{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T00:35:30Z","timestamp":1792802130862,"version":"4.5.0"},"reference-count":74,"publisher":"Elsevier BV","issue":"4","license":[{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T00:00:00Z","timestamp":1716163200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82341083"],"award-info":[{"award-number":["82341083"]}],"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":["82100119"],"award-info":[{"award-number":["82100119"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002920","name":"Research Grants Council, University Grants Committee","doi-asserted-by":"publisher","award":["17207722"],"award-info":[{"award-number":["17207722"]}],"id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012401","name":"Beijing Science and Technology Planning Project","doi-asserted-by":"publisher","award":["Z211100003521009"],"award-info":[{"award-number":["Z211100003521009"]}],"id":[{"id":"10.13039\/501100012401","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013365","name":"West China Hospital, Sichuan University","doi-asserted-by":"publisher","award":["ZYYC23027"],"award-info":[{"award-number":["ZYYC23027"]}],"id":[{"id":"10.13039\/501100013365","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2020YFG0473"],"award-info":[{"award-number":["2020YFG0473"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2022ZDZX0018"],"award-info":[{"award-number":["2022ZDZX0018"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004912","name":"Sichuan University","doi-asserted-by":"publisher","award":["2023SCUH0051"],"award-info":[{"award-number":["2023SCUH0051"]}],"id":[{"id":"10.13039\/501100004912","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["cell.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["The Innovation"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1016\/j.xinn.2024.100648","type":"journal-article","created":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T03:06:11Z","timestamp":1716347171000},"page":"100648","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":36,"title":["A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections"],"prefix":"10.1016","volume":"5","author":[{"given":"Jun","family":"Shao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiechao","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yizhou","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenyang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weimin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengdi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.xinn.2024.100648_bib1","doi-asserted-by":"crossref","first-page":"2100","DOI":"10.1016\/S0140-6736(24)00367-2","article-title":"Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021","volume":"403","year":"2024","journal-title":"Lancet"},{"key":"10.1016\/j.xinn.2024.100648_bib2","doi-asserted-by":"crossref","first-page":"2133","DOI":"10.1016\/S0140-6736(24)00757-8","volume":"403","year":"2024","journal-title":"Lancet"},{"issue":"1","key":"10.1016\/j.xinn.2024.100648_bib3","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/s41572-021-00259-0","article-title":"Pneumonia","volume":"7","author":"Torres","year":"2021","journal-title":"Nat. Rev. Dis. Primers"},{"key":"10.1016\/j.xinn.2024.100648_bib4","series-title":"Global Tuberculosis Report 2022","year":"2022"},{"issue":"10181","key":"10.1016\/j.xinn.2024.100648_bib5","doi-asserted-by":"crossref","first-page":"1642","DOI":"10.1016\/S0140-6736(19)30308-3","article-title":"Tuberculosis","volume":"393","author":"Furin","year":"2019","journal-title":"Lancet"},{"issue":"5","key":"10.1016\/j.xinn.2024.100648_bib6","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1056\/NEJMoa1500245","article-title":"Community-acquired pneumonia requiring hospitalization among U.S. adults","volume":"373","author":"Jain","year":"2015","journal-title":"N.\u00a0Engl. J. Med."},{"issue":"2","key":"10.1016\/j.xinn.2024.100648_bib7","doi-asserted-by":"crossref","first-page":"59","DOI":"10.2478\/jtim-2020-0010","article-title":"COVID-19 and tuberculosis","volume":"8","author":"Yang","year":"2020","journal-title":"J.\u00a0Transl. Int. Med."},{"issue":"1","key":"10.1016\/j.xinn.2024.100648_bib8","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1093\/pcmedi\/pbaa041","article-title":"Strategies for advanced personalized tuberculosis diagnosis: current technologies and clinical approaches","volume":"4","author":"Chen","year":"2021","journal-title":"Precis. Clin. Med."},{"issue":"10303","key":"10.1016\/j.xinn.2024.100648_bib9","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1016\/S0140-6736(21)00630-9","article-title":"Community-acquired pneumonia","volume":"398","author":"Aliberti","year":"2021","journal-title":"Lancet"},{"key":"10.1016\/j.xinn.2024.100648_bib10","doi-asserted-by":"crossref","DOI":"10.1136\/bmj-2021-065871","article-title":"Management of pneumonia in critically ill patients","volume":"375","author":"Cill\u00f3niz","year":"2021","journal-title":"BMJ"},{"issue":"17","key":"10.1016\/j.xinn.2024.100648_bib11","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1056\/NEJMra1312885","article-title":"Community-acquired pneumonia","volume":"371","author":"Musher","year":"2014","journal-title":"N.\u00a0Engl. J. Med."},{"issue":"8","key":"10.1016\/j.xinn.2024.100648_bib12","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1164\/rccm.201501-0017OC","article-title":"Early chest computed tomography scan to assist diagnosis and guide treatment decision for suspected community-acquired pneumonia","volume":"192","author":"Claessens","year":"2015","journal-title":"Am. J. Respir. Crit. Care Med."},{"issue":"9","key":"10.1016\/j.xinn.2024.100648_bib13","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1007\/s00134-022-06833-8","article-title":"Management and outcomes of pregnant women admitted to intensive care unit for severe pneumonia related to SARS-CoV-2 infection: the multicenter and international COVIDPREG study","volume":"48","author":"P\u00e9ju","year":"2022","journal-title":"Intensive Care Med."},{"issue":"7972","key":"10.1016\/j.xinn.2024.100648_bib14","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","article-title":"Large language models encode clinical knowledge","volume":"620","author":"Singhal","year":"2023","journal-title":"Nature"},{"issue":"2","key":"10.1016\/j.xinn.2024.100648_bib15","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1038\/s41571-021-00560-7","article-title":"Predicting cancer outcomes with radiomics and artificial intelligence in radiology","volume":"19","author":"Bera","year":"2022","journal-title":"Nat. Rev. Clin. Oncol."},{"issue":"6654","key":"10.1016\/j.xinn.2024.100648_bib16","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1126\/science.adh1114","article-title":"Leveraging artificial intelligence in the fight against infectious diseases","volume":"381","author":"Wong","year":"2023","journal-title":"Science"},{"issue":"4","key":"10.1016\/j.xinn.2024.100648_bib17","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.jinf.2023.07.006","article-title":"Artificial intelligence, machine learning and deep learning: Potential resources for the infection clinician","volume":"87","author":"Theodosiou","year":"2023","journal-title":"J.\u00a0Infect."},{"issue":"3","key":"10.1016\/j.xinn.2024.100648_bib18","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.pccm.2023.05.001","article-title":"Novel tools for early diagnosis and precision treatment based on artificial intelligence","volume":"1","author":"Shao","year":"2023","journal-title":"Chin. Med. J. Pulm. Crit. Care Med."},{"issue":"7639","key":"10.1016\/j.xinn.2024.100648_bib19","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"issue":"5","key":"10.1016\/j.xinn.2024.100648_bib20","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based deep learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"issue":"1","key":"10.1016\/j.xinn.2024.100648_bib21","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1038\/s41746-022-00648-z","article-title":"Development and validation of an abnormality-derived deep-learning diagnostic system for major respiratory diseases","volume":"5","author":"Wang","year":"2022","journal-title":"NPJ Digit. Med."},{"key":"10.1016\/j.xinn.2024.100648_bib22","doi-asserted-by":"crossref","DOI":"10.1016\/j.eclinm.2023.102270","article-title":"Intelligent prognosis evaluation system for stage I-III resected non-small-cell lung cancer patients on CT images: a multi-center study","volume":"65","author":"Zhang","year":"2023","journal-title":"eClinicalMedicine"},{"issue":"8","key":"10.1016\/j.xinn.2024.100648_bib23","doi-asserted-by":"crossref","DOI":"10.1016\/j.tranon.2021.101141","article-title":"Deep learning for predicting subtype classification and survival of lung adenocarcinoma on computed tomography","volume":"14","author":"Wang","year":"2021","journal-title":"Transl. Oncol."},{"key":"10.1016\/j.xinn.2024.100648_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.107290","article-title":"The gap in the thickness: estimating effectiveness of pulmonary nodule detection in thick- and thin-section CT images with 3D deep neural networks","volume":"229","author":"Guo","year":"2023","journal-title":"Comput. Methods Programs Biomed."},{"issue":"2","key":"10.1016\/j.xinn.2024.100648_bib25","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1038\/s41568-021-00408-3","article-title":"Harnessing multimodal data integration to advance precision oncology","volume":"22","author":"Boehm","year":"2022","journal-title":"Nat. Rev. Cancer"},{"key":"10.1016\/j.xinn.2024.100648_bib26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.semcancer.2023.02.006","article-title":"Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology","volume":"91","author":"Shao","year":"2023","journal-title":"Semin. Cancer Biol."},{"issue":"6","key":"10.1016\/j.xinn.2024.100648_bib27","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1038\/s41551-023-01045-x","article-title":"A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics","volume":"7","author":"Zhou","year":"2023","journal-title":"Nat. Biomed. Eng."},{"issue":"1","key":"10.1016\/j.xinn.2024.100648_bib28","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1038\/s41392-023-01640-z","article-title":"The long-term health outcomes, pathophysiological mechanisms and multidisciplinary management of long COVID","volume":"8","author":"Li","year":"2023","journal-title":"Signal Transduct. Target. Ther."},{"issue":"6","key":"10.1016\/j.xinn.2024.100648_bib29","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1038\/s41551-021-00704-1","article-title":"A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images","volume":"5","author":"Wang","year":"2021","journal-title":"Nat. Biomed. Eng."},{"issue":"2","key":"10.1016\/j.xinn.2024.100648_bib30","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1148\/radiol.2017162326","article-title":"Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks","volume":"284","author":"Lakhani","year":"2017","journal-title":"Radiology"},{"issue":"6","key":"10.1016\/j.xinn.2024.100648_bib31","doi-asserted-by":"crossref","first-page":"1423","DOI":"10.1016\/j.cell.2020.04.045","article-title":"Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography","volume":"181","author":"Zhang","year":"2020","journal-title":"Cell"},{"issue":"8","key":"10.1016\/j.xinn.2024.100648_bib32","doi-asserted-by":"crossref","first-page":"1224","DOI":"10.1038\/s41591-020-0931-3","article-title":"Artificial intelligence-enabled rapid diagnosis of patients with COVID-19","volume":"26","author":"Mei","year":"2020","journal-title":"Nat. Med."},{"issue":"1","key":"10.1016\/j.xinn.2024.100648_bib33","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1038\/s41746-021-00399-3","article-title":"CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images","volume":"4","author":"Javaheri","year":"2021","journal-title":"npj Digit. Med."},{"issue":"1","key":"10.1016\/j.xinn.2024.100648_bib34","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1148\/radiol.2020202439","article-title":"Automated assessment of COVID-19 reporting and data system and chest CT severity scores in patients suspected of having COVID-19 using artificial intelligence","volume":"298","author":"Lessmann","year":"2021","journal-title":"Radiology"},{"issue":"10","key":"10.1016\/j.xinn.2024.100648_bib35","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1038\/s41591-021-01506-3","article-title":"Federated learning for predicting clinical outcomes in patients with COVID-19","volume":"27","author":"Dayan","year":"2021","journal-title":"Nat. Med."},{"issue":"1","key":"10.1016\/j.xinn.2024.100648_bib36","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1038\/s41392-021-00527-1","article-title":"COVID-19 in early 2021: current status and looking forward","volume":"6","author":"Wang","year":"2021","journal-title":"Signal Transduct. Target. Ther."},{"issue":"1","key":"10.1016\/j.xinn.2024.100648_bib37","doi-asserted-by":"crossref","DOI":"10.3390\/jcm12010303","article-title":"Diagnostic accuracy of the artificial intelligence methods in medical imaging for pulmonary tuberculosis: a systematic review and meta-analysis","volume":"12","author":"Zhan","year":"2022","journal-title":"J. Clin. Med."},{"key":"10.1016\/j.xinn.2024.100648_bib38","article-title":"BERT: pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018","journal-title":"arXiv"},{"key":"10.1016\/j.xinn.2024.100648_bib39","article-title":"ALBERT: a lite BERT for self-supervised learning of language representations","author":"Lan","year":"2019","journal-title":"arXiv"},{"key":"10.1016\/j.xinn.2024.100648_bib40","series-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.xinn.2024.100648_bib41","series-title":"2021 IEEE\/CVF International Conference on Computer Vision","article-title":"Swin Transformer: Hierarchical vision Transformer using shifted windows","author":"Liu","year":"2021"},{"issue":"6","key":"10.1016\/j.xinn.2024.100648_bib42","article-title":"Broad auto-reactive IgM responses are common in critically ill patients, including those with COVID-19","volume":"2","author":"Wong","year":"2021","journal-title":"Cell Rep. Med."},{"issue":"7","key":"10.1016\/j.xinn.2024.100648_bib43","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1002\/ajh.25829","article-title":"Hematological findings and complications of COVID-19","volume":"95","author":"Terpos","year":"2020","journal-title":"Am. J. Hematol."},{"issue":"1","key":"10.1016\/j.xinn.2024.100648_bib44","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1186\/s13054-023-04378-w","article-title":"Serum-integrated omics reveal the host response landscape for severe pediatric community-acquired pneumonia","volume":"27","author":"Wang","year":"2023","journal-title":"Crit. Care"},{"issue":"10325","key":"10.1016\/j.xinn.2024.100648_bib45","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/S0140-6736(21)02724-0","article-title":"Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis","volume":"399","year":"2022","journal-title":"Lancet"},{"key":"10.1016\/j.xinn.2024.100648_bib46","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106567","article-title":"Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans","volume":"154","author":"Wu","year":"2023","journal-title":"Comput. Biol. Med."},{"issue":"6","key":"10.1016\/j.xinn.2024.100648_bib47","doi-asserted-by":"crossref","first-page":"4280","DOI":"10.1007\/s00330-022-09335-9","article-title":"AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study","volume":"33","author":"Meng","year":"2023","journal-title":"Eur. Radiol."},{"issue":"3","key":"10.1016\/j.xinn.2024.100648_bib48","doi-asserted-by":"crossref","first-page":"382.e1","DOI":"10.1016\/j.cmi.2019.06.026","article-title":"Community-acquired pneumonia in the emergency department: an algorithm to facilitate diagnosis and guide chest CT scan indication","volume":"26","author":"Loubet","year":"2020","journal-title":"Clin. Microbiol. Infect."},{"issue":"10","key":"10.1016\/j.xinn.2024.100648_bib49","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1016\/S2589-7500(20)30199-0","article-title":"Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation","volume":"2","author":"Wang","year":"2020","journal-title":"Lancet Digit. Health"},{"issue":"5","key":"10.1016\/j.xinn.2024.100648_bib50","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1093\/cid\/ciy967","article-title":"Development and validation of a deep learning-based automatic detection algorithm for active pulmonary tuberculosis on chest radiographs","volume":"69","author":"Hwang","year":"2019","journal-title":"Clin. Infect. Dis."},{"issue":"1","key":"10.1016\/j.xinn.2024.100648_bib51","doi-asserted-by":"crossref","first-page":"4080","DOI":"10.1038\/s41467-020-17971-2","article-title":"Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets","volume":"11","author":"Harmon","year":"2020","journal-title":"Nat. Commun."},{"issue":"1","key":"10.1016\/j.xinn.2024.100648_bib52","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1007\/s11547-022-01580-8","article-title":"Development and validation of a 3D-convolutional neural network model based on chest CT for differentiating active pulmonary tuberculosis from community-acquired pneumonia","volume":"128","author":"Han","year":"2023","journal-title":"Radiol. Med."},{"issue":"12","key":"10.1016\/j.xinn.2024.100648_bib53","doi-asserted-by":"crossref","first-page":"8869","DOI":"10.1007\/s00330-023-09833-4","article-title":"Pneumonia-Plus: a deep learning model for the classification of bacterial, fungal, and viral pneumonia based on CT tomography","volume":"33","author":"Wang","year":"2023","journal-title":"Eur. Radiol."},{"issue":"10","key":"10.1016\/j.xinn.2024.100648_bib54","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1016\/j.ccell.2022.09.012","article-title":"Artificial intelligence for multimodal data integration in oncology","volume":"40","author":"Lipkova","year":"2022","journal-title":"Cancer Cell"},{"issue":"7894","key":"10.1016\/j.xinn.2024.100648_bib55","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1038\/s41586-021-04278-5","article-title":"Multi-omic machine learning predictor of breast cancer therapy response","volume":"601","author":"Sammut","year":"2022","journal-title":"Nature"},{"issue":"7969","key":"10.1016\/j.xinn.2024.100648_bib56","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/s41586-023-06160-y","article-title":"Health system-scale language models are all-purpose prediction engines","volume":"619","author":"Jiang","year":"2023","journal-title":"Nature"},{"issue":"1","key":"10.1016\/j.xinn.2024.100648_bib57","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1038\/s41746-023-00838-3","article-title":"DeepBreath-automated detection of respiratory pathology from lung auscultation in 572 pediatric outpatients across 5 countries","volume":"6","author":"Heitmann","year":"2023","journal-title":"NPJ Digit. Med."},{"issue":"7956","key":"10.1016\/j.xinn.2024.100648_bib58","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1038\/s41586-023-05881-4","article-title":"Foundation models for generalist medical artificial intelligence","volume":"616","author":"Moor","year":"2023","journal-title":"Nature"},{"issue":"2","key":"10.1016\/j.xinn.2024.100648_bib59","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","article-title":"Multimodal machine learning: a survey and taxonomy","volume":"41","author":"Baltrusaitis","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.xinn.2024.100648_bib60","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.inffus.2018.09.012","article-title":"Machine learning for integrating data in biology and medicine: principles, practice, and opportunities","volume":"50","author":"Zitnik","year":"2019","journal-title":"Inf. Fusion"},{"issue":"2","key":"10.1016\/j.xinn.2024.100648_bib61","article-title":"Artificial intelligence for medicine: progress, challenges, and perspectives","volume":"1","author":"Huang","year":"2023","journal-title":"Innovat. Med."},{"issue":"1","key":"10.1016\/j.xinn.2024.100648_bib62","article-title":"Predicting illness severity and short-term outcomes of COVID-19: a retrospective cohort study in China","volume":"1","author":"Chen","year":"2020","journal-title":"Innovation"},{"issue":"11","key":"10.1016\/j.xinn.2024.100648_bib63","doi-asserted-by":"crossref","first-page":"2793","DOI":"10.1038\/s41591-023-02617-9","article-title":"Robust airway microbiome signatures in acute respiratory failure and hospital-acquired pneumonia","volume":"29","author":"Montassier","year":"2023","journal-title":"Nat. Med."},{"issue":"8","key":"10.1016\/j.xinn.2024.100648_bib64","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1164\/rccm.202308-1321OC","article-title":"The plasma lipidomic landscape in patients with sepsis due to community-acquired pneumonia","volume":"209","author":"Chouchane","year":"2024","journal-title":"Am. J. Respir. Crit. Care. Med."},{"issue":"12","key":"10.1016\/j.xinn.2024.100648_bib65","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1038\/s41551-020-00633-5","article-title":"Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning","volume":"4","author":"Ning","year":"2020","journal-title":"Nat. Biomed. Eng."},{"issue":"9","key":"10.1016\/j.xinn.2024.100648_bib66","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1016\/S2589-7500(22)00112-1","article-title":"Development of a multiomics model for identification of predictive biomarkers for COVID-19 severity: a retrospective cohort study","volume":"4","author":"Byeon","year":"2022","journal-title":"Lancet Digit. Health"},{"issue":"1","key":"10.1016\/j.xinn.2024.100648_bib67","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1038\/s41467-020-20657-4","article-title":"Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients","volume":"12","author":"Lassau","year":"2021","journal-title":"Nat. Commun."},{"issue":"5","key":"10.1016\/j.xinn.2024.100648_bib68","article-title":"Co-existence and co-infection of influenza A viruses and coronaviruses: public health challenges","volume":"3","author":"Yang","year":"2022","journal-title":"Innovation"},{"issue":"6","key":"10.1016\/j.xinn.2024.100648_bib69","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1016\/j.cell.2020.08.002","article-title":"Elevated calprotectin and abnormal myeloid cell subsets discriminate severe from mild COVID-19","volume":"182","author":"Silvin","year":"2020","journal-title":"Cell"},{"issue":"7865","key":"10.1016\/j.xinn.2024.100648_bib70","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1038\/s41586-021-03569-1","article-title":"A molecular single-cell lung atlas of lethal COVID-19","volume":"595","author":"Melms","year":"2021","journal-title":"Nature"},{"issue":"3","key":"10.1016\/j.xinn.2024.100648_bib71","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1016\/j.ygeno.2021.01.026","article-title":"Single-cell RNA sequencing reveals the sustained immune cell dysfunction in the pathogenesis of sepsis secondary to bacterial pneumonia","volume":"113","author":"Wang","year":"2021","journal-title":"Genomics"},{"issue":"23","key":"10.1016\/j.xinn.2024.100648_bib72","doi-asserted-by":"crossref","first-page":"5838","DOI":"10.1016\/j.cell.2021.10.023","article-title":"COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas","volume":"184","author":"Ren","year":"2021","journal-title":"Cell"},{"issue":"7847","key":"10.1016\/j.xinn.2024.100648_bib73","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1038\/s41586-020-03148-w","article-title":"Circuits between infected macrophages and T\u00a0cells in SARS-CoV-2 pneumonia","volume":"590","author":"Grant","year":"2021","journal-title":"Nature"},{"issue":"7","key":"10.1016\/j.xinn.2024.100648_bib74","doi-asserted-by":"crossref","first-page":"1814","DOI":"10.1038\/s41591-023-02437-x","article-title":"Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians","volume":"29","author":"Dvijotham","year":"2023","journal-title":"Nat. Med."}],"container-title":["The Innovation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666675824000869?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666675824000869?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T04:28:13Z","timestamp":1787372893000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2666675824000869"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7]]},"references-count":74,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["S2666675824000869"],"URL":"https:\/\/doi.org\/10.1016\/j.xinn.2024.100648","relation":{},"ISSN":["2666-6758"],"issn-type":[{"value":"2666-6758","type":"print"}],"subject":[],"published":{"date-parts":[[2024,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections","name":"articletitle","label":"Article Title"},{"value":"The Innovation","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.xinn.2024.100648","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Author(s).","name":"copyright","label":"Copyright"}],"article-number":"100648"}}