{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T04:26:32Z","timestamp":1754108792775,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,1]],"date-time":"2021-08-01T00:00:00Z","timestamp":1627776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"CPRIT","award":["RR180012"],"award-info":[{"award-number":["RR180012"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1145\/3459930.3469551","type":"proceedings-article","created":{"date-parts":[[2021,7,30]],"date-time":"2021-07-30T18:30:10Z","timestamp":1627669810000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["DBNet"],"prefix":"10.1145","author":[{"given":"Kai","family":"Zhang","sequence":"first","affiliation":[{"name":"University of Texas Health Science Center at Houston"}]},{"given":"Xiaoqian","family":"Jiang","sequence":"additional","affiliation":[{"name":"University of Texas Health Science Center at Houston"}]},{"given":"Mahboubeh","family":"Madadi","sequence":"additional","affiliation":[{"name":"State University"}]},{"given":"Luyao","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Texas Health Science Center at Houston"}]},{"given":"Sean","family":"Savitz","sequence":"additional","affiliation":[{"name":"University of Texas Health Science Center at Houston"}]},{"given":"Shayan","family":"Shams","sequence":"additional","affiliation":[{"name":"University of Texas Health Science Center at Houston"}]}],"member":"320","published-online":{"date-parts":[[2021,8]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Tabnet: Attentive interpretable tabular learning. arXiv preprint arXiv:1908.07442","author":"Arik Sercan O","year":"2019","unstructured":"Sercan O Arik and Tomas Pfister. 2019. Tabnet: Attentive interpretable tabular learning. arXiv preprint arXiv:1908.07442 (2019)."},{"key":"e_1_3_2_1_2_1","volume-title":"Brian L Egleston, and Slobodan Vucetic.","author":"Bai Tian","year":"2018","unstructured":"Tian Bai, Ashis Kumar Chanda, Brian L Egleston, and Slobodan Vucetic. 2018. EHR phenotyping via jointly embedding medical concepts and words into a unified vector space. BMC medical informatics and decision making 18, 4 (2018), 15--25."},{"key":"e_1_3_2_1_3_1","volume-title":"Embedding complexity in the data representation instead of in the model: A case study using heterogeneous medical data. arXiv preprint arXiv:1802.04233","author":"Bajor Jacek M","year":"2018","unstructured":"Jacek M Bajor, Diego A Mesa, Travis J Osterman, and Thomas A Lasko. 2018. Embedding complexity in the data representation instead of in the model: A case study using heterogeneous medical data. arXiv preprint arXiv:1802.04233 (2018)."},{"key":"e_1_3_2_1_4_1","volume-title":"Luca Novelli, Mario Silva, Ferdinando Luca Lorini, Andrea Duca, et al.","author":"Balbi Maurizio","year":"2020","unstructured":"Maurizio Balbi, Anna Caroli, Andrea Corsi, Gianluca Milanese, Alessandra Surace, Fabiano Di Marco, Luca Novelli, Mario Silva, Ferdinando Luca Lorini, Andrea Duca, et al. 2020. Chest X-ray for predicting mortality and the need for ventilatory support in COVID-19 patients presenting to the emergency department. European radiology (2020), 1--14."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3097997"},{"key":"e_1_3_2_1_6_1","volume-title":"Recurrent neural networks for multivariate time series with missing values. Scientific reports 8, 1","author":"Che Zhengping","year":"2018","unstructured":"Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu. 2018. Recurrent neural networks for multivariate time series with missing values. Scientific reports 8, 1 (2018), 1--12."},{"key":"e_1_3_2_1_7_1","volume-title":"International Conference on Machine Learning. PMLR, 1174--1182","author":"Futoma Joseph","year":"2017","unstructured":"Joseph Futoma, Sanjay Hariharan, and Katherine Heller. 2017. Learning to detect sepsis with a multitask Gaussian process RNN classifier. In International Conference on Machine Learning. PMLR, 1174--1182."},{"key":"e_1_3_2_1_8_1","volume-title":"Machine Learning for Healthcare Conference. PMLR, 243--254","author":"Futoma Joseph","year":"2017","unstructured":"Joseph Futoma, Sanjay Hariharan, Katherine Heller, Mark Sendak, Nathan Brajer, Meredith Clement, Armando Bedoya, and Cara O'Brien. 2017. An improved multi-output gaussian process rnn with real-time validation for early sepsis detection. In Machine Learning for Healthcare Conference. PMLR, 243--254."},{"key":"e_1_3_2_1_9_1","volume-title":"Elevated levels of interleukin-6 and CRP predict the need for mechanical ventilation in COVID-19. Journal of Allergy and Clinical Immunology","author":"Herold Tobias","year":"2020","unstructured":"Tobias Herold, Vindi Jurinovic, Chiara Arnreich, Brian J Lipworth, Johannes C Hellmuth, Michael von Bergwelt-Baildon, Matthias Klein, and Tobias Weinberger. 2020. Elevated levels of interleukin-6 and CRP predict the need for mechanical ventilation in COVID-19. Journal of Allergy and Clinical Immunology (2020)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2015.7344858"},{"key":"e_1_3_2_1_11_1","volume-title":"Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems 30","author":"Ke Guolin","year":"2017","unstructured":"Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems 30 (2017), 3146--3154."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mehy.2020.110292"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Anoop R Kulkarni Ambarish M Athavale Ashima Sahni Shashvat Sukhal Abhimanyu Sahni Matthew Itteera Sara Zhukovsky Jane Vernik Mohan Abraham Amit Joshi et al. 2020. A DEEP LEARNING MODEL TO PREDICT THE NEED FOR MECHANICAL VENTILATION USING CHEST X-RAY IMAGES IN HOSPITALIZED COVID-19 PATIENTS. medRxiv (2020).","DOI":"10.1101\/2020.08.17.20176917"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103138"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Wenhua Liang Hengrui Liang Limin Ou Binfeng Chen Ailan Chen Caichen Li Yimin Li Weijie Guan Ling Sang Jiatao Lu et al. 2020. Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19. JAMA Internal Medicine (2020).","DOI":"10.1001\/jamainternmed.2020.2033"},{"key":"e_1_3_2_1_16_1","volume-title":"Christian Lino Cardenas, and Rajeev Malhotra","author":"Nicholson Christopher J","year":"2020","unstructured":"Christopher J Nicholson, Luke Wooster, Haakon H Sigurslid, Rebecca F Li, Wanlin Jiang, Wenjie Tian, Christian Lino Cardenas, and Rajeev Malhotra. 2020. Estimating Risk of Mechanical Ventilation and Mortality Among Adult COVID-19 patients Admitted to Mass General Brigham: The VICE and DICE Scores. medRxiv (2020)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Maulin Patel Junad Chowdhury Nicole Mills Robert Marron Andrew Gangemi Zachariah Dorey-Stein Ibraheem Yousef Lauren Tragesser Julie Giurintano Rohit Gupta et al. 2020. ROX Index Predicts Intubation in Patients with COVID-19 Pneumonia and Moderate to Severe Hypoxemic Respiratory Failure Receiving High Flow Nasal Therapy. medRxiv (2020).","DOI":"10.1101\/2020.06.30.20143867"},{"key":"e_1_3_2_1_18_1","unstructured":"Armand Ruiz. 2017. The 80\/20 data science dilemma. https:\/\/www.infoworld.com\/article\/3228245\/the-80-20-data-science-dilemma.html. Accessed: 2021-3-27."},{"key":"e_1_3_2_1_19_1","volume-title":"WS de Boer, GJM Herder, M Bonarius, OA Groot, E Jong, et al.","author":"Schalekamp S","year":"2020","unstructured":"S Schalekamp, M Huisman, RA van Dijk, MF Boomsma, PJ Freire Jorge, WS de Boer, GJM Herder, M Bonarius, OA Groot, E Jong, et al. 2020. Model-based prediction of critical illness in hospitalized patients with COVID-19. Radiology (2020), 202723."},{"key":"e_1_3_2_1_20_1","volume-title":"Interpolation-prediction networks for irregularly sampled time series. arXiv preprint arXiv:1909.07782","author":"Shukla Satya Narayan","year":"2019","unstructured":"Satya Narayan Shukla and Benjamin M Marlin. 2019. Interpolation-prediction networks for irregularly sampled time series. arXiv preprint arXiv:1909.07782 (2019)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Martin J Tobin. 2020. Basing respiratory management of COVID-19 on physiological principles.","DOI":"10.1164\/rccm.202004-1076ED"},{"key":"e_1_3_2_1_22_1","volume-title":"Representation Learning of EHR Data via Graph-Based Medical Entity Embedding. arXiv preprint arXiv:1910.02574","author":"Wu Tong","year":"2019","unstructured":"Tong Wu, Yunlong Wang, Yue Wang, Emily Zhao, Yilian Yuan, and Zhi Yang. 2019. Representation Learning of EHR Data via Graph-Based Medical Entity Embedding. arXiv preprint arXiv:1910.02574 (2019)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2875677"},{"key":"e_1_3_2_1_24_1","volume-title":"Real-time Prediction for Mechanical Ventilation in COVID-19 Patients using A Multi-task Gaussian Process Multi-objective Self-attention Network. arXiv preprint arXiv:2102.01147","author":"Zhang Kai","year":"2021","unstructured":"Kai Zhang, Siddharth Karanth, Bela Patel, Robert Murphy, and Xiaoqian Jiang. 2021. Real-time Prediction for Mechanical Ventilation in COVID-19 Patients using A Multi-task Gaussian Process Multi-objective Self-attention Network. arXiv preprint arXiv:2102.01147 (2021)."}],"event":{"name":"BCB '21: 12th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","sponsor":["SIGBIOM ACM Special Interest Group on Biomedical Computing"],"location":"Gainesville Florida","acronym":"BCB '21"},"container-title":["Proceedings of the 12th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459930.3469551","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3459930.3469551","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:17:43Z","timestamp":1750191463000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459930.3469551"}},"subtitle":["a novel deep learning framework for mechanical ventilation prediction using electronic health records"],"short-title":[],"issued":{"date-parts":[[2021,8]]},"references-count":24,"alternative-id":["10.1145\/3459930.3469551","10.1145\/3459930"],"URL":"https:\/\/doi.org\/10.1145\/3459930.3469551","relation":{},"subject":[],"published":{"date-parts":[[2021,8]]},"assertion":[{"value":"2021-08-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}