Call for Paper: Workshop on Extraction

and Processing of Rich Semantics from Medical Texts (RichMedSem 2016)

Important Dates: Submission deadline: Friday March 4, 2016

Notifications: Friday April 1, 2016

Camera-ready version: Friday April 15, 2016

 

Workshop Organizers:

Kerstin Denecke (Bern University of Applied Sciences), Yihan Deng (University of Leipzig), Thierry Declerck (German Research Center for Artificial Intelligence in Medicine)

In conjunction with the European Semantic Web Conference 2016

Medical documents bear rich semantics such as facts, experiences, opinions or information which could – when extracted automatically - support a broad range of applications. Physicians could learn about the experiences of their colleagues, get hints to critical events in the treatment of a specific patient or receive information for improving treatment. However, language peculiarities, content diversity, streaming nature of clinical documents pose many challenges and the trade-off of filtering noise at the cost of losing information which is potentially relevant. This workshop is devoted to the technologies for dealing with clinical documents for medical information gathering and application in knowledge based systems. The aim of the workshop is to encourage researchers from the medical natural language processing (NLP) and knowledge management community to present novel issues and techniques related to the extraction and processing of rich semantics from medical texts, but more importantly to discuss current challenges and future steps towards new directions for gathering and processing rich semantics in the medical domain. Topics include but are not limited to:

Applications for rich semantics

  • Patient stratification and patient retrieval through rich semantics
  • Digital Patient Modelling
  • Clinical decision support systems and knowledge-based systems 

Extraction of medical sentiments

  • Machine learning and lexicon-based methods for medical sentiment analysis
  • Specific language models for sentiment analysis
  • Deep learning and its usage in medical sentiment analysis

Analysis of extracted rich semantics from medical texts

  • Extraction of negation, uncertainty or intentions
  • Extraction and interpretation of quality, quantity, extent, severity indicators
  • Extraction of correlation between events
  • Topic detection and modelling in clinical text
  • Context scope determination

Event extraction in medical texts

  • Event extraction from medical texts
  • Identification of relationship between events
  • Causality analysis between events in the medical domain

Corpus and gold standard generation for clinical domain

  • Annotation management
  • Schema definition
  • Annotation toolkits
  • Corpus sharing (convention with exchange schema)

Predictive modelling for morbidity and mortality based on medical semantics

  • Predictive modelling for  temporal prediction
  • Predictive modelling suitable for clinical sparse data
  • Predictive modelling coping with uncertainty

Programme Committee

  • Wendy Chapman, University of Utah
  • Danielle Mowery, University of Utah
  • Ronald Cornet, University of Amsterdam
  • Stefan Schulz, University of Graz
  • Hans-Ulrich Krieger, Language Technology Lab, German Research Center for Artificial Intelligence
  • Peter Dolog, Aalborg University Wendy Chapman,

Paper submission

Submitted papers should describe original work, present significant results, and provide rigorous, principled, and repeatable evaluation. Papers should not exceed fifteen (15) pages in length and must be formatted according to the guidelines for LNCS authors. Papers must be submitted in PDF (Adobe's Portable Document Format) format. At least one author of each accepted paper must register for the workshop. More information about the Springer's Lecture Notes in Computer Science (LNCS) are available on the Springer LNCS Web site. Paper submission via EasyChair Conference Submission System.