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2020 – today
- 2024
[c7]Scott L. Fleming, Alejandro Lozano, William J. Haberkorn, Jenelle A. Jindal, Eduardo Pontes Reis, Rahul Thapa, Louis Blankemeier, Julian Z. Genkins, Ethan Steinberg, Ashwin Nayak, Birju S. Patel, Chia-Chun Chiang, Alison Callahan, Zepeng Huo, Sergios Gatidis, Scott J. Adams, Oluseyi Fayanju, Shreya J. Shah, Thomas Savage, Ethan Goh, Akshay S. Chaudhari, Nima Aghaeepour, Christopher D. Sharp, Michael A. Pfeffer, Percy Liang, Jonathan H. Chen, Keith E. Morse, Emma P. Brunskill, Jason A. Fries, Nigam H. Shah:
MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records. AAAI 2024: 22021-22030- [c6]Rahul Thapa, Bryan He, Magnus Ruud Kjær, Hyatt E. Moore IV, Gauri Ganjoo, Emmanuel Mignot, James Zou:
SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals. ICML 2024 - [i11]Jenish Maharjan, Anurag Garikipati, Navan Preet Singh, Leo Cyrus, Mayank Sharma, Madalina G. Ciobanu, Gina Barnes, Rahul Thapa, Qingqing Mao, Ritankar Das:
OpenMedLM: Prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language models. CoRR abs/2402.19371 (2024) - [i10]Hyewon Jeong, Sarah Jabbour, Yuzhe Yang, Rahul Thapa, Hussein Mozannar, William Jongwon Han, Nikita Mehandru, Michael Wornow, Vladislav Lialin, Xin Liu, Alejandro Lozano, Jiacheng Zhu, Rafal Dariusz Kocielnik, Keith Harrigian, Haoran Zhang, Edward Lee, Milos Vukadinovic, Aparna Balagopalan, Vincent Jeanselme, Katherine Matton, Ilker Demirel, Jason A. Fries, Parisa Rashidi, Brett K. Beaulieu-Jones, Xuhai Orson Xu, Matthew B. A. McDermott, Tristan Naumann, Monica Agrawal, Marinka Zitnik, Berk Ustun, Edward Choi, Kristen Yeom, Gamze Gürsoy, Marzyeh Ghassemi, Emma Pierson, George H. Chen, Sanjat Kanjilal, Michael Oberst, Linying Zhang
, Harvineet Singh, Tom Hartvigsen, Helen Zhou, Chinasa T. Okolo:
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium. CoRR abs/2403.01628 (2024) - [i9]Alison Callahan, Duncan C. McElfresh, Juan M. Banda, Gabrielle Bunney, Danton Char, Jonathan H. Chen, Conor K. Corbin, Debadutta Dash, Norman L. Downing, Sneha S. Jain, Nikesh Kotecha, Jonathan Masterson, Michelle M. Mello, Keith E. Morse, Srikar Nallan, Abby Pandya, Anurang Revri, Aditya Sharma, Christopher D. Sharp, Rahul Thapa, Michael Wornow, Alaa Youssef, Michael A. Pfeffer, Nigam H. Shah:
Standing on FURM ground - A framework for evaluating Fair, Useful, and Reliable AI Models in healthcare systems. CoRR abs/2403.07911 (2024) - [i8]Rahul Thapa, Bryan He, Magnus Ruud Kjær, Hyatt E. Moore IV, Gauri Ganjoo, Emmanuel Mignot, James Zou:
SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals. CoRR abs/2405.17766 (2024) - [i7]Kezhen Chen, Rahul Thapa, Rahul Chalamala, Ben Athiwaratkun, Shuaiwen Leon Song, James Zou:
Dragonfly: Multi-Resolution Zoom Supercharges Large Visual-Language Model. CoRR abs/2406.00977 (2024) - 2023
- [j3]Keyvan Rahmani, Rahul Thapa, Pei-Ling Tsou, Satish Casie Chetty, Gina Barnes, Carson K. Lam, Chak Foon Tso:
Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction. Int. J. Medical Informatics 173: 104930 (2023) - [j2]Conor K. Corbin, Rob Maclay, Aakash Acharya, Sreedevi Mony, Soumya Punnathanam, Rahul Thapa, Nikesh Kotecha, Nigam H. Shah, Jonathan H. Chen:
DEPLOYR: a technical framework for deploying custom real-time machine learning models into the electronic medical record. J. Am. Medical Informatics Assoc. 30(9): 1532-1542 (2023) - [j1]Michael Wornow, Yizhe Xu, Rahul Thapa, Birju S. Patel, Ethan Steinberg, Scott L. Fleming, Michael A. Pfeffer, Jason Alan Fries, Nigam H. Shah:
The shaky foundations of large language models and foundation models for electronic health records. npj Digit. Medicine 6 (2023) - [c5]Michael Wornow, Rahul Thapa, Ethan Steinberg, Jason A. Fries, Nigam Shah:
EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models. NeurIPS 2023 - [i6]Conor K. Corbin, Rob Maclay, Aakash Acharya, Sreedevi Mony, Soumya Punnathanam, Rahul Thapa, Nikesh Kotecha, Nigam H. Shah, Jonathan H. Chen:
DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical record. CoRR abs/2303.06269 (2023) - [i5]Michael Wornow, Yizhe Xu, Rahul Thapa, Birju S. Patel, Ethan Steinberg, Scott L. Fleming, Michael A. Pfeffer, Jason A. Fries, Nigam H. Shah:
The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs. CoRR abs/2303.12961 (2023) - [i4]Debadutta Dash, Rahul Thapa, Juan M. Banda, Akshay Swaminathan, Morgan Cheatham, Mehr Kashyap, Nikesh Kotecha, Jonathan H. Chen, Saurabh Gombar, Lance Downing, Rachel Pedreira, Ethan Goh, Angel Arnaout, Garret Kenn Morris, Honor Magon, Matthew P. Lungren, Eric Horvitz, Nigam H. Shah:
Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare delivery. CoRR abs/2304.13714 (2023) - [i3]Michael Wornow, Rahul Thapa, Ethan Steinberg, Jason A. Fries, Nigam Shah:
EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models. CoRR abs/2307.02028 (2023) - [i2]Scott L. Fleming, Alejandro Lozano, William J. Haberkorn, Jenelle A. Jindal, Eduardo Pontes Reis, Rahul Thapa, Louis Blankemeier, Julian Z. Genkins, Ethan Steinberg, Ashwin Nayak, Birju S. Patel, Chia-Chun Chiang, Alison Callahan, Zepeng Huo, Sergios Gatidis, Scott J. Adams, Oluseyi Fayanju, Shreya J. Shah, Thomas Savage, Ethan Goh, Akshay S. Chaudhari, Nima Aghaeepour, Christopher D. Sharp, Michael A. Pfeffer, Percy Liang, Jonathan H. Chen, Keith E. Morse, Emma P. Brunskill, Jason A. Fries, Nigam H. Shah:
MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records. CoRR abs/2308.14089 (2023) - 2022
- [c4]Dongning Ma, Rahul Thapa, Xun Jiao:
MoleHD: Efficient Drug Discovery using Brain Inspired Hyperdimensional Computing. BIBM 2022: 390-393 - 2021
- [c3]Dongning Ma, Rahul Thapa, Xingjian Wang, Xun Jiao, Cong Hao:
Workload-Aware Approximate Computing Configuration. DATE 2021: 920-925 - [c2]Rahul Thapa, Bikal Lamichhane, Dongning Ma, Xun Jiao:
SpamHD: Memory-Efficient Text Spam Detection using Brain-Inspired Hyperdimensional Computing. ISVLSI 2021: 84-89 - [c1]Rahul Thapa, Dongning Ma, Xun Jiao:
HDXplore: Automated Blackbox Testing of Brain-Inspired Hyperdimensional Computing. ISVLSI 2021: 90-95 - [i1]Rahul Thapa, Dongning Ma, Xun Jiao:
HDXplore: Automated Blackbox Testing of Brain-Inspired Hyperdimensional Computing. CoRR abs/2105.12770 (2021)
Coauthor Index
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