Introduction

 

The medical application domain and the discipline of Natural Language Processing have been interacting for a good half century now, to mutual benefit. NLP techniques have helped in medical knowledge discovery and clinical practice improvements. At the same time, the medical field has contributed meaningful tasks, sizeable document collections (e.g., MIMIC for patient records, MEDLINE for scientific abstracts), and detailed lexical resources (UMLS), and these have helped advancements in the discipline of NLP in general.

 

Language is prevalent throughout the care process. It is used to encode institutional knowledge and health information, patient language productions can be analyzed for diagnosis (speech and/or voice disorders, language disorders, mental illnesses), it conveys patient and health professional interactions (consultations, consensus meetings), and also encodes clinical descriptions of pathologies and their management from the perspective of patients and health professionals (social networks, patient records). These language productions all address the specialiazed theme of health while exhibiting great diversity in terms of medium (written or spoken language), register (written material published in journals, professional note-taking in clinical documents, spontaneous production on social networks), language (English for literature, any language for other types of documents), etc. As computer science goes through rapid changes (deep learning, big data, internet of things), and the medical field is seeing its own opportunities (precision medicine, drug discovery) and pressures (chronic diseases, an aging population), interactions between these fields are more relevant than ever. This special issue of TAL aims to provide an overview of current NLP research on all aspects of health, including fundamental work and applications.

 

Authors are invited to submit papers on all aspects of NLP for health, in particular regarding, but not limited to, the following issues and tasks:

• general vs. in-domain pre-trained language models general pre-trained vs. in-domain embeddings
• analytics of social media related to health
• analysis of speech for medical purposes: speech pathology, interactions between medical professionals and patients
• accessibility of health information: NLP for improving text comprehension, text simplification, machine translation, systematic review support
• Automatic processing in the context of speech and language impairment: Augmentative and Alternative Communication (AAC) solutions (from or to speech, text, symbols, sign language), Automatic characterization of disorders, assessment of their severity, decision support
• Conversational Agents (CA) in health and medical care (e-learning, virtual assistants, ...)

We particularly welcome submissions reporting work on languages other than English and inclusive of vulnerable groups.

 

IMPORTANT DATES

 

Submission deadline: May 15, 2020

Notification to the authors after the first review July 31 2020

Notification to the authors after the second review: mid-September 2020

Final version: December 2020

Publication: End of January 2021