KDHD 2018 - 3rd International Workshop on Knowledge Discovery in Healthcare Data

There are many healthcare datasets consisting of both structured and unstructured information, which provide a challenge for artificial intelligence and machine learning researchers seeking to extract knowledge from data. Existing healthcare datasets include electronic medical records, large collections of complex physiological information, medical imaging data, genomics, as well as other socio-economic and behavioral data. In order to perform data-driven analysis or build causal and inferential models using these datasets, challenges such as integrating multiple data types, dealing with missing data and handling irregularly sampled data, need to be addressed. While these challenges need to be considered by researchers working with healthcare data, a larger problem involves how to best ensure the hypotheses posed and types of knowledge discoveries sought are relevant to the healthcare community. Clinical perspectives from medical professionals are required to assure that advancements in healthcare data analysis results in positive impact to eventual point-of-care and outcome-based systems.
This workshop will build on previously held successful Knowledge Discovery in Healthcare Data workshops and will align with this year’s theme of Evolution of the Contours of AI 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:
-- Data extraction, organisation & assembly
• Knowledge-driven and data-driven approaches for information retrieval and data mining
• Multilevel data integration in healthcare, e.g. behavioral 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
• Biomedical data generation and curation
-- 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 utilizing quantitative data analysis
• Mathematical model development in biology and medicine, modeling of disease interaction and progression
• Novel visualization techniques
• Active, transfer and reinforcement learning in healthcare
• Physiological 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
• Clinical decision support systems
-- Blood glucose level prediction
• System description papers detailing results of the BGLP Challenge
• Scientific papers presenting new research in machine learning for blood glucose level prediction