mlsanityData to 7 Oct 2026

MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System

arXiv 2111.09381 ↗First posted 17 Nov 2021cs.CL cs.AI cs.LGcited by 3

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Abstract (arXiv)

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 and evaluated specifically for the history taking task. It integrates the advantage of a traditional modular approach to incorporate (medical) domain knowledge with modern deep learning techniques to generate flexible, human-like natural language expressions. Two key aspects of MEDCOD's natural language output are described in detail. First, the generated sentences are emotive and empathetic, similar to how a doctor would communicate to the patient. Second, the generated sentence structures and phrasings are varied and diverse while maintaining medical consistency with the desired medical concept (provided by the dialogue manager module of MEDCOD). Experimental results demonstrate the effectiveness of our approach in creating a human-like medical dialogue system. Relevant code is available at https://github.com/curai/curai-research/tree/main/MEDCOD

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 21 of its 64 references that are in the map. First posted on arXiv 17 Nov 2021.

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21 of 64 references.

References · 21

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9 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 2.

3 papers.

  1. 2023Injecting knowledge into language generation: a case study in auto-charting after-visit care instructions from medical dialoguecited by 3
  2. 2022Mind the Knowledge Gap: A Survey of Knowledge-enhanced Dialogue Systemscited by 1
  3. 2023Dialogue-Contextualized Re-ranking for Medical History-Takingcited by 0