No abstract

No abstract

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, we focus on extracting the Medication Regimen (dosage and frequency for medications) discussed in a medical conversation. We frame the problem as a Question Answering (QA) task and perform comparative analysis over: a QA approach, a new combined QA and Information Extraction approach, and other baselines. We use a small corpus of 6,692 annotated doctor-patient conversations for the task. Clinical conversation corpora are costly to create, difficult to handle (because of data privacy concerns), and thus scarce. We address this data scarcity challenge through data augmentation methods, using publicly available embeddings and pretrain part of the network on a related task (summarization) to improve the model's performance. Compared to the baseline, our best-performing models improve the dosage and frequency extractions' ROUGE-1 F1 scores from 54.28 and 37.13 to 89.57 and 45.94, respectively. Using our best-performing model, we present the first fully automated system that can extract Medication Regimen tags from spontaneous doctor-patient conversations with about ∼71% accuracy.

No abstract

Significant advances in the performance of deep neural networks, such as Convolutional Neural Networks (CNNs) for image classification, have created a drive for understanding how they work. Different techniques have been proposed to determine which features (e.g., image pixels) are most important for a CNN’s classification. However, the important features output by these techniques have typically been judged subjectively by a human to assess whether the important features capture the features relevant to the classification and not whether the features were actually important to classifier itself. We address the need for an objective measure to assess the quality of different feature importance measures. In particular, we propose measuring the ratio of a CNN’s accuracy on the whole image com- pared to an image containing only the important features. We also consider scaling this ratio by the relative size of the important region in order to measure the conciseness. We demonstrate that our measures correlate well with prior subjective comparisons of important features, but importantly do not require their human studies. We also demonstrate that the features on which multiple techniques agree are important have a higher impact on accuracy than those features that only one technique finds.

Advances in state-of-the-art techniques including convolutional neural networks (CNNs) have led to improved perception in autonomous robots. However, these new techniques make a robot’s decision-making process obscure even for the experts. Our goal is to auto- matically generate natural language explanations of a robot’s perception-based inferences in order to help people understand what features contribute to these classification predic- tions. Generating natural language explanations is particularly challenging for perception and other high-dimension classification tasks because 1) we lack a mapping from features to language and 2) there are a large number of features which could be explained. We present a novel approach to generating explanations, which first finds the important features that most affect the classification prediction and then utilizes a secondary detector which can identify and label multiple parts of the features, to label only those important features. Those labels serve as the natural language groundings that we use in our explanations. We demonstrate our explanation algorithm’s ability on the floor identification classifier of our mobile service robot.