{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:35:13Z","timestamp":1772120113596,"version":"3.50.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T00:00:00Z","timestamp":1750377600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T00:00:00Z","timestamp":1750377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Science Foundation,United States","award":["CNS-2318210"],"award-info":[{"award-number":["CNS-2318210"]}]},{"name":"National Science Foundation, United States","award":["IIS-2245920"],"award-info":[{"award-number":["IIS-2245920"]}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["R01CA258193"],"award-info":[{"award-number":["R01CA258193"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Healthc Inform Res"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Radiology report generation, translating radiological images into precise and clinically relevant description, may face the data imbalance challenge \u2014 medical tokens appear less frequently than regular tokens, and normal entries are significantly more than abnormal ones. However, very few studies consider the imbalance issues, not even with conjugate imbalance factors. In this study, we propose a\n                    <jats:bold>J<\/jats:bold>\n                    oint\n                    <jats:bold>Im<\/jats:bold>\n                    balance\n                    <jats:bold>A<\/jats:bold>\n                    daptation (\n                    <jats:italic>JIMA<\/jats:italic>\n                    ) model to promote task robustness by leveraging token and label imbalance. We employ a hard-to-easy learning strategy that mitigates overfitting to frequent labels and tokens, thereby encouraging the model to focus more on infrequent labels and clinical tokens. JIMA presents notable improvements (16.75\u201350.50% on average) across evaluation metrics on IU X-ray and MIMIC-CXR datasets. Our ablation analysis and human evaluations show the improvements mainly come from enhancing performance on infrequent tokens and abnormal radiological entries, which can also lead to more clinically accurate reports. While data imbalance (e.g., infrequent tokens and abnormal labels) can lead to the underperformance of radiology report generation, our imbalance learning strategy opens promising directions on how to encounter data imbalance by reducing overfitting on frequent patterns and underfitting on infrequent patterns.\n                  <\/jats:p>","DOI":"10.1007\/s41666-025-00205-9","type":"journal-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T12:26:46Z","timestamp":1750422406000},"page":"720-742","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Joint Imbalance Adaptation for Radiology Report Generation"],"prefix":"10.1007","volume":"9","author":[{"given":"Wang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangzeng","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuexin","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"I.-Chan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolei","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,20]]},"reference":[{"key":"205_CR1","doi-asserted-by":"publisher","unstructured":"Jing B, Xie P, Xing E (2018) On the automatic generation of medical imaging reports. In: Proceedings of the 56th annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp 2577\u20132586. Association for Computational Linguistics, Melbourne, Australia. https:\/\/doi.org\/10.18653\/v1\/P18-1240","DOI":"10.18653\/v1\/P18-1240"},{"key":"205_CR2","doi-asserted-by":"publisher","unstructured":"Jing B, Wang Z, Xing E (2019) Show, describe and conclude: on exploiting the structure information of chest X-ray reports. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 6570\u20136580. Association for Computational Linguistics, Florence, Italy. https:\/\/doi.org\/10.18653\/v1\/P19-1657","DOI":"10.18653\/v1\/P19-1657"},{"key":"205_CR3","doi-asserted-by":"publisher","unstructured":"Lovelace J, Mortazavi B (2020) Learning to generate clinically coherent chest X-ray reports. In: Findings of the association for computational linguistics: EMNLP 2020, pp 1235\u20131243. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.110","DOI":"10.18653\/v1\/2020.findings-emnlp.110"},{"key":"205_CR4","doi-asserted-by":"publisher","unstructured":"Tan B, Yang Z, Al-Shedivat M, Xing E, Hu Z (2021) Progressive generation of long text with pretrained language models. In: Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 4313\u20134324. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.341","DOI":"10.18653\/v1\/2021.naacl-main.341"},{"key":"205_CR5","doi-asserted-by":"crossref","unstructured":"Wang Z, Liu L, Wang L, Zhou L (2023) Metransformer: radiology report generation by transformer with multiple learnable expert tokens. In: Proceedings of the IEEE\/CVF conference on Computer Vision and Pattern Recognition (CVPR), pp 11558\u201311567. https:\/\/openaccess.thecvf.com\/content\/CVPR2023\/html\/Wang_METransformer_Radiology_Report_Generation_by_Transformer_With_Multiple_Learnable_Expert_CVPR_2023_paper.html","DOI":"10.1109\/CVPR52729.2023.01112"},{"key":"205_CR6","doi-asserted-by":"publisher","unstructured":"Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: common objects in context. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer vision \u2013 ECCV 2014, pp 740\u2013755. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"205_CR7","doi-asserted-by":"publisher","unstructured":"Nishino T, Ozaki R, Momoki Y, Taniguchi T, Kano R, Nakano N, Tagawa Y, Taniguchi M, Ohkuma T, Nakamura K (2020) Reinforcement learning with imbalanced dataset for data-to-text medical report generation. In: Findings of the association for computational linguistics: EMNLP 2020, pp 2223\u20132236. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.202","DOI":"10.18653\/v1\/2020.findings-emnlp.202"},{"key":"205_CR8","doi-asserted-by":"publisher","unstructured":"Yu H, Zhang Q (2022) Clinically coherent radiology report generation with imbalanced chest x-rays. In: 2022 IEEE International conference on Bioinformatics and Biomedicine (BIBM), pp 1781\u20131786. IEEE Computer Society, Los Alamitos, CA, USA. https:\/\/doi.org\/10.1109\/BIBM55620.2022.9994871","DOI":"10.1109\/BIBM55620.2022.9994871"},{"key":"205_CR9","doi-asserted-by":"publisher","unstructured":"Gu S, Zhang J, Meng F, Feng Y, Xie W, Zhou J, Yu D (2020) Token-level adaptive training for neural machine translation. In: Proceedings of the 2020 conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1035\u20131046. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.76","DOI":"10.18653\/v1\/2020.emnlp-main.76"},{"key":"205_CR10","unstructured":"Wu Y, Huang I-C, Huang X (2023) Token imbalance adaptation for radiology report generation. In: Mortazavi BJ, Sarker T, Beam A, Ho JC (eds) Proceedings of the conference on health, inference, and learning, vol 209, pp 72\u201385. PMLR, Boston, MA. https:\/\/proceedings.mlr.press\/v209\/wu23a.html"},{"key":"205_CR11","doi-asserted-by":"publisher","unstructured":"Li Q, Peng H, Li J, Xia C, Yang R, Sun L, Yu PS, He L (2022) A survey on text classification: from traditional to deep learning. ACM Trans Intell Syst Technol 13(2). https:\/\/doi.org\/10.1145\/3495162","DOI":"10.1145\/3495162"},{"issue":"2","key":"205_CR12","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","volume":"42","author":"T-Y Lin","year":"2020","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2020) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell 42(2):318\u2013327. https:\/\/doi.org\/10.1109\/TPAMI.2018.2858826","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"205_CR13","doi-asserted-by":"publisher","unstructured":"Delbrouck J-B, Chambon P, Bluethgen C, Tsai E, Almusa O, Langlotz C (2022) Improving the factual correctness of radiology report generation with semantic rewards. In: Findings of the association for computational linguistics: EMNLP 2022, pp 4348\u20134360. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates. https:\/\/doi.org\/10.18653\/v1\/2022.findings-emnlp.319","DOI":"10.18653\/v1\/2022.findings-emnlp.319"},{"key":"205_CR14","doi-asserted-by":"publisher","unstructured":"Bengio Y, Louradour J, Collobert R, Weston J (2009) Curriculum learning. In: Proceedings of the 26th annual International Conference on Machine Learning. ICML \u201909, pp 41\u201348. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/1553374.1553380","DOI":"10.1145\/1553374.1553380"},{"issue":"1","key":"205_CR15","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1038\/s41597-019-0322-0","volume":"6","author":"AEW Johnson","year":"2019","unstructured":"Johnson AEW, Pollard TJ, Berkowitz SJ, Greenbaum NR, Lungren MP, Deng C-Y, Mark RG, Horng S (2019) MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Sci Data 6(1):317. https:\/\/doi.org\/10.1038\/s41597-019-0322-0","journal-title":"Sci Data"},{"issue":"2","key":"205_CR16","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1093\/jamia\/ocv080","volume":"23","author":"D Demner-Fushman","year":"2015","unstructured":"Demner-Fushman D, Kohli MD, Rosenman MB, Shooshan SE, Rodriguez L, Antani S, Thoma GR, McDonald CJ (2015) Preparing a collection of radiology examinations for distribution and retrieval. J Am Med Inform Assoc 23(2):304\u2013310. https:\/\/doi.org\/10.1093\/jamia\/ocv080","journal-title":"J Am Med Inform Assoc"},{"key":"205_CR17","unstructured":"Endo M, Krishnan R, Krishna V, Ng AY, Rajpurkar P (2021) Retrieval-based chest x-ray report generation using a pre-trained contrastive language-image model. In: Roy S, Pfohl S, Rocheteau E, Tadesse GA, Oala L, Falck F, Zhou Y, Shen L, Zamzmi G, Mugambi P, Zirikly A, McDermott MBA, Alsentzer E (eds) Proceedings of machine learning for health, vol 158, pp 209\u2013219. PMLR, Virtual. https:\/\/proceedings.mlr.press\/v158\/endo21a.html"},{"key":"205_CR18","unstructured":"Jeong J, Tian K, Li A, Hartung S, Adithan S, Behzadi F, Calle J, Osayande D, Pohlen M, Rajpurkar P (2024) Multimodal image-text matching improves retrieval-based chest x-ray report generation. In: Oguz I, Noble J, Li X, Styner M, Baumgartner C, Rusu M, Heinmann T, Kontos D, Landman B, Dawant B (eds) Medical imaging with deep learning. proceedings of machine learning research, vol. 227, pp 978\u2013990. PMLR, Paris, France. https:\/\/proceedings.mlr.press\/v227\/jeong24a.html"},{"key":"205_CR19","doi-asserted-by":"publisher","unstructured":"Chen Z, Song Y, Chang T-H, Wan X (2020) Generating radiology reports via memory-driven transformer. In: Proceedings of the 2020 conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1439\u20131449. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.112","DOI":"10.18653\/v1\/2020.emnlp-main.112"},{"key":"205_CR20","doi-asserted-by":"publisher","unstructured":"Qin H, Song Y (2022) Reinforced cross-modal alignment for radiology report generation. In: Findings of the Association for Computational Linguistics: ACL 2022, pp 448\u2013458. Association for Computational Linguistics, Dublin, Ireland. https:\/\/doi.org\/10.18653\/v1\/2022.findings-acl.38","DOI":"10.18653\/v1\/2022.findings-acl.38"},{"key":"205_CR21","doi-asserted-by":"publisher","unstructured":"Kale K, Bhattacharyya P, Jadhav K (2023) Replace and report: NLP assisted radiology report generation. In: Findings of the Association for Computational Linguistics: ACL 2023, pp 10731\u201310742. Association for Computational Linguistics, Toronto, Canada. https:\/\/doi.org\/10.18653\/v1\/2023.findings-acl.683","DOI":"10.18653\/v1\/2023.findings-acl.683"},{"key":"205_CR22","doi-asserted-by":"publisher","unstructured":"Liu F, Ge S, Wu X (2021) Competence-based multimodal curriculum learning for medical report generation. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (Volume 1: Long Papers), pp 3001\u20133012. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.234","DOI":"10.18653\/v1\/2021.acl-long.234"},{"key":"205_CR23","doi-asserted-by":"publisher","unstructured":"Delbrouck J-B, Varma M, Chambon P, Langlotz C (2023) Overview of the RadSum23 shared task on multi-modal and multi-anatomical radiology report summarization. In: The 22nd workshop on Biomedical Natural Language Processing and BioNLP shared tasks, pp 478\u2013482. Association for Computational Linguistics, Toronto, Canada. https:\/\/doi.org\/10.18653\/v1\/2023.bionlp-1.45","DOI":"10.18653\/v1\/2023.bionlp-1.45"},{"key":"205_CR24","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357. https:\/\/doi.org\/10.1613\/jair.953","journal-title":"J Artif Intell Res"},{"key":"205_CR25","doi-asserted-by":"publisher","unstructured":"Yan A, He Z, Lu X, Du J, Chang E, Gentili A, McAuley J, Hsu C-N (2021) Weakly supervised contrastive learning for chest x-ray report generation. In: Findings of the association for computational linguistics: EMNLP 2021, pp 4009\u20134015. Association for Computational Linguistics, Punta Cana, Dominican Republic. https:\/\/doi.org\/10.18653\/v1\/2021.findings-emnlp.336","DOI":"10.18653\/v1\/2021.findings-emnlp.336"},{"key":"205_CR26","doi-asserted-by":"publisher","unstructured":"Chen Z, Shen Y, Song Y, Wan X (2021) Cross-modal memory networks for radiology report generation. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (Volume 1: Long Papers), pp 5904\u20135914. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2021.acl-long.459","DOI":"10.18653\/v1\/2021.acl-long.459"},{"key":"205_CR27","doi-asserted-by":"publisher","unstructured":"Papineni K, Roukos S, Ward T, Zhu W-J (2002) Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting on Association for Computational Linguistics. ACL \u201902, pp 311\u2013318. Association for Computational Linguistics, USA. https:\/\/doi.org\/10.3115\/1073083.1073135","DOI":"10.3115\/1073083.1073135"},{"key":"205_CR28","doi-asserted-by":"publisher","unstructured":"Yu S, Song J, Kim H, Lee S, Ryu W-J, Yoon S (2022) Rare tokens degenerate all tokens: improving neural text generation via adaptive gradient gating for rare token embeddings. In: Proceedings of the 60th annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp 29\u201345. Association for Computational Linguistics, Dublin, Ireland. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.3","DOI":"10.18653\/v1\/2022.acl-long.3"},{"issue":"9","key":"205_CR29","doi-asserted-by":"publisher","first-page":"4555","DOI":"10.1109\/TPAMI.2021.3069908","volume":"44","author":"X Wang","year":"2022","unstructured":"Wang X, Chen Y, Zhu W (2022) A survey on curriculum learning. IEEE Trans Pattern Anal Mach Intell 44(9):4555\u20134576. https:\/\/doi.org\/10.1109\/TPAMI.2021.3069908","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"205_CR30","unstructured":"Zhou T, Wang S, Bilmes J (2020) Curriculum learning by dynamic instance hardness. In: Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H (eds) Advances in neural information processing systems, vol 33, pp 8602\u20138613. Curran Associates, Inc., Virtual. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/62000dee5a05a6a71de3a6127a68778a-Paper.pdf"},{"key":"205_CR31","unstructured":"Jain S, Agrawal A, Saporta A, Truong S, Duong DN, Bui T, Chambon P, Zhang Y, Lungren M, Ng A, Langlotz C, Rajpurkar P, Rajpurkar P (2021) Radgraph: extracting clinical entities and relations from radiology reports. In: Vanschoren J, Yeung S (eds) Proceedings of the neural information processing systems track on datasets and benchmarks, vol 1. virtual. https:\/\/datasets-benchmarks-proceedings.neurips.cc\/paper\/2021\/file\/c8ffe9a587b126f152ed3d89a146b445-Paper-round1.pdf"},{"key":"205_CR32","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"205_CR33","unstructured":"Song X, Zhang X, Ji J, Liu Y, Wei P (2022) Cross-modal contrastive attention model for medical report generation. In: Proceedings of the 29th international conference on computational linguistics, pp 2388\u20132397. International Committee on Computational Linguistics, Gyeongju, Republic of Korea. https:\/\/aclanthology.org\/2022.coling-1.210"},{"key":"205_CR34","doi-asserted-by":"publisher","unstructured":"Smit A, Jain S, Rajpurkar P, Pareek A, Ng A, Lungren M (2020) Combining automatic labelers and expert annotations for accurate radiology report labeling using BERT. In: Proceedings of the 2020 conference on Empirical Methods in Natural Language Processing (EMNLP), pp 1500\u20131519. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.117","DOI":"10.18653\/v1\/2020.emnlp-main.117"},{"key":"205_CR35","doi-asserted-by":"crossref","unstructured":"Tanida T, M\u00fcller P, Kaissis G, Rueckert D (2023) Interactive and explainable region-guided radiology report generation. In: Proceedings of the IEEE\/CVF conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada, pp 7433\u20137442. https:\/\/openaccess.thecvf.com\/content\/CVPR2023\/papers\/Tanida_Interactive_and_Explainable_Region-Guided_Radiology_Report_Generation_CVPR_2023_paper.pdf","DOI":"10.1109\/CVPR52729.2023.00718"},{"key":"205_CR36","doi-asserted-by":"publisher","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2261\u20132269. https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"205_CR37","doi-asserted-by":"publisher","unstructured":"Irvin J, Rajpurkar P, Ko M, Yu Y, Ciurea-Ilcus S, Chute C, Marklund H, Haghgoo B, Ball R, Shpanskaya K, Seekins J, Mong DA, Halabi SS, Sandberg JK, Jones R, Larson DB, Langlotz CP, Patel BN, Lungren MP, Ng AY (2019) Chexpert: a large chest radiograph dataset with uncertainty labels and expert comparison. In: Proceedings of the AAAI conference on artificial intelligence, vol 33. Montr\u00e9al Canada, pp 590\u2013597. https:\/\/doi.org\/10.1609\/aaai.v33i01.3301590","DOI":"10.1609\/aaai.v33i01.3301590"},{"key":"205_CR38","doi-asserted-by":"publisher","unstructured":"Deka P, Jurek-Loughrey A et al (2022) Evidence extraction to validate medical claims in fake news detection. In: International conference on health information science, pp 3\u201315. https:\/\/doi.org\/10.1007\/978-3-031-20627-6_1 . Springer","DOI":"10.1007\/978-3-031-20627-6_1"},{"key":"205_CR39","doi-asserted-by":"publisher","unstructured":"Loper E, Bird S (2002) NLTK: the natural language toolkit. In: Proceedings of the ACL-02 workshop on effective tools and methodologies for teaching natural language processing and computational linguistics, pp 63\u201370. Association for Computational Linguistics, Philadelphia, Pennsylvania, USA.https:\/\/doi.org\/10.3115\/1118108.1118117","DOI":"10.3115\/1118108.1118117"},{"key":"205_CR40","unstructured":"Kingma D, Ba J (2015) Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR). San Diega, CA, USA. arXiv:1412.6980"},{"key":"205_CR41","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Ghahramani Z, Welling M, Cortes C, Lawrence N, Weinberger KQ (eds) Advances in neural information processing systems, vol 27. Curran Associates, Inc., Montr\u00e9al, Canada. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2014\/file\/5a18e133cbf9f257297f410bb7eca942-Paper.pdf"},{"key":"205_CR42","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) Pytorch: an imperative style, high-performance deep learning library. In: Advances in neural information processing systems 32, vol 32, pp 8024\u20138035. Curran Associates, Vancouver, Canada. https:\/\/proceedings.neurips.cc\/paper\/2019\/file\/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf"},{"key":"205_CR43","unstructured":"Denkowski M, Lavie A (2011) Meteor 1.3: automatic metric for reliable optimization and evaluation of machine translation systems. In: Proceedings of the sixth workshop on statistical machine translation, pp 85\u201391. Association for Computational Linguistics, Edinburgh, Scotland. https:\/\/aclanthology.org\/W11-2107"},{"key":"205_CR44","unstructured":"Lin C-Y (2004) ROUGE: a package for automatic evaluation of summaries. In: Text summarization branches out, pp 74\u201381. Association for Computational Linguistics, Barcelona, Spain. https:\/\/aclanthology.org\/W04-1013"}],"container-title":["Journal of Healthcare Informatics Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41666-025-00205-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41666-025-00205-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41666-025-00205-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T14:56:53Z","timestamp":1762786613000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41666-025-00205-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,20]]},"references-count":44,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["205"],"URL":"https:\/\/doi.org\/10.1007\/s41666-025-00205-9","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4837662\/v1","asserted-by":"object"}]},"ISSN":["2509-4971","2509-498X"],"issn-type":[{"value":"2509-4971","type":"print"},{"value":"2509-498X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,20]]},"assertion":[{"value":"31 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 June 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"All authors have reviewed and approved the final version of this manuscript and consent to its publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}