4 citations · 8 across the 8 of their papers we have counts for
8 papers
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…
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…
Learning functional sections in medical conversations: iterative pseudo-labeling and human-in-the-loop approach
Mengqian Wang, Ilya Valmianski, Xavier Amatriain +1
Medical conversations between patients and medical professionals have implicit functional sections, such as "history taking", "summarization", "education", and "care plan." In this…
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…
Adding more data does not always help: A study in medical conversation summarization with PEGASUS
Varun Nair, Namit Katariya, Xavier Amatriain +2
Medical conversation summarization is integral in capturing information gathered during interactions between patients and physicians. Summarized conversations are used to facilitat…
MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System
Rhys Compton, Ilya Valmianski, Li Deng +4
We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system with a unique approach to the natural language generator module. MEDCOD has been developed…
SmartTriage: A system for personalized patient data capture, documentation generation, and decision support
Ilya Valmianski, Nave Frost, Navdeep Sood +6
Symptom checkers have emerged as an important tool for collecting symptoms and diagnosing patients, minimizing the involvement of clinical personnel. We developed a machine-learnin…
Evaluating robustness of language models for chief complaint extraction from patient-generated text
Ilya Valmianski, Caleb Goodwin, Ian M. Finn +2
Automated classification of chief complaints from patient-generated text is a critical first step in developing scalable platforms to triage patients without human intervention. In…