{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T06:29:35Z","timestamp":1776752975997,"version":"3.51.2"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T00:00:00Z","timestamp":1709683200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T00:00:00Z","timestamp":1709683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82000540"],"award-info":[{"award-number":["82000540"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81801886"],"award-info":[{"award-number":["81801886"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Suzhou Clinical Center of Digestive Diseases","award":["Szlcyxzx202101"],"award-info":[{"award-number":["Szlcyxzx202101"]}]},{"name":"Youth Program of Suzhou Health Committee","award":["KJXW2019001"],"award-info":[{"award-number":["KJXW2019001"]}]},{"name":"Scientific research project of Jiangsu Provincial Health Commission","award":["M2020013"],"award-info":[{"award-number":["M2020013"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-024-01066-1","type":"journal-article","created":{"date-parts":[[2024,3,6]],"date-time":"2024-03-06T18:01:51Z","timestamp":1709748111000},"page":"1312-1322","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Development and Validation of Multimodal Models to Predict the 30-Day Mortality of ICU Patients Based on Clinical Parameters and Chest X-Rays"],"prefix":"10.1007","volume":"37","author":[{"given":"Jiaxi","family":"Lin","sequence":"first","affiliation":[]},{"given":"Jin","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Minyue","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Yuxiu","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Liquan","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Chang","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Shiqi","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Jingwen","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Lu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xiaolin","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Chenqi","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Zhou","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Yao","family":"Wei","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0544-9248","authenticated-orcid":false,"given":"Jinzhou","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,6]]},"reference":[{"key":"1066_CR1","doi-asserted-by":"publisher","first-page":"2957","DOI":"10.1001\/jama.1993.03510240069035","volume":"270","author":"JR Le Gall","year":"1993","unstructured":"J. R. Le Gall, S. Lemeshow, F. Saulnier, A new Simplified Acute Physiology Score (SAPS II) based on a European\/North American multicenter study. JAMA 270:2957\u20132963, 1993","journal-title":"JAMA"},{"key":"1066_CR2","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1097\/00003246-198510000-00009","volume":"13","author":"WA Knaus","year":"1985","unstructured":"W. A. Knaus, E. A. Draper, D. P. Wagner, J. E. Zimmerman, APACHE II: a severity of disease classification system. Crit Care Med 13:818\u2013829, 1985","journal-title":"Crit Care Med"},{"key":"1066_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2022.100861","volume":"3","author":"MMH Shandhi","year":"2022","unstructured":"M. M. H. Shandhi, J. P. Dunn, AI in medicine: Where are we now and where are we going? Cell Rep Med 3, 100861, 2022","journal-title":"Cell Rep Med"},{"key":"1066_CR4","doi-asserted-by":"crossref","unstructured":"R.-E. Ko, J. Cho, M.-K. Shin, S. W. Oh, Y. Seong, J. Jeon, K. Jeon, S. Paik, J. S. Lim, S. J. Shin, J. B. Ahn, J. H. Park, S. C. You, H. S. Kim, Machine Learning-Based Mortality Prediction Model for Critically Ill Cancer Patients Admitted to the Intensive Care Unit (CanICU). Cancers (Basel) 15, 569, 2023","DOI":"10.3390\/cancers15030569"},{"key":"1066_CR5","doi-asserted-by":"publisher","first-page":"1567","DOI":"10.1093\/jamia\/ocac098","volume":"29","author":"H Tang","year":"2022","unstructured":"H. Tang, Z. Jin, J. Deng, Y. She, Y. Zhong, W. Sun, Y. Ren, N. Cao, C. Chen, Development and validation of a deep learning model to predict the survival of patients in ICU. J Am Med Inform Assoc 29:1567\u20131576, 2022","journal-title":"J Am Med Inform Assoc"},{"key":"1066_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.accpm.2022.101167","volume":"42","author":"E Ishii","year":"2022","unstructured":"E. Ishii, N. Nawa, S. Hashimoto, H. Shigemitsu, T. Fujiwara, Development, validation, and feature extraction of a deep learning model predicting in-hospital mortality using Japan\u2019s largest national ICU database: a validation framework for transparent clinical Artificial Intelligence (cAI) development. Anaesth Crit Care Pain Med 42, 101167, 2022","journal-title":"Anaesth Crit Care Pain Med"},{"key":"1066_CR7","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1038\/s41467-021-20910-4","volume":"12","author":"KH Goh","year":"2021","unstructured":"K. H. Goh, L. Wang, A. Y. K. Yeow, H. Poh, K. Li, J. J. L. Yeow, G. Y. H. Tan, Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nat Commun 12, 711, 2021","journal-title":"Nat Commun"},{"key":"1066_CR8","doi-asserted-by":"publisher","first-page":"7448","DOI":"10.3390\/molecules27217448","volume":"27","author":"KSF Azam","year":"2022","unstructured":"K. S. F. Azam, O. Ryabchykov, T. Bocklitz, A Review on Data Fusion of Multidimensional Medical and Biomedical Data. Molecules 27, 7448, 2022","journal-title":"Molecules"},{"key":"1066_CR9","doi-asserted-by":"crossref","unstructured":"S. R. Stahlschmidt, B. Ulfenborg, J. Synnergren, Multimodal deep learning for biomedical data fusion: a review. Brief Bioinform 23, bbab569, 2022","DOI":"10.1093\/bib\/bbab569"},{"key":"1066_CR10","unstructured":"L. Y, W. Fx, N. A, A review on machine learning principles for multi-view biological data integration. Briefings in bioinformatics 19, 2018"},{"key":"1066_CR11","doi-asserted-by":"crossref","unstructured":"G. Mirabnahrazam, D. Ma, C. Beaulac, S. Lee, K. Popuri, H. Lee, J. Cao, L. Wang, J. E. Galvin, M. F. Beg, Predicting Alzheimer\u2019s disease progression in healthy and MCI subjects using multi-modal deep learning approach. Alzheimers Dement 18 Suppl 2, e060949, 2022","DOI":"10.1002\/alz.060949"},{"key":"1066_CR12","doi-asserted-by":"crossref","unstructured":"Y. Yao, Y. Lv, L. Tong, Y. Liang, S. Xi, B. Ji, G. Zhang, L. Li, G. Tian, M. Tang, X. Hu, S. Li, J. Yang, ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data. Brief Bioinform 23, bbac448, 2022","DOI":"10.1093\/bib\/bbac448"},{"key":"1066_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107207","volume":"227","author":"I Guez","year":"2022","unstructured":"I. Guez, G. Focht, M.-L. C. Greer, R. Cytter-Kuint, L.-T. Pratt, D. A. Castro, D. Turner, A. M. Griffiths, M. Freiman, Development of a multimodal machine-learning fusion model to non-invasively assess ileal Crohn\u2019s disease endoscopic activity. Comput Methods Programs Biomed 227, 107207, 2022","journal-title":"Comput Methods Programs Biomed"},{"key":"1066_CR14","doi-asserted-by":"crossref","unstructured":"L. R. Soenksen, Y. Ma, C. Zeng, L. Boussioux, K. Villalobos Carballo, L. Na, H. M. Wiberg, M. L. Li, I. Fuentes, D. Bertsimas, Integrated multimodal artificial intelligence framework for healthcare applications. NPJ Digit Med 5, 149, 2022","DOI":"10.1038\/s41746-022-00689-4"},{"key":"1066_CR15","doi-asserted-by":"crossref","unstructured":"F. Khader, G. M\u00fcller-Franzes, T. Wang, T. Han, S. Tayebi Arasteh, C. Haarburger, J. Stegmaier, K. Bressem, C. Kuhl, S. Nebelung, J. N. Kather, D. Truhn, Multimodal Deep Learning for Integrating Chest Radiographs and Clinical Parameters: A Case for Transformers. Radiology 309, e230806, 2023","DOI":"10.1148\/radiol.230806"},{"key":"1066_CR16","doi-asserted-by":"publisher","first-page":"1136071","DOI":"10.3389\/fmolb.2023.1136071","volume":"10","author":"K Niu","year":"2023","unstructured":"K. Niu, K. Zhang, X. Peng, Y. Pan, N. Xiao, Deep multi-modal intermediate fusion of clinical record and time series data in mortality prediction. Front Mol Biosci 10, 1136071, 2023","journal-title":"Front Mol Biosci"},{"key":"1066_CR17","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1038\/s41467-020-20657-4","volume":"12","author":"N Lassau","year":"2021","unstructured":"N. Lassau, S. Ammari, E. Chouzenoux, H. Gortais, P. Herent, M. Devilder, S. Soliman, O. Meyrignac, M.-P. Talabard, J.-P. Lamarque, R. Dubois, N. Loiseau, P. Trichelair, E. Bendjebbar, G. Garcia, C. Balleyguier, M. Merad, A. Stoclin, S. Jegou, F. Griscelli, N. Tetelboum, Y. Li, S. Verma, M. Terris, T. Dardouri, K. Gupta, A. Neacsu, F. Chemouni, M. Sefta, P. Jehanno, I. Bousaid, Y. Boursin, E. Planchet, M. Azoulay, J. Dachary, F. Brulport, A. Gonzalez, O. Dehaene, J.-B. Schiratti, K. Schutte, J.-C. Pesquet, H. Talbot, E. Pronier, G. Wainrib, T. Clozel, F. Barlesi, M.-F. Bellin, M. G. B. Blum, Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients. Nat Commun 12, 634, 2021","journal-title":"Nat Commun"},{"key":"1066_CR18","doi-asserted-by":"crossref","unstructured":"F. Dipaola, M. Gatti, A. Giaj Levra, R. Men\u00e8, D. Shiffer, R. Faccincani, Z. Raouf, A. Secchi, P. Rovere Querini, A. Voza, S. Badalamenti, M. Solbiati, G. Costantino, V. Savevski, R. Furlan, Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study. Sci Rep 13, 10868, 2023","DOI":"10.1038\/s41598-023-37512-3"},{"key":"1066_CR19","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AEW Johnson","year":"2016","unstructured":"A. E. W. Johnson, T. J. Pollard, L. Shen, L.-W. H. Lehman, M. Feng, M. Ghassemi, B. Moody, P. Szolovits, L. A. Celi, R. G. Mark, MIMIC-III, a freely accessible critical care database. Sci Data 3, 160035, 2016","journal-title":"Sci Data"},{"key":"1066_CR20","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1038\/s41597-019-0322-0","volume":"6","author":"AEW Johnson","year":"2019","unstructured":"A. E. W. Johnson, T. J. Pollard, S. J. Berkowitz, N. R. Greenbaum, M. P. Lungren, C.-Y. Deng, R. G. Mark, S. Horng, MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Sci Data 6, 317, 2019","journal-title":"Sci Data"},{"key":"1066_CR21","doi-asserted-by":"publisher","unstructured":"G. Huang, Z. Liu, L. van der Maaten, K. Q. Weinberger, Densely Connected Convolutional Networks. arXiv arXiv:1608.06993 [Preprint] (2018). https:\/\/doi.org\/10.48550\/arXiv.1608.06993","DOI":"10.48550\/arXiv.1608.06993"},{"key":"1066_CR22","doi-asserted-by":"publisher","unstructured":"A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, H. Adam, MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv arXiv:1704.04861 [Preprint] (2017). https:\/\/doi.org\/10.48550\/arXiv.1704.04861","DOI":"10.48550\/arXiv.1704.04861"},{"key":"1066_CR23","doi-asserted-by":"publisher","unstructured":"C. Tan, F. Sun, T. Kong, W. Zhang, C. Yang, C. Liu, A Survey on Deep Transfer Learning. arXiv arXiv:1808.01974 [Preprint] (2018). https:\/\/doi.org\/10.48550\/arXiv.1808.01974","DOI":"10.48550\/arXiv.1808.01974"},{"key":"1066_CR24","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1038\/s41746-020-00341-z","volume":"3","author":"S-C Huang","year":"2020","unstructured":"S.-C. Huang, A. Pareek, S. Seyyedi, I. Banerjee, M. P. Lungren, Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. NPJ Digit Med 3, 136, 2020","journal-title":"NPJ Digit Med"},{"key":"1066_CR25","doi-asserted-by":"publisher","unstructured":"A. A. H. de Hond, I. M. J. Kant, M. Fornasa, G. Cin\u00e0, P. W. G. Elbers, P. J. Thoral, M. Sesmu Arbous, E. W. Steyerberg, Predicting Readmission or Death After Discharge From the ICU: External Validation and Retraining of a Machine Learning Model. Crit Care Med,\u00a0https:\/\/doi.org\/10.1097\/CCM.0000000000005758, 2022","DOI":"10.1097\/CCM.0000000000005758"},{"key":"1066_CR26","doi-asserted-by":"publisher","unstructured":"Q. Yao, M. Wang, Y. Chen, W. Dai, Y.-F. Li, W.-W. Tu, Q. Yang, Y. Yu, Taking Human out of Learning Applications: A Survey on Automated Machine Learning. arXiv arXiv:1810.13306 [Preprint] (2019). https:\/\/doi.org\/10.48550\/arXiv.1810.13306.","DOI":"10.48550\/arXiv.1810.13306"},{"key":"1066_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101822","volume":"104","author":"J Waring","year":"2020","unstructured":"J. Waring, C. Lindvall, R. Umeton, Automated machine learning: Review of the state-of-the-art and opportunities for healthcare. Artificial Intelligence in Medicine 104, 101822, 2020","journal-title":"Artificial Intelligence in Medicine"},{"key":"1066_CR28","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1007\/s00134-022-06708-y","volume":"48","author":"M Legrand","year":"2022","unstructured":"M. Legrand, A. Zarbock, Ten tips to optimize vasopressors use in the critically ill patient with hypotension. Intensive Care Med 48: 736\u2013739, 2022","journal-title":"Intensive Care Med"},{"key":"1066_CR29","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.1056\/NEJMra1208943","volume":"369","author":"J-L Vincent","year":"2013","unstructured":"J.-L. Vincent, D. De Backer, Circulatory shock. N Engl J Med 369:1726\u20131734, 2013","journal-title":"N Engl J Med"},{"key":"1066_CR30","doi-asserted-by":"crossref","unstructured":"L. Ortiz-Reyes, J. J. Patel, X. Jiang, A. Coz Yataco, A. G. Day, F. Shah, J. Zelten, M. Tamae-Kakazu, T. Rice, D. K. Heyland, Early versus delayed enteral nutrition in mechanically ventilated patients with circulatory shock: a nested cohort analysis of an international multicenter, pragmatic clinical trial. Crit Care 26, 173, 2022","DOI":"10.1186\/s13054-022-04047-4"},{"key":"1066_CR31","doi-asserted-by":"publisher","first-page":"7841","DOI":"10.2147\/IDR.S389161","volume":"15","author":"P Xie","year":"2022","unstructured":"P. Xie, W. Wang, M. Dong, A Predictive Model for 30-Day Mortality of Fungemia in ICUs. Infect Drug Resist 15:7841\u20137852, 2022","journal-title":"Infect Drug Resist"},{"key":"1066_CR32","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.994359","volume":"9","author":"S Peng","year":"2022","unstructured":"S. Peng, J. Huang, X. Liu, J. Deng, C. Sun, J. Tang, H. Chen, W. Cao, W. Wang, X. Duan, X. Luo, S. Peng, Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: A retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases. Front Cardiovasc Med 9, 994359, 2022","journal-title":"Front Cardiovasc Med"},{"key":"1066_CR33","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1186\/s12967-020-02620-5","volume":"18","author":"N Hou","year":"2020","unstructured":"N. Hou, M. Li, L. He, B. Xie, L. Wang, R. Zhang, Y. Yu, X. Sun, Z. Pan, K. Wang, Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost. J Transl Med 18, 462, 2020","journal-title":"J Transl Med"},{"key":"1066_CR34","doi-asserted-by":"publisher","first-page":"1726","DOI":"10.1007\/s00134-022-06868-x","volume":"48","author":"O Mousai","year":"2022","unstructured":"O. Mousai, L. Tafoureau, T. Yovell, H. Flaatten, B. Guidet, C. Jung, D. de Lange, S. Leaver, W. Szczeklik, J. Fjolner, P. V. van Heerden, L. Joskowicz, M. Beil, G. Hyams, S. Sviri, Clustering analysis of geriatric and acute characteristics in a cohort of very old patients on admission to ICU. Intensive Care Med 48: 1726\u20131735, 2022","journal-title":"Intensive Care Med"},{"key":"1066_CR35","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1007\/s00134-015-3934-7","volume":"41","author":"the multinational AKI-EPI study","year":"2015","unstructured":"E. A. J. Hoste, S. M. Bagshaw, R. Bellomo, C. M. Cely, R. Colman, D. N. Cruz, K. Edipidis, L. G. Forni, C. D. Gomersall, D. Govil, P. M. Honor\u00e9, O. Joannes-Boyau, M. Joannidis, A.-M. Korhonen, A. Lavrentieva, R. L. Mehta, P. Palevsky, E. Roessler, C. Ronco, S. Uchino, J. A. Vazquez, E. Vidal Andrade, S. Webb, J. A. Kellum, Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study. Intensive Care Med 41:1411\u20131423, 2015","journal-title":"Intensive Care Med"},{"key":"1066_CR36","doi-asserted-by":"crossref","unstructured":"P. Soda, N. C. D\u2019Amico, J. Tessadori, G. Valbusa, V. Guarrasi, C. Bortolotto, M. U. Akbar, R. Sicilia, E. Cordelli, D. Fazzini, M. Cellina, G. Oliva, G. Callea, S. Panella, M. Cariati, D. Cozzi, V. Miele, E. Stellato, G. Carrafiello, G. Castorani, A. Simeone, L. Preda, G. Iannello, A. Del Bue, F. Tedoldi, M. Al\u00ed, D. Sona, S. Papa, AIforCOVID: Predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study. Med Image Anal 74, 102216, 2021","DOI":"10.1016\/j.media.2021.102216"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01066-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01066-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01066-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T17:19:32Z","timestamp":1722878372000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-024-01066-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,6]]},"references-count":36,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["1066"],"URL":"https:\/\/doi.org\/10.1007\/s10278-024-01066-1","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,6]]},"assertion":[{"value":"6 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 February 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 March 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The research was approved by the ethics committee of the First Affiliated Hospital of Soochow University (2023-054).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"The authors declare that they have no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}