Contributions to AI research

The machine learning team at Abridge conducts cutting-edge research that powers development. The end result is a product that rapidly improves its ability to summarize and structure medical conversations for clinicians and patients alike.

Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques

Kundan Krishna, Sopan Khosla, Jeffrey Bigham, Zachary C. Lipton

Association for Computational Linguistics (ACL), 2021

The Mythos of Model Interpretability

Zachary C. Lipton

Communications of the ACM (CACM) 2018

Learning to Diagnose with LSTM Recurrent Neural Networks

Zachary C. Lipton, David C. Kale, Charles Elkan, Randall Wetzel

International Conference on Learning Representations (ICLR) 2016

The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants

Lucas Bandarkar, Davis Liang*, Benjamin Muller, Mikel Artetxe, Satya Narayan Shukla, Donald Husa, Naman Goyal, Abhinandan Krishnan, Luke Zettlemoyer, Madian Khabsa

Belebele sheds light on the capabilities of LLMs on both reading comprehension and long-tail multilingual understanding (arXiv whitepaper). *Davis Liang is affiliated with Abridge.

Generating More Faithful and Consistent SOAP Notes Using Attribute-Specific Parameters

Sanjana Ramprasad, Elisa Ferracane, Sai P. Selvaraj

Proceedings of Machine Learning Research, 2023

ASR Error Detection via Audio-Transcript entailment

Nimshi Venkat Meripo and Sandeep Konam

Interspeech 2022 Special Session, Speech and Language in Health: from Remote Monitoring to Medical Conversations

Weakly Supervised Medication Regimen Extraction from Medical Conversations

Dhruvesh Patel, Sandeep Konam and Sai Prabhakar

3rd Clinical Natural Language Processing Workshop, The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

MedFilter: Improving Extraction of Task-relevant Utterances through Integration of Discourse Structure and Ontological Knowledge

Sopan Khosla, Shikhar Vashishth, Jill Fain Lehman and Carolyn Rose

Proceedings of The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Extracting Appointment Spans from Medical Conversations

Nimshi Venkat Meripo, Sandeep Konam

Accepted to the 2nd Workshop on NLP for Medical Conversations (NLPMC), NAACL 2021

Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations

Benjamin J Schloss, Sandeep Konam

Proceedings of Machine Learning Research, Machine Learning for Healthcare (MLHC) 2020

Towards Fairness in Classifying Medical Conversations into SOAP Sections

Elisa Ferracane, Sandeep Konam

Trustworthy AI for Healthcare Workshop, 2021 AAAI Conference on Artificial Intelligence

Towards Understanding ASR Error Correction for Medical Conversations

Anirudh Mani, Shruti Palaskar, Sandeep Konam

Proceedings of the First Workshop on Natural Language Processing for Medical Conversations of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

ASR Error Correction and Domain Adaptation Using Machine Translation

Anirudh Mani, Shruti Palaskar, Nimshi Venkat Meripo, Sandeep Konam, Florian Metze

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing

Medication Regimen Extraction From Medical Conversations

Sai Prabhakar Pandi Selvaraj and Sandeep Konam

Proceedings of the International Workshop on Health Intelligence (W3PHIAI) of the 34th AAAI Conference on Artificial Intelligence, 2020

Extracting Structured Data from Physician-Patient Conversations By Predicting Noteworthy Utterances

Kundan Krishna, Amy Pavel, Benjamin Schloss, Jeffrey Bigham and Zachary Lipton

Proceedings of the International Workshop on Health Intelligence (W3PHIAI) of the 34th AAAI Conference on Artificial Intelligence, 2020

A Mission Driven Approach to Machine Learning for Healthcare Conversation

Sandeep Konam, Shivdev Rao

Journal of Commercial Biotechnology (ISSN: 1462-8732 / eISSN 1478-565X), Vol. 26 No. 2 (2021)
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