhn, T. (Projektin jäsen), Honkamaa, J. (Projektin jäsen), Kumar, Y. (Projektin jäsen), Pöllänen, A. (Projektin jäsen), Ojala, F. (Projektin jäsen), Raj, V. (Projektin jäsen) & Tiwari, P. (Projektin jäsen)
01/10/2020 → 30/09/2023
Projekti: Academy of Finland: Strategic research funding
Laitteet
Lehtileikkeet
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Study Data from Aalto University Provide New Insights into Networks (Multitask Balanced and Recalibrated Network for Medical Code Prediction)
21/04/2023
1 kohde/ Medianäkyvyys
Lehdistö/media: Esiintyminen mediassa