The 4th International Workshop on Knowledge Discovery in Healthcare Data


CFP.pdf

Call for Papers

In this workshop we wish to address the challenge of leveraging knowledge-based models that can utilise patient-focused data to improve care delivery to bring about "learning healthcare systems". The notion of the learning healthcare system encompasses research in prominent areas of Artificial Intelligence including language engineering, data mining, knowledge representation & reasoning, learning and autonomous systems.

This workshop will take place as part of the IJCAI-workshop series and builds on previously held successful Knowledge Discovery in Healthcare Data workshops by welcoming contributions providing insight on the extent to which AI techniques have successfully penetrated the healthcare field, interaction among AI techniques to achieve a successful learning healthcare system and the distinction between AI and non-AI models needed in modern healthcare environments. The workshop will focus on discussing issues in data extraction and assembly, knowledge discovery and personalised decision support to care providers and self-care aiding tools to patients.

Topics

Contributions are welcome in areas including, but not limited to, the following:

  • Theme-related contributions:
    • Analysis of the rise of techniques and approaches, and the decline of techniques and approaches for knowledge discovery in healthcare
    • Analyses of the interaction between AI subfields serving the learning healthcare system
    • AI and non-AI techniques to solve the basic methodological and technological problems associated to the real deployment of health-care agent-based systems: security, privacy, stakeholder acceptance, ethical issues, etc.
  • Data extraction, organisation & assembly:
    • Knowledge-driven and data-driven approaches for information retrieval and data mining
    • Multilevel data integration in healthcare, e.g. behavioural data, diagnoses, vitals, radiology imaging, Doctor's notes, phenotype, and different omics data, including multi-agent approaches.
    • Integration and use of medical ontologies.
    • Knowledge abstraction, classification, and summarization from literature or electronic health records
  • Knowledge discovery & analytics
    • Handling uncertainty in large healthcare datasets: dealing with missing values and non-uniformly sampled data
    • Detecting and extracting hidden information from healthcare data
    • The rise of Artificial neural network models or deep learning approaches for healthcare data analytics
    • Extracting causal relationships from healthcare data
    • Predictive and prescriptive analyses of healthcare data
    • Applications of probabilistic analysis in medicine
    • Development of novel diagnostic and prognostic tests utilising quantitative data analysis
  • Personalisation and decision support
    • Mobile agents in hospital environment
    • Patient Empowerment through Personalised patient-centred systems
    • Autonomous and remote care delivery.
    • Medical Decision Support Systems, including Recommender Systems
    • Automation of clinical trials, including implementation of adaptive and platform trial designs.
    • Applications of IoT (wearables, sensors, etc.) in healthcare
  • Provenance, Security and privacy of health data
    • Frameworks for data security management
    • Transparency and explainability
    • Provenance of health data
  • Multi-modal Low-back Pain Exercise Recognition challenge
    • Scientific papers reviewing existing machine learning models or presenting novel machine learning models for reasoning with multi-modal sensor data
    • Papers that addresses the workshop challenge in areas such as representation, translation, alignment, fusion and co-learning.

Submission & Format

Submissions can be made as:

  1. Long papers (7 pages + 1 page references): Long papers should present original research work and be no longer than eight pages in total: seven pages for the main text of the paper (including all figures but excluding references), and one additional page for references.
  2. Short papers (4 pages + 1 page references): Short papers may report on works in progress, descriptions of available datasets, as well as data collection efforts. Position papers regarding potential research challenges are also welcomed. Short paper submissions should be no longer than five pages in total: four pages for the main text of the paper (including all figures but excluding references), and one additional page for references.
  3. All papers submitted for Multi-modal Low-back Pain Exercise Recognition challenge can be formatted as either long or short papers.

Both long and short papers must be formatted according to IJCAI guidelines and submitted electronically through EasyChair: https://easychair.org/conferences/?conf=kdh2019.

Publication

Proceedings: The papers accepted for KDHD 2019 will be published in the CEUR-WS.org international proceedings volume. This proceedings volume will be published electronically and indexed by Google Scholar and DBLP.