We discuss automatic creation of medical reports from ASR-generated patient-doctor conversational transcripts using an end-to-end neural summarization approach.We explore both recurrent neural network (RNN) and Transformer-based sequence-to-sequence architectures for summarizing medical conversations. We have incorporated enhancements to these architectures, such as the pointer-generator network that facilitates copying parts of the conversations to the reports, and a hierarchical RNN encoder that makes RNN training three times faster with long inputs. A comparison of the relative improvements from the different model architectures over an oracle extractive baseline is provided on a dataset of 800k orthopedic encounters. Consistent with observations in literature for machine translation and related tasks, we find the Transformer models outperform RNN in accuracy, while taking less than half the time to train. Significantly large wins over a strong oracle baseline indicate that sequenceto-sequence modeling is a promising approach for automatic generation of medical reports, in the presence of data at scale.
In this paper, we propose a fine grained classification of english adjectives geared at modeling the distinct inference patterns licensed by each adjective class. We show how it can be implemented in description logic and illustrate the predictions made by a series of examples. The proposal has been implemented using Description logic as a semantic representation language and the prediction verified using the DL theorem prover RACER.
In recent years the use of electronic medical records has accelerated resulting in large volumes of medical data when a patient visits a healthcare facility. As a first step towards reimbursement healthcare institutions need to associate ICD-10 billing codes to these documents. This is done by trained clinical coders who may use a computer assisted solution for shortlisting of codes. In this work, we present our work to build a machine learning based scalable system for predicting ICD-10 codes from electronic medical records. We address data imbalance issues by implementing two system architectures using convolutional neural networks and logistic regression models. We illustrate the pros and cons of those system designs and show that the best performance can be achieved by leveraging the advantages of both using a system combination approach.
As shown in the formal semantics literature, adjectives can display very different inferential patterns depending on whether they are intersective, privative, subsective or plain non-subsective. Moreover, many of these classes are often described using second order constructs. In this paper, we adopt Hobbs's ontologically promiscuous approach and present a first order treatment of adjective semantics which opens the way for a sophisticated treatment of adjectival inference. The approach was implemented and tested using first order automated reasoners.
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