WNLPe-Health 2022 - The first Workshop on Context-aware NLP in eHealth will be held at IIIT Delhi, India on December 15th, 2022 in conjunction with 19th International Conference on Natural Language Processing (ICON 2022) . It is currently recognised that as much as 30% of the world’s stored data is produced by the healthcare sector. However, this ‘data-rich’ sector does not currently explore data to the full potential which may allow the development of much more individual and person-centred AI technologies. For example, by combining ubiquitous data with user-generated and publicly available data, AI algorithms can guide and inform citizens about risk modifying behaviors in an appropriate context. Context can be defined as “any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant for the interaction between a user and an application, including the user and applications themselves.”
The goal of this workshop is to provide a unique platform to bring together researchers and practitioners in healthcare informatics working with health-related data especially textual data, and facilitate close interaction among students, scholars, and industry professionals on eHealth language processing tasks. In particular, we are interested in works that advance state-of-the-art NLP and ML techniques for eHealth domains by incorporating more contextual knowledge in order to make models explainable, trustable and robust in changing situations.
We are interested in research on novel approaches, works in progress, comparative analyses of tools, and advancing state-of-the-art work in eHealth NLP methods, tools, and applications. Relevant topics for the workshop include, but are not limited to, the following areas:
Modelling of healthcare text in classical NLP tasks (tagging, chunking, parsing, entity identification, relation extraction, coreference, summarization, etc.) for under-resourced languages.
Person-centred NLP applications for eHealth including early risk prediction.
Algorithm for Context Data reasoning.
Context sensitive recommendations to individual citizens and patients.
Integration of structured and unstructured resources for health applications.
Domain adaptation techniques for clinical data.
Medical terminologies and ontologies.
Interpretability and analysis of NLP models for healthcare applications.
Processing clinical literature and trial reports.
Bayesian modelling and feature selection techniques for high-dimensional healthcare data.
Multimodal learning for decision support systems: Ubiquitous data, public databases, user generated content (in combination with wearable sensor technology).