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Found 21 papers, 4 papers with code
Large Language Models: A Survey
Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks, since the release of ChatGPT in November 2022.
Prompt Design and Engineering: Introduction and Advanced Methods
Prompt design and engineering has rapidly become essential for maximizing the potential of large language models.
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.
Transformer models: an introduction and catalog
The goal of this paper is to offer a somewhat comprehensive but simple catalog and classification of the most popular Transformer models.
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.
Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization
In medical dialogue summarization, summaries must be coherent and must capture all the medically relevant information in the dialogue.
Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions
We study the problem of medical symptoms recognition from patient text, for the purposes of gathering pertinent information from the patient (known as history-taking).
Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures.
Understanding a medical conversation between a patient and a physician poses unique natural language understanding challenge since it combines elements of standard open-ended conversation with very domain-specific elements that require expertise and medical knowledge.
Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures
Understanding a medical conversation between a patient and a physician poses a unique natural language understanding challenge since it combines elements of standard open ended conversation with very domain specific elements that require expertise and medical knowledge.
COVID-19 in differential diagnosis of online symptom assessments
Traditional symptom checkers, however, are based on manually curated expert systems that are inflexible and hard to modify, especially in a quickly changing situation like the one we are facing today.
Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs
People increasingly search online for answers to their medical questions but the rate at which medical questions are asked online significantly exceeds the capacity of qualified people to answer them.
The accuracy vs. coverage trade-off in patient-facing diagnosis models
A third of adults in America use the Internet to diagnose medical concerns, and online symptom checkers are increasingly part of this process.
Classification as Decoder: Trading Flexibility for Control in Medical Dialogue
Generative seq2seq dialogue systems are trained to predict the next word in dialogues that have already occurred.
Domain-Relevant Embeddings for Medical Question Similarity
The rate at which medical questions are asked online far exceeds the capacity of qualified people to answer them, and many of these questions are not unique.
Open Set Medical Diagnosis
Machine-learned diagnosis models have shown promise as medical aides but are trained under a closed-set assumption, i. e. that models will only encounter conditions on which they have been trained.
Classification As Decoder: Trading Flexibility For Control In Neural Dialogue
Only 12% of our discriminative approach's responses are worse than the doctor's response in the same conversational context, compared to 18% for the generative model.
Prototypical Clustering Networks for Dermatological Disease Diagnosis
We consider the problem of image classification for the purpose of aiding doctors in dermatological diagnosis.
Learning from the experts: From expert systems to machine-learned diagnosis models
In this paper, we present a method to merge both approaches by using expert systems as generative models that create simulated data on which models can be learned.
