{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T15:10:06Z","timestamp":1752073806717,"version":"3.41.2"},"publisher-location":"New York, NY, USA","reference-count":15,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,12,14]]},"DOI":"10.1145\/3719384.3719398","type":"proceedings-article","created":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T14:44:03Z","timestamp":1752072243000},"page":"101-113","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["An Improved Infectious Disease Risk Prediction Model Based on Attention Mechanism"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5457-3240","authenticated-orcid":false,"given":"YingShuai","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Medical Information\/Library, Chinese Academy of Medical Sciences &amp; Peking Union Medical College, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4836-352X","authenticated-orcid":false,"given":"Yanli","family":"Wan","sequence":"additional","affiliation":[{"name":"Institute of Medical Information\/Library, Chinese Academy of Medical Sciences &amp; Peking Union Medical College, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6954-2653","authenticated-orcid":false,"given":"Qingkun","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Medical Information\/Library, Chinese Academy of Medical Sciences &amp; Peking Union Medical College, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2585-7251","authenticated-orcid":false,"given":"Xingyun","family":"Lei","sequence":"additional","affiliation":[{"name":"Institute of Medical Information\/Library, Chinese Academy of Medical Sciences &amp; Peking Union Medical College, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2046-1907","authenticated-orcid":false,"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Medical Information\/Library, Chinese Academy of Medical Sciences &amp; Peking Union Medical College, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8201-3742","authenticated-orcid":false,"given":"Guoqiang","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute of Medical Information\/Library, Chinese Academy of Medical Sciences &amp; Peking Union Medical College, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3750-0551","authenticated-orcid":false,"given":"Xiaoze","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Medical Information\/Library, Chinese Academy of Medical Sciences &amp; Peking Union Medical College, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2675-9384","authenticated-orcid":false,"given":"Hongpu","family":"Hu","sequence":"additional","affiliation":[{"name":"Institute of Medical Information\/Library, Chinese Academy of Medical Sciences &amp; Peking Union Medical College, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"key":"e_1_3_3_1_1_2","volume-title":"International health","author":"Mangla S","year":"2021","unstructured":"Mangla S, Pathak A K, Arshad M, et al. Short-term forecasting of the COVID-19 outbreak in India[J]. International health, 2021, 13(5): 410-420."},{"key":"e_1_3_3_1_2_2","volume-title":"Deep learning in public health: Comparative predictive models for COVID-19 case forecasting[J]. Plos one","author":"Tariq M U","year":"2024","unstructured":"Tariq M U, Ismail S B. Deep learning in public health: Comparative predictive models for COVID-19 case forecasting[J]. Plos one, 2024, 19(3): e0294289."},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.04.042"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","first-page":"123534","DOI":"10.1016\/j.eswa.2024.123534","article-title":"Heart disease prediction: Improved quantum convolutional neural network and enhanced features[J]","volume":"249","author":"Pitchal P","year":"2024","unstructured":"Pitchal P, Ponnusamy S, Soundararajan V. Heart disease prediction: Improved quantum convolutional neural network and enhanced features[J]. Expert Systems with Applications, 2024, 249: 123534.","journal-title":"Expert Systems with Applications"},{"key":"e_1_3_3_1_5_2","volume-title":"Collider bias undermines our understanding of COVID-19 disease risk and severity[J]. Nature communications","author":"Griffith G J","year":"2020","unstructured":"Griffith G J, Morris T T, Tudball M J, et al. Collider bias undermines our understanding of COVID-19 disease risk and severity[J]. Nature communications, 2020, 11(1): 5749."},{"key":"e_1_3_3_1_6_2","first-page":"68","article-title":"Identification and validation of an explainable prediction model of acute kidney injury with prognostic implications in critically ill children: a prospective multicenter cohort study[J]","author":"Hu J","year":"2024","unstructured":"Hu J, Xu J, Li M, et al. Identification and validation of an explainable prediction model of acute kidney injury with prognostic implications in critically ill children: a prospective multicenter cohort study[J]. EClinicalMedicine, 2024, 68.","journal-title":"EClinicalMedicine"},{"key":"e_1_3_3_1_7_2","volume-title":"Epidemiology-aware deep learning for infectious disease dynamics prediction[C]\/\/Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 2023: 4084-4088","author":"Liu M","unstructured":"Liu M, Liu Y, Liu J. Epidemiology-aware deep learning for infectious disease dynamics prediction[C]\/\/Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 2023: 4084-4088."},{"issue":"14","key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1056\/NEJMoa2206916","article-title":"Global effect of modifiable risk factors on cardiovascular disease and mortality[J]","volume":"389","author":"Global Cardiovascular Risk Consortium","year":"2023","unstructured":"Global Cardiovascular Risk Consortium. Global effect of modifiable risk factors on cardiovascular disease and mortality[J]. New England Journal of Medicine, 2023, 389(14): 1273-1285.","journal-title":"New England Journal of Medicine"},{"issue":"1","key":"e_1_3_3_1_9_2","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1186\/s13073-023-01245-9","article-title":"Polygenic risk scores for disease risk prediction in Africa: current challenges and future directions[J]","volume":"15","author":"Fatumo S","year":"2023","unstructured":"Fatumo S, Sathan D, Samtal C, et al. Polygenic risk scores for disease risk prediction in Africa: current challenges and future directions[J]. Genome Medicine, 2023, 15(1): 87.","journal-title":"Genome Medicine"},{"issue":"1","key":"e_1_3_3_1_10_2","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1186\/s12889-023-16236-z","article-title":"exposure risk perception using machine learning[J]","volume":"23","author":"Bakkeli N Z","year":"2023","unstructured":"Bakkeli N Z. Predicting COVID-19 exposure risk perception using machine learning[J]. BMC Public Health, 2023, 23(1): 1377.","journal-title":"BMC Public Health"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"crossref","first-page":"e46891","DOI":"10.2196\/46891","volume":"25","author":"Huang G","year":"2023","unstructured":"Huang G, Jin Q, Mao Y. Predicting the 5-year risk of nonalcoholic fatty liver disease using machine learning models: prospective cohort study[J]. Journal of Medical Internet Research, 2023, 25: e46891.","journal-title":"Journal of Medical Internet Research"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","first-page":"103518","DOI":"10.1016\/j.seta.2023.103518","article-title":"Research on the regional prediction model of urban raster infectious diseases based on deep learning[J]","volume":"60","author":"Han B","year":"2023","unstructured":"Han B, Mao Y, Liu Z, et al. Research on the regional prediction model of urban raster infectious diseases based on deep learning[J]. Sustainable Energy Technologies and Assessments, 2023, 60: 103518.","journal-title":"Sustainable Energy Technologies and Assessments"},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.106.682658"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11749-016-0481-7"},{"key":"e_1_3_3_1_15_2","volume-title":"Deep neural networks as scientific models[J]. Trends in cognitive sciences","author":"Cichy R M","year":"2019","unstructured":"Cichy R M, Kaiser D. Deep neural networks as scientific models[J]. Trends in cognitive sciences, 2019, 23(4): 305-317."}],"event":{"name":"AICCC 2024: 2024 the 7th Artificial Intelligence and Cloud Computing Conference","location":"Tokyo Japan","acronym":"AICCC 2024"},"container-title":["Proceedings of the 2024 7th Artificial Intelligence and Cloud Computing Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3719384.3719398","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T14:46:12Z","timestamp":1752072372000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3719384.3719398"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,14]]},"references-count":15,"alternative-id":["10.1145\/3719384.3719398","10.1145\/3719384"],"URL":"https:\/\/doi.org\/10.1145\/3719384.3719398","relation":{},"subject":[],"published":{"date-parts":[[2024,12,14]]},"assertion":[{"value":"2025-07-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}