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Found 8 papers, 4 papers with code
Injecting knowledge into language generation: a case study in auto-charting after-visit care instructions from medical dialogue
For high-stake domains that are also knowledge-rich, we show how to use knowledge to (a) identify which rare tokens that appear in both source and reference are important and (b) uplift their conditional probability.
Dialogue-Contextualized Re-ranking for Medical History-Taking
We find that relative to the expert system, the best performance is achieved by our proposed global re-ranker with a transformer backbone, resulting in a 30% higher normalized discount cumulative gain (nDCG) and a 77% higher mean average precision (mAP).
Learning functional sections in medical conversations: iterative pseudo-labeling and human-in-the-loop approach
This paper presents an approach that tackles the problem of learning to classify medical dialogue into functional sections without requiring a large number of annotations.
OSLAT: Open Set Label Attention Transformer for Medical Entity Retrieval and Span Extraction
In this paper, we propose a method for linking an open set of entities that does not require any span annotations.
MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System
We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system with a unique approach to the natural language generator module.
Adding more data does not always help: A study in medical conversation summarization with PEGASUS
Summarized conversations are used to facilitate patient hand-offs between physicians, and as part of providing care in the future.
SmartTriage: A system for personalized patient data capture, documentation generation, and decision support
Symptom checkers have emerged as an important tool for collecting symptoms and diagnosing patients, minimizing the involvement of clinical personnel.
Evaluating robustness of language models for chief complaint extraction from patient-generated text
In this work, we evaluate several approaches to chief complaint classification using a novel Chief Complaint (CC) Dataset that contains ~200, 000 patient-generated reasons-for-visit entries mapped to a set of 795 discrete chief complaints.
