13 citations · 15 across the 5 of their papers we have counts for
5 papers · 1 filter
Injecting knowledge into language generation: a case study in auto-charting after-visit care instructions from medical dialogue
Maksim Eremeev, Ilya Valmianski, Xavier Amatriain +1
Factual correctness is often the limiting factor in practical applications of natural language generation in high-stakes domains such as healthcare. An essential requirement for ma…
Generating medically-accurate summaries of patient-provider dialogue: A multi-stage approach using large language models
Varun Nair, Elliot Schumacher, Anitha Kannan
A medical provider's summary of a patient visit serves several critical purposes, including clinical decision-making, facilitating hand-offs between providers, and as a reference f…
Dialogue-Contextualized Re-ranking for Medical History-Taking
Jian Zhu, Ilya Valmianski, Anitha Kannan
AI-driven medical history-taking is an important component in symptom checking, automated patient intake, triage, and other AI virtual care applications. As history-taking is extre…
DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents
Varun Nair, Elliot Schumacher, Geoffrey Tso +1
Large language models (LLMs) have emerged as valuable tools for many natural language understanding tasks. In safety-critical applications such as healthcare, the utility of these…
OSLAT: Open Set Label Attention Transformer for Medical Entity Retrieval and Span Extraction
Raymond Li, Ilya Valmianski, Li Deng +2
Medical entity span extraction and linking are critical steps for many healthcare NLP tasks. Most existing entity extraction methods either have a fixed vocabulary of medical entit…