34 citations · 79 across the 11 of their papers we have counts for
11 papers
Transformer models: an introduction and catalog
Xavier Amatriain, Ananth Sankar, Jie Bing +3
In the past few years we have seen the meteoric appearance of dozens of foundation models of the Transformer family, all of which have memorable and sometimes funny, but not self-e…
Adding more data does not always help: A study in medical conversation summarization with PEGASUS
Varun Nair, Namit Katariya, Xavier Amatriain +2
Medical conversation summarization is integral in capturing information gathered during interactions between patients and physicians. Summarized conversations are used to facilitat…
Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures
Anirudh Joshi, Namit Katariya, Xavier Amatriain +1
Understanding a medical conversation between a patient and a physician poses a unique natural language understanding challenge since it combines elements of standard open ended con…
Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs
Clara H. McCreery, Namit Katariya, Anitha Kannan +2
People increasingly search online for answers to their medical questions but the rate at which medical questions are asked online significantly exceeds the capacity of qualified pe…
COVID-19 in differential diagnosis of online symptom assessments
Anitha Kannan, Richard Chen, Vignesh Venkataraman +2
The COVID-19 pandemic has magnified an already existing trend of people looking for healthcare solutions online. One class of solutions are symptom checkers, which have become very…
The accuracy vs. coverage trade-off in patient-facing diagnosis models
Anitha Kannan, Jason Alan Fries, Eric Kramer +3
A third of adults in America use the Internet to diagnose medical concerns, and online symptom checkers are increasingly part of this process. These tools are powered by diagnosis…
Classification as Decoder: Trading Flexibility for Control in Medical Dialogue
Sam Shleifer, Manish Chablani, Anitha Kannan +2
Generative seq2seq dialogue systems are trained to predict the next word in dialogues that have already occurred. They can learn from large unlabeled conversation datasets, build a…
Domain-Relevant Embeddings for Medical Question Similarity
Clara McCreery, Namit Katariya, Anitha Kannan +2
The rate at which medical questions are asked online far exceeds the capacity of qualified people to answer them, and many of these questions are not unique. Identifying same-quest…
Open Set Medical Diagnosis
Viraj Prabhu, Anitha Kannan, Geoffrey J. Tso +4
Machine-learned diagnosis models have shown promise as medical aides but are trained under a closed-set assumption, i.e. that models will only encounter conditions on which they ha…
Prototypical Clustering Networks for Dermatological Disease Diagnosis
Viraj Prabhu, Anitha Kannan, Murali Ravuri +3
We consider the problem of image classification for the purpose of aiding doctors in dermatological diagnosis. Dermatological diagnosis poses two major challenges for standard off-…
Learning from the experts: From expert systems to machine-learned diagnosis models
Murali Ravuri, Anitha Kannan, Geoffrey J. Tso +1
Expert diagnostic support systems have been extensively studied. The practical applications of these systems in real-world scenarios have been somewhat limited due to well-understo…