*The 3rd International Workshop on Knowledge Discovery in Healthcare Data
(KDH) 2018*

Website: https://sites.google.com/view/kdhd-2018/home

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
workshop will be co-located with the 27th International Joint Conference on
Artificial Intelligence and the 23rd European Conference on Artificial
Intelligence (IJCAI-ECAI 2018, https://www.ijcai-18.org/) in Stockholm,
Sweden and the focus will be on learning healthcare systems. For the first
time, this workshop will feature a challenge: The Machine Learning Blood
Glucose Level Prediction Challenge (https://sites.google.com/
view/kdhd-2018/bglp-challenge).



*Call for Papers*
---------------------------

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 personalized 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, organization & 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
   - Personalization and decision support
      - Mobile agents in hospital environment
      - Patient Empowerment through personalized patient-centered 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:

   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. 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
<http://www.google.com/url?q=http%3A%2F%2Fwww.ijcai.org%2Fauthors_kit&sa=D&sntz=1&usg=AFQjCNEYQ1Qx5vaqfJmZXf21mZV1dhKr2A>
and submitted electronically through EasyChair: https://easychair.org/
conferences/?conf=kdh2018
<https://www.google.com/url?q=https%3A%2F%2Feasychair.org%2Fconferences%2F%3Fconf%3Dkdh2018&sa=D&sntz=1&usg=AFQjCNG-RexYJgbJUNciFES_bimXrRvb0w>
.



*Publication*

*Proceedings:* The papers accepted for KDH 2018 will be published in
the CEUR-WS.org
international proceedings volume
<http://www.google.com/url?q=http%3A%2F%2Fceur-ws.org%2F&sa=D&sntz=1&usg=AFQjCNEBm1QvnYY6Vm7SnigPfVyTcXDYgQ>.
This proceedings volume will be published electronically and indexed by
Google Scholar and DBLP.


*Organizing Committee*
-------------------------------

Kerstin Bach, Norwegian University of Science and Technology, Norway

Razvan Bunescu, Ohio University, USA

Oladimeji Farri, Philips Research, USA

Aili Guo, Ohio University, USA

Sadid Hasan, Philips Research, USA

Zina Ibrahim, King's College London, UK

Cindy Marling, Ohio University, USA

Jesse Raffa, Massachusetts Institute of Technology, USA

Jonathan Rubin, Philips Research, USA

Honghan Wu, University of Edinburgh, UK


*Program Committee*
----------------------------

Imon Banerjee, Stanford University

Isabelle Bichindaritz, State University of New York at Oswego

Ali Cinar, Illinois Institute of Technology

Kevin Bretonnel Cohen, University of Colorado School of Medicine

José Manuel Colmenar, Universidad Rey Juan Carlos

Bryan Conroy, Philips Research North America

Sergio Consoli, Philips Research, Data Science Department

Alexandra Constantin, Bigfoot Biomedical

Vivek V Datla, Philips Research North America

Spiros Denaxas, University College London

Franck Dernoncourt, Massachusetts Institute of Technology

Andrea Facchinetti, University of Padova

Michele Filannino, MIT

Pau Herrero, Imperial College London

Ignacio Hidalgo, Universidad Complutense de Madrid

Yuan Ling, Philips Research North America

Bo Liu, Auburn University

Stewart Massie, Robert Gordon University

Claudia Moro, PUCPR

Tristan Naumann, Massachusetts Institute of Technology

Anna Rumshisky, University of Massachusetts, Lowell

Sadiq Sani, Robert Gordon University Aberdeen

Alexander Schliep, Gothenburg University

Rushdi Shams, OneClass - Notesolution Inc.

Giovanni Sparacino, University of Padova

Shawn Stapleton, Philips Research North America

Ozlem Uzuner, George Mason University
Josep Vehi, University of Girona





*Important Dates*
------------------------

*Technical Papers*

   - Paper Submission Deadline: April 23, 2018
   - Notification of Acceptance: May 14, 2018
   - Camera-Ready Deadline: June 8, 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



Please feel free to contact organizing committee members should you have
any questions/concerns.



Thank you.
*Sadid Hasan, PhD.*
Senior Scientist, Artificial Intelligence Lab
Philips Research North America
Web: www.sadidhasan.com
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