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MLMH 2018 : 2018 KDD workshop on Machine Learning for Medicine and Healthcare

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Link: https://mlmhworkshop.github.io/mlmh-2018/
 
When Aug 20, 2018 - Aug 20, 2018
Where London
Submission Deadline May 25, 2018
Notification Due Jun 8, 2018
Categories    machine learning   health informatics   precision medicine   artifical intelligence
 

Call For Papers

# MLMH 2018

2018 KDD workshop on Machine Learning for Medicine and Healthcare.
London, United Kingdom.
August 20, 2018

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CALL FOR PAPERS
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4 page submissions due by May 25, 2018

Over the recent years, the decreasing cost of data acquisition and ready
availability of data sources such as Electronic Health records (EHR), claims,
administrative data and patient-generated health data (PGHD), as well as
unstructured data, have led to an increased focus on data-driven and ML methods
for medical and healthcare domain. From the systems biology point of view,
large multimodal data typically including omics, clinical measurements, and
imaging data are now readily available. Valuable information for obtaining
mechanistic insight into the disease is also currently available in
unstructured formats for example in the scientific literature. The storage,
integration, and analysis of these data present significant challenges for
translational medicine research and impact on the effective exploitation of the
data. Furthermore, intelligent analysis of observational data from EHR and PGHD
sources and integration of insights generated from the same to the system
biology sphere can greatly improving patient experience, outcome, and improving
the overall health of the population while reducing per capita cost of care.
However, the black-box nature, inherent in some of the best performing ML
methods, has widened the gap between how human and machines think and often
failed to provide explanations to make insights actionable. In the new era with
users of “right for explanation”, this is detrimental to the adoption in
practice. To drive the usage of such rich yet heterogeneous datasets into
actionable insights, we aim to bring together a wide array of stakeholders,
including practitioners, biomedical and data science specialists, and industry
solution subject matter experts. We will seek to start discussions in the area
of precision medicine as well as the importance of interpretability of ML
models towards the increased practical use of ML in medicine and healthcare.


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Important dates:
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* Paper Submission: May 25, 2018
* Acceptance Notice: Jun 8, 2018
* Workshop Date: Aug 20, 2018

All deadlines correspond to 11:59 PM Pacific Standard Time

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Submission instructions:
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We invite full papers, as well as work-in-progress on the application of
machine learning for precision medicine and healthcare informatics. Topics may
include, but not limited to, the following topics (For more information see
workshop overview)

* Data Standards for Translational Medicine Informatics
* Analysis of large scale electronic health records or patient-generated health data records
* Visualisation of complex and dynamic biomedical networks
* Disease Subtype Discovery for Precision Medicine
* Interpretable Machine Learning for biomedicine and healthcare
* Deep learning for biomedicine

Papers must be submitted in PDF format to
https://easychair.org/conferences/?conf=mlmh2018 and formatted according to the
new Standard ACM Conference Proceedings Template . Papers must be a maximum
length of 4 pages, including references.

The program committee will select the papers based on originality,
presentation, and technical quality for spotlight and/or poster presentation.

The best selected student paper will be granted with a $1,000 travel grant. Please
send a note to mlmhworkshop@googlegroups.com to indicate that you would like to be considered in your
submission.

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Organizers:
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* Mansoor Saqi, Imperial College London, UK
* Prithwish Chakraborty, IBM Research, USA
* Irina Balaur, EISBM, Lyon, France
* Paul Agapow, ICL, UK
* Scott Wagers, BioSci Consulting, Belgium
* Pei-Yun Sabrina Hsueh, IBM Research, USA
* Fred Rahmanian, Geneia, USA
* Muhammad Aurangzeb Ahmad, University of Washington, USA

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