From explainable to interpretable deep learning for natural language processing in healthcare: How far from reality?-Reference-Cited by-同舟云学术

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From explainable to interpretable deep learning for natural language processing in healthcare: How far from reality?

Published:2024-12 Issue: Volume:24 Page:362-373
ISSN:2001-0370
Container-title:Computational and Structural Biotechnology Journal
language:en
Short-container-title:Computational and Structural Biotechnology Journal

Author:

Huang Guangming^ORCID,Li Yingya,Jameel Shoaib^ORCID,Long Yunfei^ORCID,Papanastasiou Giorgos

Funder

European Commission

Publisher

Elsevier BV

Reference103 articles.

1. Improving the generalizability of depression detection by leveraging clinical questionnaires;Nguyen,2022

2. Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review;Koleck;J Am Med Inform Assoc,2019

3. Can current explainability help provide references in clinical notes to support humans annotate medical codes?;Kim,2022

4. Section classification in clinical notes with multi-task transformers;Zhang,2022

5. How long is enough? Exploring the optimal intervals of long-range clinical note language modeling;Cahyawijaya,2022

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2. Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review;2024-08-09

3. Large Language Models in Healthcare and Medical Domain: A Review;Informatics;2024-08-07

4. Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and Challenges;Sensors;2024-08-04

5. Rethinking the implementation and application of the Benczur-Karger minimum cuts algorithm;International Journal of ADVANCED AND APPLIED SCIENCES;2024-07

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