activity
20172020
most citedTowards Visual Explanations for Convolutional Neural Networks via Input Resampling

3 citations · 7 across the 4 of their papers we have counts for

collaborators

8 papers

cs.CY20202 cited

Towards Fairness in Classifying Medical Conversations into SOAP Sections

Elisa Ferracane, Sandeep Konam

As machine learning algorithms are more widely deployed in healthcare, the question of algorithmic fairness becomes more critical to examine. Our work seeks to identify and underst…

cs.CL2020

Weakly Supervised Medication Regimen Extraction from Medical Conversations

Dhruvesh Patel, Sandeep Konam, Sai P. Selvaraj

Automated Medication Regimen (MR) extraction from medical conversations can not only improve recall and help patients follow through with their care plan, but also reduce the docum…

cs.CL20201 cited

Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations

Benjamin Schloss, Sandeep Konam

Summaries generated from medical conversations can improve recall and understanding of care plans for patients and reduce documentation burden for doctors. Recent advancements in a…

eess.AS2020

ASR Error Correction and Domain Adaptation Using Machine Translation

Anirudh Mani, Shruti Palaskar, Nimshi Venkat Meripo +2

Off-the-shelf pre-trained Automatic Speech Recognition (ASR) systems are an increasingly viable service for companies of any size building speech-based products. While these ASR sy…

cs.CL2019

Medication Regimen Extraction From Medical Conversations

Sai P. Selvaraj, Sandeep Konam

Extracting relevant information from medical conversations and providing it to doctors and patients might help in addressing doctor burnout and patient forgetfulness. In this paper…

cs.LG2018

Understanding Convolutional Networks with APPLE : Automatic Patch Pattern Labeling for Explanation

Sandeep Konam, Ian Quah, Stephanie Rosenthal +1

With the success of deep learning, recent efforts have been focused on analyzing how learned networks make their classifications. We are interested in analyzing the network output…