*The 2nd Workshop on Knowledge Discovery from Healthcare Data, Co-located with IJCAI 2017, Melbourne, Australia* https://sites.google.com/site/kdhijcai2017/
The Knowledge Discovery from Healthcare Data (KDH) workshop series was established in 2016 to present AI research efforts to solve pressing problems in healthcare. The workshop series aims to bring together clinical and AI researchers to foster collaborative discussions. Following a successful 2016 KHD workshop <https://sites.google.com/site/ijcai2016kdhealth/home> and aligning with this year’s IJCAI theme of autonomy, the 2017 KDH workshop will support areas of research covered by the novel concept of *learning healthcare systems <https://sites.google.com/site/kdhijcai2017/special-tracks>*. Important Dates *May 5, 2017 Paper submission deadlineJune 5, 2017 Paper acceptance notificationJuly 5, 2017 Camera ready version dueAugust 19, 2017 Workshop* Publication - The papers accepted for KDH 2017 will be published in a CEUR-WS.org electronic *i**nternational proceedings volume *indexed by Google Scholar and DBLP. - Selected papers will be invited to submit* extended versions *to the *Journal of Health Informatics Research* <http://www.springer.com/computer/information+systems+and+applications/journal/41666>, published by Springer. Contributions are welcome in areas including, but not limited to, the following: 1. Knowledge Acquisition and Processing: - Ontology based data/system integration - Integration and application of Biomedical Ontologies and Terminologies - Multiscale data-Âintegration - Knowledge graph construction and utilisation from medical data - Knowledge-driven approaches for information retrieval - Procedural knowledge extraction from health-care databases. - Modelling with missing or biased data - Natural language processing and biomedical named entity recognition. - Knowledge validation, eg. checking compliance with guidelines and protocols 2. Knowledge Representation and Reasoning: - Knowledge representation for health-care processes. - Formalization of medical processes and knowledge-based health-care models. - Temporal knowledge representation, reasoning and exploitation. - Qualitative models for representing medical knowledge and processes. - Knowledge combination and adaptation for health-care processes. 3. Mining, Learning and Pattern Recognition: - Probabilistic analysis in medicine - Applications of Machine Learning techniques in health and biomedicine - Parallel Machine Learning approaches biomedical a nd health applications - Artificial neural network models or deep learning approaches for healthcare data analytics - Development of novel diagnostic and prognostic tests utilising quantitative data analysis - Predictive and prescriptive analyses of healthcare data - Visual Analytics in Biomedicine - Biologically inspired frameworks for Innovative healthcare and biomedical systems - Evolutionary computation learning techniques for ultra large biomedical data - Machine Learning paradigms for predictive modelling of complex diseases 4. Autonomous and Multi-agent Systems: - AI methods in Telemedicine and eHealth - Mobile agents in hospital environment - Applications of AI solutions for Ambient Assisted Living - Patient monitoring and diagnosis through autonomous processes - Automation of clinical trials, including implementation of adaptive and platform trial designs. - Applications of wearables in healthcare - Personalised patient-centred system - Autonomous and remote care delivery.
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