Search Results for author:
Found 11 papers, 1 papers with code
Extracting Appointment Spans from Medical Conversations
Extracting structured information from medical conversations can reduce the documentation burden for doctors and help patients follow through with their care plan.
ASR Error Detection via Audio-Transcript entailment
To the best of our knowledge, we are the first to frame this problem as an end-to-end entailment task between the audio segment and its corresponding transcript segment.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Towards Fairness in Classifying Medical Conversations into SOAP Sections
As machine learning algorithms are more widely deployed in healthcare, the question of algorithmic fairness becomes more critical to examine.
Weakly Supervised Medication Regimen Extraction from Medical Conversations
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 documentation burden for doctors.
Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations
Summaries generated from medical conversations can improve recall and understanding of care plans for patients and reduce documentation burden for doctors.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
ASR Error Correction and Domain Adaptation Using Machine Translation
We propose a simple technique to perform domain adaptation for ASR error correction via machine translation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Medication Regimen Extraction From Medical Conversations
In this paper, we focus on extracting the Medication Regimen (dosage and frequency for medications) discussed in a medical conversation.
Understanding Convolutional Networks with APPLE : Automatic Patch Pattern Labeling for Explanation
With the success of deep learning, recent efforts have been focused on analyzing how learned networks make their classifications.
UAV and Service Robot Coordination for Indoor Object Search Tasks
In this paper, we propose the concept of coordination between CoBot and the Parrot ARDrone 2. 0 to perform service-based object search tasks, in which CoBot localizes and navigates to the general search areas carrying the ARDrone and the ARDrone searches locally for objects.
Towards Visual Explanations for Convolutional Neural Networks via Input Resampling
The predictive power of neural networks often costs model interpretability.
Obstacle Avoidance through Deep Networks based Intermediate Perception
Second, predict the trajectory from the depth and normal.
