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pro vyhledávání: '"Miller Timothy A"'
The success of multi-task learning can depend heavily on which tasks are grouped together. Naively grouping all tasks or a random set of tasks can result in negative transfer, with the multi-task models performing worse than single-task models. Thoug
Externí odkaz:
http://arxiv.org/abs/2410.12774
Autor:
Myers, Skatje, Miller, Timothy A., Gao, Yanjun, Churpek, Matthew M., Mayampurath, Anoop, Dligach, Dmitriy, Afshar, Majid
Objective: Applying large language models (LLMs) to the clinical domain is challenging due to the context-heavy nature of processing medical records. Retrieval-augmented generation (RAG) offers a solution by facilitating reasoning over large text sou
Externí odkaz:
http://arxiv.org/abs/2409.15163
Autor:
Gao, Yanjun, Myers, Skatje, Chen, Shan, Dligach, Dmitriy, Miller, Timothy A, Bitterman, Danielle, Churpek, Matthew, Afshar, Majid
The introduction of Large Language Models (LLMs) has advanced data representation and analysis, bringing significant progress in their use for medical questions and answering. Despite these advancements, integrating tabular data, especially numerical
Externí odkaz:
http://arxiv.org/abs/2408.11854
Autor:
Hon, Marc, Huber, Daniel, Li, Yaguang, Metcalfe, Travis S., Bedding, Timothy R., Ong, Joel, Chontos, Ashley, Rubenzahl, Ryan, Halverson, Samuel, García, Rafael A., Kjeldsen, Hans, Stello, Dennis, Hey, Daniel R., Campante, Tiago, Howard, Andrew W., Gibson, Steven R., Rider, Kodi, Roy, Arpita, Baker, Ashley D., Edelstein, Jerry, Smith, Chris, Fulton, Benjamin J., Walawender, Josh, Brodheim, Max, Brown, Matt, Chan, Dwight, Dai, Fei, Deich, William, Gottschalk, Colby, Grillo, Jason, Hale, Dave, Hill, Grant M., Holden, Bradford, Householder, Aaron, Isaacson, Howard, Ishikawa, Yuzo, Jelinsky, Sharon R., Kassis, Marc, Kaye, Stephen, Laher, Russ, Lanclos, Kyle, Lee, Chien-Hsiu, Lilley, Scott, McCarney, Ben, Miller, Timothy N., Payne, Joel, Petigura, Erik A., Poppett, Claire, Raffanti, Michael, Rockosi, Constance, Sanford, Dale, Schwab, Christian, Shaum, Abby P., Sirk, Martin M., Smith, Roger, Thorne, Jim, Valliant, John, Vandenberg, Adam, Wang, Shin Ywan, Wishnow, Edward, Wold, Truman, Yeh, Sherry, Baker, Ashley, Basu, Sarbani, Bedell, Megan, Cegla, Heather M., Crossfield, Ian, Dressing, Courtney, Dumusque, Xavier, Knutson, Heather, Mawet, Dimitri, O'Meara, John, Stefánsson, Guðmundur, Teske, Johanna, Vasisht, Gautam, Wang, Sharon Xuesong, Weiss, Lauren M., Winn, Joshua N., Wright, Jason T.
Asteroseismology of dwarf stars cooler than the Sun is very challenging due to the low amplitudes and rapid timescales of oscillations. Here, we present the asteroseismic detection of solar-like oscillations at 4-minute timescales ($\nu_{\mathrm{max}
Externí odkaz:
http://arxiv.org/abs/2407.21234
Autor:
Chen, Shan, Gallifant, Jack, Guevara, Marco, Gao, Yanjun, Afshar, Majid, Miller, Timothy, Dligach, Dmitriy, Bitterman, Danielle S.
Generative models have been showing potential for producing data in mass. This study explores the enhancement of clinical natural language processing performance by utilizing synthetic data generated from advanced language models. Promising results s
Externí odkaz:
http://arxiv.org/abs/2403.19511
Autor:
Chen, Shan, Guevara, Marco, Moningi, Shalini, Hoebers, Frank, Elhalawani, Hesham, Kann, Benjamin H., Chipidza, Fallon E., Leeman, Jonathan, Aerts, Hugo J. W. L., Miller, Timothy, Savova, Guergana K., Mak, Raymond H., Lustberg, Maryam, Afshar, Majid, Bitterman, Danielle S.
Documentation burden is a major contributor to clinician burnout, which is rising nationally and is an urgent threat to our ability to care for patients. Artificial intelligence (AI) chatbots, such as ChatGPT, could reduce clinician burden by assisti
Externí odkaz:
http://arxiv.org/abs/2310.17703
Autor:
Gao, Yanjun, Li, Ruizhe, Caskey, John, Dligach, Dmitriy, Miller, Timothy, Churpek, Matthew M., Afshar, Majid
Electronic Health Records (EHRs) and routine documentation practices play a vital role in patients' daily care, providing a holistic record of health, diagnoses, and treatment. However, complex and verbose EHR narratives overload healthcare providers
Externí odkaz:
http://arxiv.org/abs/2308.14321
Large language models (LLMs) like ChatGPT have excited scientists across fields; in medicine, one source of excitement is the potential applications of LLMs trained on electronic health record (EHR) data. But there are tough questions we must first a
Externí odkaz:
http://arxiv.org/abs/2309.12339
Autor:
Miller, Timothy N., Doel, Peter, Gutierrez, Gaston, Besuner, Robert, Brooks, David, Gallo, Giuseppe, Heetderks, Henry, Jelinsky, Patrick, Kent, Stephen M., Lampton, Michael, Levi, Michael, Liang, Ming, Meisner, Aaron, Sholl, Michael J., Silber, Joseph Harry, Sprayberry, David, Aguilar, Jessica Nicole, de la Macorra, Axel, Eisenstein, Daniel, Fanning, Kevin, Font-Ribera, Andreu, Gaztanaga, Enrique, Gontcho, Satya Gontcho A, Honscheid, Klaus, Jimenez, Jorge, Joyce, Dick, Kehoe, Robert, Kisner, Theodore, Kremin, Anthony, Landriau, Martin, Guillou, Laurent Le, Magneville, Christophe, Martini, Paul, Miquel, Ramon, Moustakas, John, Nie, Jundan, Percival, Will, Poppett, Claire, Prada, Francisco, Rossi, Graziano, Schlegel, David, Schubnell, Michael, Seo, Hee-Jong, Sharples, Ray, Tarle, Gregory, Vargas-Magana, Mariana, Zhou, Zhimin
The Dark Energy Spectroscopic Instrument (DESI) is currently measuring the spectra of 40\,million galaxies and quasars, the largest such survey ever made to probe the nature of cosmological dark energy. The 4-meter Mayall telescope at Kitt Peak Natio
Externí odkaz:
http://arxiv.org/abs/2306.06310
The BioNLP Workshop 2023 initiated the launch of a shared task on Problem List Summarization (ProbSum) in January 2023. The aim of this shared task is to attract future research efforts in building NLP models for real-world diagnostic decision suppor
Externí odkaz:
http://arxiv.org/abs/2306.05270