Zobrazeno 1 - 10
of 546
pro vyhledávání: '"Chen, Jonathan H"'
Autor:
Savage, Thomas, Ma, Stephen, Boukil, Abdessalem, Patel, Vishwesh, Rangan, Ekanath, Rodriguez, Ivan, Chen, Jonathan H
Large Language Model (LLM) fine tuning is underutilized in the field of medicine. Two of the most common methods of fine tuning are Supervised Fine Tuning (SFT) and Direct Preference Optimization (DPO), but there is little guidance informing users wh
Externí odkaz:
http://arxiv.org/abs/2409.12741
Autor:
Ashtari, Pooya, Behmandpoor, Pourya, Haredasht, Fateme Nateghi, Chen, Jonathan H., Patrinos, Panagiotis, Van Huffel, Sabine
Lossy image compression is essential for efficient transmission and storage. Traditional compression methods mainly rely on discrete cosine transform (DCT) or singular value decomposition (SVD), both of which represent image data in continuous domain
Externí odkaz:
http://arxiv.org/abs/2408.12691
Autor:
Jiang, Yixing, Irvin, Jeremy, Wang, Ji Hun, Chaudhry, Muhammad Ahmed, Chen, Jonathan H., Ng, Andrew Y.
Large language models are effective at few-shot in-context learning (ICL). Recent advancements in multimodal foundation models have enabled unprecedentedly long context windows, presenting an opportunity to explore their capability to perform ICL wit
Externí odkaz:
http://arxiv.org/abs/2405.09798
Autor:
Fleming, Scott L., Lozano, Alejandro, Haberkorn, William J., Jindal, Jenelle A., Reis, Eduardo P., Thapa, Rahul, Blankemeier, Louis, Genkins, Julian Z., Steinberg, Ethan, Nayak, Ashwin, Patel, Birju S., Chiang, Chia-Chun, Callahan, Alison, Huo, Zepeng, Gatidis, Sergios, Adams, Scott J., Fayanju, Oluseyi, Shah, Shreya J., Savage, Thomas, Goh, Ethan, Chaudhari, Akshay S., Aghaeepour, Nima, Sharp, Christopher, Pfeffer, Michael A., Liang, Percy, Chen, Jonathan H., Morse, Keith E., Brunskill, Emma P., Fries, Jason A., Shah, Nigam H.
The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care. However, evaluating LLMs on realistic
Externí odkaz:
http://arxiv.org/abs/2308.14089
One of the major barriers to using large language models (LLMs) in medicine is the perception they use uninterpretable methods to make clinical decisions that are inherently different from the cognitive processes of clinicians. In this manuscript we
Externí odkaz:
http://arxiv.org/abs/2308.06834
Autor:
Dash, Debadutta, Thapa, Rahul, Banda, Juan M., Swaminathan, Akshay, Cheatham, Morgan, Kashyap, Mehr, Kotecha, Nikesh, Chen, Jonathan H., Gombar, Saurabh, Downing, Lance, Pedreira, Rachel, Goh, Ethan, Arnaout, Angel, Morris, Garret Kenn, Magon, Honor, Lungren, Matthew P, Horvitz, Eric, Shah, Nigam H.
Despite growing interest in using large language models (LLMs) in healthcare, current explorations do not assess the real-world utility and safety of LLMs in clinical settings. Our objective was to determine whether two LLMs can serve information nee
Externí odkaz:
http://arxiv.org/abs/2304.13714
Autor:
Corbin, Conor K., Maclay, Rob, Acharya, Aakash, Mony, Sreedevi, Punnathanam, Soumya, Thapa, Rahul, Kotecha, Nikesh, Shah, Nigam H., Chen, Jonathan H.
Machine learning (ML) applications in healthcare are extensively researched, but successful translations to the bedside are scant. Healthcare institutions are establishing frameworks to govern and promote the implementation of accurate, actionable an
Externí odkaz:
http://arxiv.org/abs/2303.06269
When evaluating the performance of clinical machine learning models, one must consider the deployment population. When the population of patients with observed labels is only a subset of the deployment population (label selection), standard model per
Externí odkaz:
http://arxiv.org/abs/2209.09188
Autor:
Ouyang, David, Theurer, John, Stein, Nathan R., Hughes, J. Weston, Elias, Pierre, He, Bryan, Yuan, Neal, Duffy, Grant, Sandhu, Roopinder K., Ebinger, Joseph, Botting, Patrick, Jujjavarapu, Melvin, Claggett, Brian, Tooley, James E., Poterucha, Tim, Chen, Jonathan H., Nurok, Michael, Perez, Marco, Perotte, Adler, Zou, James Y., Cook, Nancy R., Chugh, Sumeet S., Cheng, Susan, Albert, Christine M.
Background. Pre-operative risk assessments used in clinical practice are limited in their ability to identify risk for post-operative mortality. We hypothesize that electrocardiograms contain hidden risk markers that can help prognosticate post-opera
Externí odkaz:
http://arxiv.org/abs/2205.03242
Autor:
Wong, Shuk-Ching, Yip, Cyril C.-Y., Chen, Jonathan H.-K., Yuen, Lithia L.-H., AuYeung, Christine H.-Y., Chan, Wan-Mui, Chu, Allen W.-H., Leung, Rhoda C.-Y., Ip, Jonathan D., So, Simon Y.-C., Yuen, Kwok-Yung, To, Kelvin K.-W., Cheng, Vincent C.-C.
Publikováno v:
In AJIC: American Journal of Infection Control April 2024 52(4):472-478
