**important dates were updated on 22 April 2018**
** please check: https://sites.google.com/view/kdhd-2018/dates**

The Knowledge Discovery in 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. This year, the focus is on learning healthcare systems. For the first time, this workshop will feature a challenge: The Machine Learning Blood Glucose Level Prediction Challenge.

The notion of the learning healthcare system has been put forward to denote the translation of routinely collected data into knowledge that drives the continual improvement of medical care. This notion has been described in many forms, but each follows a similar cycle of assembling, analyzing and interpreting data from multiple sources (clinical records, guidelines, patient-provided data including wearables, omic data, etc..), followed by feeding the acquired knowledge back into clinical practice. This framework aims to provide personalised recommendations and decision support tools to aid both patients and care providers, to improve outcomes and personalise care.

This framework also extends the range of actions possible in response to patient monitoring data, for example, alerting patients or automatically adjusting insulin doses when blood glucose levels are predicted to go out of range. Blood glucose level prediction is a challenging task for AI researchers with the potential to improve the health and wellbeing of people with diabetes. In the Machine Learning Blood Glucose Level Prediction (BGLP) Challenge, researchers will come together to compare the efficacy of different machine learning prediction approaches on a standard set of real patient data. Those interested in participating in this Challenge will find additional information at: https://sites.google.com/view/kdhd-2018/bglp-challenge

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


*Submission & Format: Submissions can be made as:
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. Papers reporting on original research in blood glucose level prediction, but
not BGLP Challenge system description papers , should be formatted as long papers and
submitted by the deadline for all workshop papers.
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. BGLP Challenge system description papers should be formatted as short
papers; however, these papers have their own submission deadline.
Both long and short papers must be formatted according to IJCAI guidelines and submitted
electronically through EasyChair: https://easychair.org/conferences/?conf=kdh2018.

*Important Dates (**updated on 22 April 2018**)
-Technical Papers
● Paper Submission Deadline: May 4, 2018 (UTC-12) (extended deadline)
● Notification of Acceptance: May 28, 2018
● Camera-Ready Deadline: June 21, 2018

-BGLP Challenge
● Training and Development Data Release: Feb 21, 2018
● Test Data Release: May 21, 2018
● Results Submission Deadline: June 7, 2018
● System Description Paper Submission Deadline: June 21, 2018
● Notification Date: June 28, 2018
● Camera-Ready Deadline: July 7, 2018

*Workshop Organizers
Kerstin Bach, Norwegian University of Science and Technology (Norway)
Razvan Bunescu, Ohio University (USA)
Oladimeji Farri, Philips Research North America (USA)
Aili Guo, Ohio University (USA)
Sadid Hasan, Philips Research North America (USA)
Zina Ibrahim, King's College London (UK)
Cindy Marling, Ohio University (USA)
Jesse Raffa, Massachusetts Institute of Technology (USA)
Jonathan Rubin, Philips Research North America (USA)
Honghan Wu, University of Edinburgh (UK)

*Program Committee Members (Tentative)
Behzad Aalipur, The University of Texas at El Paso (USA)
Rui Abreu, Palo Alto Research Center (USA)
Ali Cinar, Illinois Institute of Technology (USA)
J. Manuel Colmenar, Universidad Rey Juan Carlos (Spain)
Alexandra Constantin, Bigfoot Biomedical (USA)
Ivan Contreras, University of Girona (Spain)
Vivek Datla, Philips Research North America (USA)
Andrea Facchinetti, University of Padova (Italy)
Gaurang Gavai, Palo Alto Research Center (USA)
Agnes Grünerbl, TU Kaiserslautern (Germany)
Winston Haynes, Stanford University (USA)
Pau Herrero, Imperial College London (UK)
J. Ignacio Hidalgo, Universidad Complutense de Madrid (Spain)
Jim Kirkendall, Midas+, A Xerox Company (USA)
David Klonoff, University of California, San Francisco (USA)
Noël Malod-Dognin, University College London (UK)
Samhar Mahmoud, King’s College London (UK)
Stewart Massie, Robert Gordon University (UK)
Roque Marín Morales, University of Murcia (Spain)
Oscar Mayora, CREATE-NET, University of Trento (Italy)
Antonio Moreno, Universitat Rovira i Virgili, Spain
Laura Moss, University of Glasgow (UK)
Stefania Montani, Università del Piemonte Orientale A. Avogadro (Italy)
Marzeih Nabi, Palo Alto Research Center (USA)
Razieh Nabi, The University of Texas at El Paso (USA)
Bob Price, Palo Alto Research Center (USA)
Elhadi Shakshuki, Acadia University (Canada)
John Shawe-Taylor, University College London (UK)
Saied Shahraz, Tufts Medical School (USA)
Syed Sibte Raza Abidi, Dalhousie University (Canada)
Javier Vázquez Salceda, University of Barcelona (Spain)
Sadiq Sani, Robert Gordon University (UK)
Alexander Schliep, Gothenburg University (Sweden)
Giovanni Sparacino, University of Padova (Italy)
Nigam Shah, Stanford University (USA)
Annette ten Teije, Vrije Universiteit, (Netherlands)
Paolo Terenziani, Universita del Piemonte Orientale (Italy)
Rohit Vashisht, Stanford University (USA)
Josep Vehi, University of Girona (Spain)
Qian Wang, Pennsylvania State University (USA)
Jim Warren, University of Auckland (New Zealand)
Aaron Wilson, Livermore National Lab (USA)
Nirmalie Wiratunga, Robert Gordon University (UK)
Zack Zhu, ETH Zurich (Switzerland)