Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing von Gu, Yu, Tinn, Robert, Cheng, Hao, Lucas, Michael, Usuyama, Naoto, Liu, Xiaodong, Naumann, Tristan, Gao, Jianfeng, Poon, Hoifung

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Natural Language Processing for EHR-Based Computational Phenotyping von Zeng, Zexian, Deng, Yu, Li, Xiaoyu, Naumann, Tristan, Luo, Yuan

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The role of machine learning in clinical research: transforming the future of evidence generation von Weissler, E. Hope, Naumann, Tristan, Andersson, Tomas, Ranganath, Rajesh, Elemento, Olivier, Luo, Yuan, Freitag, Daniel F., Benoit, James, Hughes, Michael C., Khan, Faisal, Slater, Paul, Shameer, Khader, Roe, Matthew, Hutchison, Emmette, Kollins, Scott H., Broedl, Uli, Meng, Zhaoling, Wong, Jennifer L., Curtis, Lesley, Huang, Erich, Ghassemi, Marzyeh

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Practical guidance on artificial intelligence for health-care data von Ghassemi, Marzyeh, Naumann, Tristan, Schulam, Peter, Beam, Andrew L, Chen, Irene Y, Ranganath, Rajesh

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Making big data useful for health care: a summary of the inaugural mit critical data conference von Badawi, Omar, Brennan, Thomas, Celi, Leo Anthony, Feng, Mengling, Ghassemi, Marzyeh, Ippolito, Andrea, Johnson, Alistair, Mark, Roger G, Mayaud, Louis, Moody, George, Moses, Christopher, Naumann, Tristan, Pimentel, Marco, Pollard, Tom J, Santos, Mauro, Stone, David J, Zimolzak, Andrew

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Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact von Balagopalan, Aparna, Baldini, Ioana, Celi, Leo Anthony, Gichoya, Judy, McCoy, Liam G, Naumann, Tristan, Shalit, Uri, van der Schaar, Mihaela, Wagstaff, Kiri L

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Correction to: The role of machine learning in clinical research: transforming the future of evidence generation von Weissler, E. Hope, Naumann, Tristan, Andersson, Tomas, Ranganath, Rajesh, Elemento, Olivier, Luo, Yuan, Freitag, Daniel F., Benoit, James, Hughes, Michael C., Khan, Faisal, Slater, Paul, Shameer, Khader, Roe, Matthew, Hutchison, Emmette, Kollins, Scott H., Broedl, Uli, Meng, Zhaoling, Wong, Jennifer L., Curtis, Lesley, Huang, Erich, Ghassemi, Marzyeh

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Fine-tuning large neural language models for biomedical natural language processing von Tinn, Robert, Cheng, Hao, Gu, Yu, Usuyama, Naoto, Liu, Xiaodong, Naumann, Tristan, Gao, Jianfeng, Poon, Hoifung

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A whole-slide foundation model for digital pathology from real-world data von Xu, Hanwen, Usuyama, Naoto, Bagga, Jaspreet, Zhang, Sheng, Rao, Rajesh, Naumann, Tristan, Wong, Cliff, Gero, Zelalem, González, Javier, Gu, Yu, Xu, Yanbo, Wei, Mu, Wang, Wenhui, Ma, Shuming, Wei, Furu, Yang, Jianwei, Li, Chunyuan, Gao, Jianfeng, Rosemon, Jaylen, Bower, Tucker, Lee, Soohee, Weerasinghe, Roshanthi, Wright, Bill J., Robicsek, Ari, Piening, Brian, Bifulco, Carlo, Wang, Sheng, Poon, Hoifung

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A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities von Zhao, Theodore, Gu, Yu, Yang, Jianwei, Usuyama, Naoto, Lee, Ho Hin, Kiblawi, Sid, Naumann, Tristan, Gao, Jianfeng, Crabtree, Angela, Abel, Jacob, Moung-Wen, Christine, Piening, Brian, Bifulco, Carlo, Wei, Mu, Poon, Hoifung, Wang, Sheng

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Making the Most of Text Semantics to Improve Biomedical Vision–Language Processing von Boecking, Benedikt, Usuyama, Naoto, Bannur, Shruthi, Castro, Daniel C., Schwaighofer, Anton, Hyland, Stephanie, Wetscherek, Maria, Naumann, Tristan, Nori, Aditya, Alvarez-Valle, Javier, Poon, Hoifung, Oktay, Ozan

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Toward a responsible future: recommendations for AI-enabled clinical decision support von Labkoff, Steven, Oladimeji, Bilikis, Kannry, Joseph, Solomonides, Anthony, Leftwich, Russell, Koski, Eileen, Joseph, Amanda L, Lopez-Gonzalez, Monica, Fleisher, Lee A, Nolen, Kimberly, Dutta, Sayon, Levy, Deborah R, Price, Amy, Barr, Paul J, Hron, Jonathan D, Lin, Baihan, Srivastava, Gyana, Pastor, Nuria, Luque, Unai Sanchez, Bui, Tien Thi Thuy, Singh, Reva, Williams, Tayler, Weiner, Mark G, Naumann, Tristan, Sittig, Dean F, Jackson, Gretchen Purcell, Quintana, Yuri

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Trends and Focus of Machine Learning Applications for Health Research von Beaulieu-Jones, Brett, Finlayson, Samuel G., Chivers, Corey, Chen, Irene, McDermott, Matthew, Kandola, Jaz, Dalca, Adrian V., Beam, Andrew, Fiterau, Madalina, Naumann, Tristan

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Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact von Aparna Balagopalan, Ioana Baldini, Leo Anthony Celi, Judy Gichoya, Liam G McCoy, Tristan Naumann, Uri Shalit, Mihaela van der Schaar, Kiri L Wagstaff

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Toward structuring real-world data: Deep learning for extracting oncology information from clinical text with patient-level supervision von Preston, Sam, Wei, Mu, Rao, Rajesh, Tinn, Robert, Usuyama, Naoto, Lucas, Michael, Gu, Yu, Weerasinghe, Roshanthi, Lee, Soohee, Piening, Brian, Tittel, Paul, Valluri, Naveen, Naumann, Tristan, Bifulco, Carlo, Poon, Hoifung

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Datathons and Software to Promote Reproducible Research von Celi, Leo Anthony, Lokhandwala, Sharukh, Montgomery, Robert, Moses, Christopher, Naumann, Tristan, Pollard, Tom, Spitz, Daniel, Stretch, Robert

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Compositional Zero-Shot Domain Transfer with Text-to-Text Models von Liu, Fangyu, Liu, Qianchu, Bannur, Shruthi, Pérez-García, Fernando, Usuyama, Naoto, Zhang, Sheng, Naumann, Tristan, Nori, Aditya, Poon, Hoifung, Alvarez-Valle, Javier, Oktay, Ozan, Hyland, Stephanie L.

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What's in a Note? Unpacking Predictive Value in Clinical Note Representations von Boag, Willie, Doss, Dustin, Naumann, Tristan, Szolovits, Peter

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A Review of Challenges and Opportunities in Machine Learning for Health von Ghassemi, Marzyeh, Naumann, Tristan, Schulam, Peter, Beam, Andrew L, Chen, Irene Y, Ranganath, Rajesh

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The role of machine learning in clinical research: transforming the future of evidence generation (vol 22, 537, 2021) von Weissler, E. Hope, Naumann, Tristan, Andersson, Tomas, Ranganath, Rajesh, Elemento, Olivier, Luo, Yuan, Freitag, Daniel F., Benoit, James, Hughes, Michael C., Khan, Faisal, Slater, Paul, Shameer, Khader, Roe, Matthew, Hutchison, Emmette, Kollins, Scott H., Broedl, Uli, Meng, Zhaoling, Wong, Jennifer L., Curtis, Lesley, Huang, Erich, Ghassemi, Marzyeh

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