Search Results - "Ham, Colby"

Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: a study on COVID-19 outcome prediction by Agarwal, Khushbu, Choudhury, Sutanay, Tipirneni, Sindhu, Mukherjee, Pritam, Ham, Colby, Tamang, Suzanne, Baker, Matthew, Tang, Siyi, Kocaman, Veysel, Gevaert, Olivier, Rallo, Robert, Reddy, Chandan K

“…Developing prediction models for emerging infectious diseases from relatively small numbers of cases is a critical need for improving pandemic preparedness…”
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F81. ELUCIDATING GENETIC AND ENVIRONMENTAL RISK FACTORS FOR ANTIPSYCHOTIC-INDUCED METABOLIC ADVERSE EFFECTS USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN THE MILLION VETERAN PROGRAM by Fanous, Ayman, Bacanu, Silviu-Alin, Mcmahon, Benjamin, Harvey, Philip, Kreyenbuhl, Julie, Marder, Stephen, Agarwal, Khushbu, Abbaszadegan, Hamed, Choudhury, Sutanay, Colby, Ham, Dhaubhadel, Sayera, Kolade, Beauty

“…Antipsychotic drugs are widely used to treat psychiatric disorders such as schizophrenia and bipolar disorder. However, they are known to have important…”
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54. PREDICTION OF ANTIPSYCHOTIC-INDUCED METABOLIC ADVERSE EFFECTS USING MULTIMODAL ARTIFICIAL INTELLIGENCE by Agarwal, Khushbu, Bacanu, Silviu-Alin, Choudhury, Sutanay, Madduri, Ravi, Ham, Colby, Zuniga, Alejandro, Dhaubhadel, Sayera, Harvey, Philip, Kreyenbuhl, Julie, Marder, Stephen, Abbaszadegan, Hamed, Kolade, Beauty, Mcmahon, Benjamin, Fanous, Ayman

“…Antipsychotic medications are a mainstay of pharmacotherapy of psychiatric illnesses but have been shown to cause considerable metabolic adverse effects such…”
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High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning by Dhaubhadel, Sayera, Ganguly, Kumkum, Ribeiro, Ruy M., Cohn, Judith D., Hyman, James M., Hengartner, Nicolas W., Kolade, Beauty, Singley, Anna, Bhattacharya, Tanmoy, Finley, Patrick, Levin, Drew, Thelen, Haedi, Cho, Kelly, Costa, Lauren, Ho, Yuk-Lam, Justice, Amy C., Pestian, John, Santel, Daniel, Zamora-Resendiz, Rafael, Crivelli, Silvia, Tamang, Suzanne, Martins, Susana, Trafton, Jodie, Oslin, David W., Beckham, Jean C., Kimbrel, Nathan A., McMahon, Benjamin H.

“…We present an ensemble transfer learning method to predict suicide from Veterans Affairs (VA) electronic medical records (EMR). A diverse set of base models…”
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F81. ELUCIDATING GENETIC AND ENVIRONMENTAL RISK FACTORS FOR ANTIPSYCHOTIC-INDUCED METABOLIC ADVERSE EFFECTS USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN THE MILLION VETERAN PROGRAM by Fanous, Ayman, Bacanu, Silviu-Alin, Mcmahon, Benjamin, Harvey, Philip, Kreyenbuhl, Julie, Marder, Stephen, Agarwal, Khushbu, Abbaszadegan, Hamed, Choudhury, Sutanay, Colby, Ham, Dhaubhadel, Sayera, Kolade, Beauty

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Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative by 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

“…While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute…”
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Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort CollaborativeResearch in context by 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

“…Background: While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after…”
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Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks by Wang, Ping, Agarwal, Khushbu, Ham, Colby, Choudhury, Sutanay, Reddy, Chandan K

“…Published in The Web Conference 2021 Representation learning methods for heterogeneous networks produce a low-dimensional vector embedding for each node that…”
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