2018 KDD workshop on Machine Learning for Medicine and Healthcare.
London, United Kingdom.
August 20, 2018
4 page submissions due by May 8, 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.
- Paper Submission: May 8, 2018
- Acceptance Notice: Jun 8, 2018
- Workshop Date: Aug 20, 2018
All deadlines correspond to 11:59 PM Pacific Standard Time
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.
- 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