Zobrazeno 1 - 10
of 5 003
pro vyhledávání: '"Justin, F"'
Autor:
Lysne, Nathan K, Niedermeyer, Justin F, Wilson, Andrew C, Slichter, Daniel H, Leibfried, Dietrich
Excess "micromotion" of trapped ions due to the residual radio frequency (rf) trapping field at their location is often undesirable and is usually carefully minimized. Here, we induce precise amounts of excess micromotion on individual ions by adjust
Externí odkaz:
http://arxiv.org/abs/2402.05857
Autor:
Jin, Qiao, Chen, Fangyuan, Zhou, Yiliang, Xu, Ziyang, Cheung, Justin M., Chen, Robert, Summers, Ronald M., Rousseau, Justin F., Ni, Peiyun, Landsman, Marc J, Baxter, Sally L., Al'Aref, Subhi J., Li, Yijia, Chen, Alex, Brejt, Josef A., Chiang, Michael F., Peng, Yifan, Lu, Zhiyong
Publikováno v:
npj Digital Medicine, 2024
Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily focused on the accuracy of multi-choice questions alone. Our study ex
Externí odkaz:
http://arxiv.org/abs/2401.08396
Autor:
Christopher J. McLouth, Benton Maglinger, Jacqueline A. Frank, Hunter S. Hazelwood, Jordan P. Harp, Will Cranford, Shivani Pahwa, Lila Sheikhi, David Dornbos, Amanda L. Trout, Ann M. Stowe, Justin F. Fraser, Keith R. Pennypacker
Publikováno v:
Journal of Neuroinflammation, Vol 21, Iss 1, Pp 1-10 (2024)
Abstract Introduction The Appalachia region of North America is known to have significant health disparities, specifically, worse risk factors and outcomes for stroke. Appalachians are more likely to have comorbidities related to stroke, such as diab
Externí odkaz:
https://doaj.org/article/235e0010af5e41dbb9753c84729af42d
Autor:
Qiao Jin, Fangyuan Chen, Yiliang Zhou, Ziyang Xu, Justin M. Cheung, Robert Chen, Ronald M. Summers, Justin F. Rousseau, Peiyun Ni, Marc J. Landsman, Sally L. Baxter, Subhi J. Al’Aref, Yijia Li, Alexander Chen, Josef A. Brejt, Michael F. Chiang, Yifan Peng, Zhiyong Lu
Publikováno v:
npj Digital Medicine, Vol 7, Iss 1, Pp 1-6 (2024)
Abstract Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily focused on the accuracy of multi-choice questions alone. Our
Externí odkaz:
https://doaj.org/article/59fddd1b59b84a58bdb3e77d502905d6
A human decision-maker benefits the most from an AI assistant that corrects for their biases. For problems such as generating interpretation of a radiology report given findings, a system predicting only highly likely outcomes may be less useful, whe
Externí odkaz:
http://arxiv.org/abs/2305.19339
Publikováno v:
AMIA 2022 Annual Symposium
This paper applies multiple machine learning (ML) algorithms to a dataset of de-identified COVID-19 patients provided by the COVID-19 Research Database. The dataset consists of 20,878 COVID-positive patients, among which 9,177 patients died in the ye
Externí odkaz:
http://arxiv.org/abs/2303.00517
Publikováno v:
AMIA 2023 Informatics Summit
This paper applies eXplainable Artificial Intelligence (XAI) methods to investigate the socioeconomic disparities in COVID patient mortality. An Extreme Gradient Boosting (XGBoost) prediction model is built based on a de-identified Austin area hospit
Externí odkaz:
http://arxiv.org/abs/2302.08605
Autor:
Paul E. Hanna, Tianqi Ouyang, Ismail Tahir, Nurit Katz‐Agranov, Qiyu Wang, Lea Mantz, Ian Strohbehn, Daiana Moreno, Destiny Harden, James E. Dinulos, Duru Cosar, Harish Seethapathy, Justin F. Gainor, Sachin J. Shah, Shruti Gupta, David E. Leaf, Florian J. Fintelmann, Meghan E. Sise
Publikováno v:
Journal of Cachexia, Sarcopenia and Muscle, Vol 15, Iss 3, Pp 1187-1198 (2024)
Abstract Background Creatinine‐based estimated glomerular filtration rate (eGFRCRE) may overestimate kidney function in patients with sarcopenia. While cystatin C‐based eGFR (eGFRCYS) is less affected by muscle mass, it may underestimate kidney f
Externí odkaz:
https://doaj.org/article/44af5f6f1ea54b2c8f09c7cb3eb582ac
Deep neural networks (DNNs) have rapidly become a \textit{de facto} choice for medical image understanding tasks. However, DNNs are notoriously fragile to the class imbalance in image classification. We further point out that such imbalance fragility
Externí odkaz:
http://arxiv.org/abs/2212.02675