Zobrazeno 1 - 10
of 166
pro vyhledávání: '"PRATAP, ABHISHEK"'
Autor:
Zhang, Yuezhou, Folarin, Amos A., Ranjan, Yatharth, Cummins, Nicholas, Rashid, Zulqarnain, Conde, Pauline, Stewart, Callum, Sun, Shaoxiong, Vairavan, Srinivasan, Matcham, Faith, Oetzmann, Carolin, Siddi, Sara, Lamers, Femke, Simblett, Sara, Wykes, Til, Mohr, David C., Haro, Josep Maria, Penninx, Brenda W. J. H., Narayan, Vaibhav A., Hotopf, Matthew, Dobson, Richard J. B., Pratap, Abhishek, consortium, RADAR-CNS
Prior research has shown that changes in seasons and weather can have a significant impact on depression severity. However, findings are inconsistent across populations, and the interplay between weather, behavior, and depression has not been fully q
Externí odkaz:
http://arxiv.org/abs/2404.11212
Autor:
Areán, Patricia A, Pratap, Abhishek, Hsin, Honor, Huppert, Tierney K, Hendricks, Karin E, Heagerty, Patrick J, Cohen, Trevor, Bagge, Courtney, Comtois, Katherine Anne
Publikováno v:
Journal of Medical Internet Research, Vol 23, Iss 5, p e27918 (2021)
BackgroundDespite decades of research to better understand suicide risk and to develop detection and prevention methods, suicide is still one of the leading causes of death globally. While large-scale studies using real-world evidence from electronic
Externí odkaz:
https://doaj.org/article/80f8d54d0d084b31b5aab2dfedde87e9
Publikováno v:
In Biological Psychiatry 15 October 2024 96(8):659-665
Autor:
Pratap, Abhishek, Neto, Elias Chaibub, Snyder, Phil, Stepnowsky, Carl, Elhadad, Noémie, Grant, Daniel, Mohebbi, Matthew H., Mooney, Sean, Suver, Christine, Wilbanks, John, Mangravite, Lara, Heagerty, Patrick, Arean, Pat, Omberg, Larsson
Digital technologies such as smartphones are transforming the way scientists conduct biomedical research using real-world data. Several remotely-conducted studies have recruited thousands of participants over a span of a few months. Unfortunately, th
Externí odkaz:
http://arxiv.org/abs/1910.01165
Akademický článek
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Akademický článek
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Autor:
Neto, Elias Chaibub, Pratap, Abhishek, Perumal, Thanneer M, Tummalacherla, Meghasyam, Bot, Brian M, Mangravite, Lara, Omberg, Larsson
Clinical machine learning applications are often plagued with confounders that can impact the generalizability and predictive performance of the learners. Confounding is especially problematic in remote digital health studies where the participants s
Externí odkaz:
http://arxiv.org/abs/1811.11920
Autor:
Neto, Elias Chaibub, Pratap, Abhishek, Perumal, Thanneer M, Tummalacherla, Meghasyam, Bot, Brian M, Trister, Andrew D, Friend, Stephen H, Mangravite, Lara, Omberg, Larsson
Recently, Saeb et al (2017) showed that, in diagnostic machine learning applications, having data of each subject randomly assigned to both training and test sets (record-wise data split) can lead to massive underestimation of the cross-validation pr
Externí odkaz:
http://arxiv.org/abs/1712.03120
Autor:
Neto, Elias Chaibub, Perumal, Thanneer M, Pratap, Abhishek, Bot, Brian M, Mangravite, Lara, Omberg, Larsson
In this work we provide a couple of contributions to the analysis of longitudinal data collected by smartphones in mobile health applications. First, we propose a novel statistical approach to disentangle personalized treatment and "time-of-the-day"
Externí odkaz:
http://arxiv.org/abs/1706.09574
Autor:
Halabi, Ramzi, Selvarajan, Rahavi, Lin, Zixiong, Herd, Calvin, Li, Xueying, Kabrit, Jana, Tummalacherla, Meghasyam, Chaibub Neto, Elias, Pratap, Abhishek
Publikováno v:
Sensors (14248220); Oct2024, Vol. 24 Issue 19, p6246, 21p