KDH-2019: The 4th International Workshop on Knowledge Discovery in Healthcare Data
Тезисы до: 10.05.2019
Даты: 10.08.19 — 12.08.19
Е-мейл Оргкомитета: email@example.com
Организаторы: IJCAI Committee
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 is part of the IJCAI-workshop series and will build 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.
List of Topics
Contributions are welcome in areas including, but not limited to, the following:
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.
Веб-сайт конференции: https://sites.google.com/view/kdh2019