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The third ML4H symposium was held in person on December 10, 2023, in New Orleans, Louisiana, USA.
Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2006
Earlier work this paper cites.
Hospital strategies to engage physicians in quality improvement
Allison Liebhaber, Debra A Draper, and Genna R Cohen · 2009
Earlier work this paper cites.
Matching on the estimated propensity score
Alberto Abadie and Guido W Imbens · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
The data-pooling problem
Michael Mattioli · 2017
Earlier work this paper cites.
Using propensity score weighting to reduce selection bias in large-scale data sets
Crystal D Bishop, Walter L Leite, and Patricia A Snyder · 2018
Earlier work this paper cites.
Why is my classifier discriminatory?
Irene Chen, Fredrik D Johansson, and David Sontag · 2018
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Invariant causal prediction for nonlinear models
Christina Heinze-Deml, Jonas Peters, and Nicolai Meinshausen · 2018
Earlier work this paper cites.
Natural history of pain and disability among african–americans and whites with or at risk for knee osteoarthritis: A longitudinal study
ER Vina, D Ran, EL Ashbeck, and CK Kwoh · 2018
Earlier work this paper cites.
A snp panel for identification of dna and rna specimens
Soheil Yousefi, Tooba Abbassi-Daloii, Thirsa Kraaijenbrink, Martijn Vermaat, Hailiang Mei, Peter van ‘t Hof, Maarten van Iterson, Daria V Zhernakova, Annique Claringbould, Lude Franke, et al · 2018
Earlier work this paper cites.
Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study
John R Zech, Marcus A Badgeley, Manway Liu, Anthony B Costa, Joseph J Titano, and Eric Karl Oermann · 2018
Earlier work this paper cites.
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Impact of missing data on bias and precision when estimating change in patient-reported outcomes from a clinical registry
Olawale F Ayilara, Lisa Zhang, Tolulope T Sajobi, Richard Sawatzky, Eric Bohm, and Lisa M Lix · 2019
Earlier work this paper cites.
Feature robustness in non-stationary health records: caveats to deployable model performance in common clinical machine learning tasks
Bret Nestor, Matthew BA McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C Hughes, Anna Goldenberg, and Marzyeh Ghassemi · 2019
Earlier work this paper cites.
Dissecting racial bias in an algorithm used to manage the health of populations
Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan · 2019
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Deepsofa: a continuous acuity score for critically ill patients using clinically interpretable deep learning
Benjamin Shickel, Tyler J Loftus, Lasith Adhikari, Tezcan Ozrazgat-Baslanti, Azra Bihorac, and Parisa Rashidi · 2019
Earlier work this paper cites.
Multimodal transformer for unaligned multimodal language sequences
Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, J Zico Kolter, Louis-Philippe Morency, and Ruslan Salakhutdinov · 2019
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4 ways data is improving healthcare, 2019
WEF · 2019
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Do no harm: a roadmap for responsible machine learning for health care
Jenna Wiens, Suchi Saria, Mark Sendak, Marzyeh Ghassemi, Vincent X Liu, Finale Doshi-Velez, Kenneth Jung, Katherine Heller, David Kale, Mohammed Saeed, et al · 2019
Earlier work this paper cites.
Human-centered ai: The role of human-centered design research in the development of ai, 2020
Jan Auernhammer · 2020
Earlier work this paper cites.
A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy
Emma Beede, Elizabeth Baylor, Fred Hersch, Anna Iurchenko, Lauren Wilcox, Paisan Ruamviboonsuk, and Laura M Vardoulakis · 2020
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The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al · 2020
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In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2020
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Don’t stop pretraining: Adapt language models to domains and tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A Smith · 2020
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How the fda regulates ai
H Benjamin Harvey and Vrushab Gowda · 2020
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Human-centered design for global health equity
Isaac Holeman and Dianna Kane · 2020
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Multimodal fusion with deep neural networks for leveraging ct imaging and electronic health record: a case-study in pulmonary embolism detection
Shih-Cheng Huang, Anuj Pareek, Roham Zamanian, Imon Banerjee, and Matthew P Lungren · 2020
Earlier work this paper cites.
Matthew McDermott, Bret Nestor, Evan Kim, Wancong Zhang, Anna Goldenberg, Peter Szolovits, and Marzyeh Ghassemi · 2020
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Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities
Jessica K Paulus and David M Kent · 2020
Earlier work this paper cites.
The risks of invariant risk minimization
Elan Rosenfeld, Pradeep Ravikumar, and Andrej Risteski · 2020
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A path for translation of machine learning products into healthcare delivery
Mark P Sendak, Joshua D’Arcy, Sehj Kashyap, Michael Gao, Marshall Nichols, Kristin Corey, William Ratliff, and Suresh Balu · 2020
Earlier work this paper cites.
Clinical text data in machine learning: systematic review
Irena Spasic, Goran Nenadic, et al · 2020
Earlier work this paper cites.
From development to deployment: dataset shift, causality, and shift-stable models in health ai
Adarsh Subbaswamy and Suchi Saria · 2020
Earlier work this paper cites.
Mimic-extract: A data extraction, preprocessing, and representation pipeline for mimic-iii
Shirly Wang, Matthew BA McDermott, Geeticka Chauhan, Marzyeh Ghassemi, Michael C Hughes, and Tristan Naumann · 2020
Earlier work this paper cites.
Knowledge distillation in deep learning and its applications
Abdolmaged Alkhulaifi, Fahad Alsahli, and Irfan Ahmad · 2021
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
Cited alongside, same era.
Early, intermediate and late fusion strategies for robust deep learning-based multimodal action recognition
Said Yacine Boulahia, Abdenour Amamra, Mohamed Ridha Madi, and Said Daikh · 2021
Cited alongside, same era.
Ethical machine learning in healthcare
Irene Y Chen, Emma Pierson, Sherri Rose, Shalmali Joshi, Kadija Ferryman, and Marzyeh Ghassemi · 2021
Cited alongside, same era.
Artificial intelligence on call: The physician’s decision of whether to use ai in clinical practice
Singh S Dai T · 2021
Cited alongside, same era.
Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during covid-19
Christopher Duckworth, Francis P Chmiel, Dan K Burns, Zlatko D Zlatev, Neil M White, Thomas WV Daniels, Michael Kiuber, and Michael J Boniface · 2021
Multimodal learning with graphs
Yasha Ektefaie, George Dasoulas, Ayush Noori, Maha Farhat, and Marinka Zitnik · 2023
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Fairness and bias in artificial intelligence: A brief survey of sources
E Ferrara · 2023
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Considering biased data as informative artifacts in ai-assisted health care
Kadija Ferryman, Maxine Mackintosh, and Marzyeh Ghassemi · 2023
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Medalign: A clinician-generated dataset for instruction following with electronic medical records
Scott L Fleming, Alejandro Lozano, William J Haberkorn, Jenelle A Jindal, Eduardo P Reis, Rahul Thapa, Louis Blankemeier, Julian Z Genkins, Ethan Steinberg, Ashwin Nayak, et al · 2023
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Mining for equitable health: Assessing the impact of missing data in electronic health records
Emily Getzen, Lyle Ungar, Danielle Mowery, Xiaoqian Jiang, and Qi Long · 2023
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Cited alongside, same era.
Domain adaptation for medical image analysis: a survey
Hao Guan and Mingxia Liu · 2021
Cited alongside, same era.
Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 2021
Cited alongside, same era.
An algorithmic approach to reducing unexplained pain disparities in underserved populations
Emma Pierson, David M Cutler, Jure Leskovec, Sendhil Mullainathan, and Ziad Obermeyer · 2021
Cited alongside, same era.
Machine learning for health (ml4h) 2021
Subhrajit Roy, Stephen Pfohl, Girmaw Abebe Tadesse, Luis Oala, Fabian Falck, Yuyin Zhou, Liyue Shen, Ghada Zamzmi, Purity Mugambi, Ayah Zirikly, Matthew B. A. McDermott, and Emily Alsentzer · 2021
Cited alongside, same era.
“everyone wants to do the model work, not the data work”: Data cascades in high-stakes ai
Nithya Sambasivan, Shivani Kapania, Hannah Highfill, Diana Akrong, Praveen Paritosh, and Lora M Aroyo · 2021
Cited alongside, same era.
Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations
Laleh Seyyed-Kalantari, Haoran Zhang, Matthew BA McDermott, Irene Y Chen, and Marzyeh Ghassemi · 2021
Cited alongside, same era.
Respecting autonomy and enabling diversity: The effect of eligibility and enrollment on research data demographics: Study examines the effect of eligibility and enrollment on research data demographics
Kayte Spector-Bagdady, Shengpu Tang, Sarah Jabbour, W Nicholson Price, Ana Bracic, Melissa S Creary, Sachin Kheterpal, Chad M Brummett, and Jenna Wiens · 2021
Cited alongside, same era.
Presentation matters for ai-generated clinical advice
Marzyeh Ghassemi · 2023
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Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, et al · 2023
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A multi-center study on the adaptability of a shared foundation model for electronic health records
Lin Lawrence Guo, Jason Fries, Ethan Steinberg, Scott Lanyon Fleming, Keith Morse, Catherine Aftandilian, Jose Posada, Nigam Shah, and Lillian Sung · 2023
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Deep metric learning for the hemodynamics inference with electrocardiogram signals
Hyewon Jeong, Collin M Stultz, and Marzyeh Ghassemi · 2023
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Federated benchmarking of medical artificial intelligence with medperf
Alexandros Karargyris, Renato Umeton, Micah J Sheller, Alejandro Aristizabal, Johnu George, Anna Wuest, Sarthak Pati, Hasan Kassem, Maximilian Zenk, Ujjwal Baid, et al · 2023
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Deep multimodal fusion for surgical feedback classification
Rafal Kocielnik, Elyssa Y Wong, Timothy N Chu, Lydia Lin, De-An Huang, Jiayun Wang, Anima Anandkumar, and Andrew J Hung · 2023
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Towards explaining distribution shifts
Sean Kulinski and David I Inouye · 2023
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Llava-med: Training a large language-and-vision assistant for biomedicine in one day
Chunyuan Li, Cliff Wong, Sheng Zhang, Naoto Usuyama, Haotian Liu, Jianwei Yang, Tristan Naumann, Hoifung Poon, and Jianfeng Gao · 2023
Later among the works it cites.
Holistic evaluation of language models, 2023
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel Orr, Lucia Zheng, Mert Yuksekgonul, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, and Yuta Koreeda · 2023
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Matthew McDermott, Bret Nestor, Peniel Argaw, and Isaac Kohane · 2023
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Nikita Mehandru, Sweta Agrawal, Yimin Xiao, Elaine C Khoong, Ge Gao, Marine Carpuat, and Niloufar Salehi · 2023
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Chatgpt and physicians’ malpractice risk
Michelle M Mello and Neel Guha · 2023
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President biden’s executive order on artificial intelligence—implications for health care organizations
Michelle M Mello, Nigam H Shah, and Danton S Char · 2023
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Most of the data generated is not used to its fullest potential — healthcarefinancenews.com
Susan Morse · 2023
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Hyenadna: Long-range genomic sequence modeling at single nucleotide resolution
Eric Nguyen, Michael Poli, Marjan Faizi, Armin Thomas, Callum Birch-Sykes, Michael Wornow, Aman Patel, Clayton Rabideau, Stefano Massaroli, Yoshua Bengio, et al · 2023
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Erkin Ötleş, Brian T Denton, and Jenna Wiens · 2023
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Automated cardiovascular record retrieval by multimodal learning between electrocardiogram and clinical report
Jielin Qiu, Jiacheng Zhu, Shiqi Liu, William Han, Jingqi Zhang, Chaojing Duan, Michael A Rosenberg, Emerson Liu, Douglas Weber, and Ding Zhao · 2023
Later among the works it cites.
Sequential multi-dimensional self-supervised learning for clinical time series
Aniruddh Raghu, Payal Chandak, Ridwan Alam, John Guttag, and Collin Stultz · 2023
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Are emergent abilities of large language models a mirage?
Rylan Schaeffer, Brando Miranda, and Sanmi Koyejo · 2023
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Towards expert-level medical question answering with large language models, 2023
Karan Singhal, Tao Tu, Juraj Gottweis, Rory Sayres, Ellery Wulczyn, Le Hou, Kevin Clark, Stephen Pfohl, Heather Cole-Lewis, Darlene Neal, Mike Schaekermann, Amy Wang, Mohamed Amin, Sami Lachgar, Philip Mansfield, Sushant Prakash, Bradley Green, Ewa Dominowska, Blaise Aguera y Arcas, Nenad Tomasev, Yun Liu, Renee Wong, Christopher Semturs, S. Sara Mahdavi, Joelle Barral, Dale Webster, Greg S. Corrado, Yossi Matias, Shekoofeh Azizi, Alan Karthikesalingam, and Vivek Natarajan · 2023
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When personalization harms performance: reconsidering the use of group attributes in prediction
Vinith Menon Suriyakumar, Marzyeh Ghassemi, and Berk Ustun · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
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Expanding impact of mobile health programs: Saheli for maternal and child care
Shresth Verma, Gargi Singh, Aditya Mate, Paritosh Verma, Sruthi Gorantla, Neha Madhiwalla, Aparna Hegde, Divy Thakkar, Manish Jain, Milind Tambe, et al · 2023
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Latanya Sweeney, September 2023
Wikipedia contributors · 2023
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External validation of ai models in health should be replaced with recurring local validation
Alexey Youssef, Michael Pencina, Anshul Thakur, Tingting Zhu, David Clifton, and Nigam H Shah · 2023
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Large-scale domain-specific pretraining for biomedical vision-language processing, 2023
Sheng Zhang, Yanbo Xu, Naoto Usuyama, Jaspreet Bagga, Robert Tinn, Sam Preston, Rajesh Rao, Mu Wei, Naveen Valluri, Cliff Wong, Matthew P. Lungren, Tristan Naumann, and Hoifung Poon · 2023
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Event-based contrastive learning for medical time series
Hyewon Jeong, Nassim Oufattole, Matthew Mcdermott, Aparna Balagopalan, Bryan Jangeesingh, Marzyeh Ghassemi, and Collin Stultz · 2024
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Accuracy and equity in clinical risk prediction
Emma Pierson · 2024
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A comprehensive study of knowledge editing for large language models
Ningyu Zhang, Yunzhi Yao, Bozhong Tian, Peng Wang, Shumin Deng, Mengru Wang, Zekun Xi, Shengyu Mao, Jintian Zhang, Yuansheng Ni, et al · 2024
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