Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality In Post COVID-19 Era von Isgut, Monica, Gloster, Logan, Choi, Katherine, Venugopalan, Janani, Wang, May D.

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Integrating Multi-omics Data with EHR for Precision Medicine Using Advanced Artificial Intelligence von Tong, Li, Shi, Wenqi, Isgut, Monica, Zhong, Yishan, Lais, Peter, Gloster, Logan, Sun, Jimin, Swain, Aniketh, Giuste, Felipe, Wang, May D.

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Identifying and characterizing disease subpopulations that most benefit from polygenic risk scores von Isgut, Monica, Giuste, Felipe, Gloster, Logan, Swain, Aniketh, Choi, Katherine, Hornback, Andrew, Deshpande, Shriprasad R., Wang, May D.

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Clinical and Genomic Evaluation of 207 Genetic Myopathies in the Indian Subcontinent von Chakravorty, Samya, Nallamilli, Babi Ramesh Reddy, Khadilkar, Satish Vasant, Singla, Madhu Bala, Bhutada, Ashish, Dastur, Rashna, Gaitonde, Pradnya Satish, Rufibach, Laura E, Gloster, Logan, Hegde, Madhuri

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eP168 - Advanced diagnostics and genotype-phenotype resolution using functional genomics in >500 neuromuscular and neurological disorder patients von Chakravorty, Samya, Gloster, Logan, Berger, Kiera, Verma, Sumit, Logan, Rachel, Wicklund, Matthew, Harms, Matthew, Mozaffar, Tahseen, Kimonis, Virginia, Hegde, Madhuri, Elson, Molly, Luke, Rebecca, Gibson, Greg, Arafat, Dalia

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Advanced diagnostics and genotype-phenotype resolution using functional genomics in >500 neuromuscular and neurological disorder patients von Chakravorty, Samya, Gloster, Logan, Berger, Kiera, Verma, Sumit, Logan, Rachel, Wicklund, Matthew, Harms, Matthew, Mozaffar, Tahseen, Kimonis, Virginia, Hegde, Madhuri, Elson, Molly, Luke, Rebecca, Gibson, Greg, Arafat, Dalia

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Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative von Loomba, Johanna, Pfaff, Emily, Mitchell, Elliot, Delong, Grant, Tariq, Adbul, Coyle, Jeremy, Mertens, Andrew, Hubbard, Alan, Gao, Jifan, Li, Ziyang, Holmes, John, Wong, Eric, Getzen, Emily, Al-Amery, Amera, Anand, Adit, Baghbanzadeh, Mahdi, Bangert, Bridget, Butzin-Dozier, Zachary, Chiang, Jeffrey, Dai, Zongyu, Deng, Kaiwen, Dinh, Dong, Fu, Sunyang, Gardner, Kelly, Ghalwash, Mohamed, Ham, Colby, Hanoudi, Samer, Hershkovich, Leeor, Huang, Jiani, Huang, Yu, Hur, Junguk, Ismail, Hamid, Izmirlian, Grant, Jang, Kuk, Kartoun, Uri, Krichevsky, Spencer, Larie, Dale, Lederer, Lauren, Liu, Feifan, Munia, Tamanna, Madhira, Vithal, Mansourifard, Farzaneh, Milo, Christopher, Musal, Rasim, Narain, Rohan, Peng, Le, Perkins, Diana, Poplavskiy, Dmytro, Pungitore, Sarah, Ravichandran, Sarangan, Reyes, Abel, Rouffa, Sean, Sawant, Sahil, Schiaffino, Melody, Sengupta, Saurav, Shalaev, Ruslan, Shi, Seraphina, Shinguyen, Linh, Strube, Jan, Sundararajan, Prathic, Tawiah, Edward, Walzer, Benjamin, Wang, Lu, Wu, Yifeng, Xia, Fangfang, Yin, Changyu, Zanaj, Martin, Zhao, Zixuan, Zhou, Andrea, Zhu, Yuanda, Bates, Benjamin, Beasley, Will, Bramante, Carolyn, Brown, Don, Burgoon, Penny, Buse, John, Fuentes, Rafael, Gabriel, Davera, Gagnier, Joel, Gersing, Kenneth, Girvin, Andrew, Hurley, Robert, Johnson, Steve, Koraishy, Farrukh, Kostka, Kristin, Mariona, Federico, Marti, Emily, Martin, Greg, Mehta, Hemalkumar, Miller, Robert, Northington, Lavance, O’Neil, Shawn, Patel, Brijesh, Patel, Rena, Portilla, Lili, Sokos, George, Suver, Christine, Walden, Anita, Walters, Kellie, Zhu, Richard

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Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort CollaborativeResearch in context von Timothy Bergquist, Johanna Loomba, Emily Pfaff, Fangfang Xia, Zixuan Zhao, Yitan Zhu, Elliot Mitchell, Biplab Bhattacharya, Gaurav Shetty, Tamanna Munia, Grant Delong, Adbul Tariq, Zachary Butzin-Dozier, Yunwen Ji, Haodong Li, Jeremy Coyle, Seraphina Shi, Rachael V. Philips, Andrew Mertens, Romain Pirracchio, Mark van der Laan, John M. Colford, Jr, Alan Hubbard, Jifan Gao, Guanhua Chen, Neelay Velingker, Ziyang Li, Yinjun Wu, Adam Stein, Jiani Huang, Zongyu Dai, Qi Long, Mayur Naik, John Holmes, Danielle Mowery, Eric Wong, Ravi Parekh, Emily Getzen, Jake Hightower, Jennifer Blase, Ataes Aggarwal, Joseph Agor, Amera Al-Amery, Oluwatobiloba Aminu, Adit Anand, Corneliu Antonescu, Mehak Arora, Sayed Asaduzzaman, Tanner Asmussen, Mahdi Baghbanzadeh, Frazier Baker, Bridget Bangert, Laila Bekhet, Biplab Bhattacharya, Jenny Blase, Zachary Butzin-Dozier, Brian Caffo, Hao Chang, Zeyuan Chen, Jiandong Chen, Jeffrey Chiang, Peter Cho, Robert Cockrell, Parker Combs, Jeremy Coyle, Ciara Crosby, Zongyu Dai, Ran Dai, Anseh Danesharasteh, Elif Yildirim, Grant Delong, Ryan Demilt, Kaiwen Deng, Sanjoy Dey, Rohan Dhamdhere, Andrew Dickson, Phoebe Dijour, Dong Dinh, Richard Dixon, Albi Domi, Souradeep Dutta, Mirna Elizondo, Zeynep Ertem, Solomon Feuerwerker, Danica Fliss, Jennifer Fowler, Sunyang Fu, Kelly Gardner, Neil Getty, Mohamed Ghalwash, Logan Gloster, Phil Greer, Yuanfang Guan, Colby Ham, Samer Hanoudi, Jeremy Harper, Nathaniel Hendrix, Leeor Hershkovich, Jake Hightower, Junjie Hu

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Clinical and genomic evaluation of 207 genetic myopathies in the Indian subcontinent von Chakravorty, Samya, Nallamilli, Babi, Khadilkar, Satish, Singla, Madhu Bala, Bhutada, Ashish, Dastur, Rashna, Gaitonde, Pradnya, Rufibach, Laura, Gloster, Logan, Hegde, Madhuri

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