Machine Learning for Health
is now part of CD-MAKE the IFIP Cross Domain
Conference for Machine Learning & Knowledge Extraction
https://cd-make.net
Machine Learning for Health* (MALHE)
*) Health includes all aspects of complete physical, mental and social well-being (WHO definition)
Workshop organized by Andreas HOLZINGER & Igor JURISICA
MALHE – August, 31, 2017
12th International Cross Domain Conference and Workshop (CD-ARES 2017),
Reggio di Calabria, Italy, August 29 – September, 2, 2017
supported by the International Federation of Information Processing IFIP > TC5 and WG 8.4 and WG 8.9
https://cd-ares-conference.eu
https://www.ares-conference.eu
Keynote Talk by Marta MILO, University of Sheffield (UK)
<Bioinformatics, Computational Biology, gene expression analysis, microRNA, Next Generation Sequencing>
Mini Bio: Marta Milo is Lecturer in Computational Biology at the Department of Biomedical Science and is group leader at the Centre 
for Stem Cell Biology at the University of Sheffield. She was a Bioinformatics research fellow at the Sheffield Teaching Hospitals NHS Trust. She holds a PhD in Applied Mathematics and Computer Science from the University of Naples. The main focus of her professional career has been to develop truly interdisciplinary skills, complementing and refining her bioinformatics skills with a deep understanding of the biological nature of the data collected. This is to better identify limitations in the experimental designs and better quantify variations in the data collection and validation. Her work has been concentrating on the analysis and interpretation of high-throughput biological data, with the aim to produce feasible and robust hypotheses for a deeper understanding of the biological systems under study. In quantitative sciences numerical knowledge is not enough to understand and predict systems behaviours that are only partially observed. Since the beginning of 20th century it was clear that predictions of data required an additional “knowledge” to become meaningful. This knowledge needed to be quantified in a way that reflects our prior knowledge of the systems and what we were able to measure. It signed the start of introducing the concept of quantified uncertainty. Marta’s research interests focus on developing computational tools, pipelines, appropriate experimental designs and protocols to assist in improving accuracy and sensitivity in the analysis of biological data.
Topics of MALHE
Research topics covered by this special session focus on machine learning for the application in health. Work with focus on privacy aware machine learning shall consider the PAML session.
Machine learning (ML) is the fastest growing field in computer science, and health is among the greatest application challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. The holy grail is in automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets, or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, defined as “algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human.” This “human-in-the-loop”, “doctor-in-the-loop”, or “expert-in-the-loop” can be highly beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.
This special session will bring together scientists with diverse background, interested in both the underlying theoretical principles as well as the application of such methods for practical use in the biomedical, life sciences and health care domain. The cross-domain integration and appraisal of different fields will provide an atmosphere to foster different perspectives and opinions; it will offer a platform for novel crazy ideas and a fresh look on the methodologies to put these ideas into business.
Accepted Papers will be published in a Springer Lecture Notes in Computer Science LNCS Volume.
Outstanding papers will be invited to extent the work in a Journal special issue (tba).
Schedule:
1) Deadline for submissions: April, 1, 2017
Paper submission via:
https://cd-ares-conference.eu/?page_id=43
2) Notification: May, 1, 2017
3) Camera Ready deadline: June, 1, 2017
4) Special Session: September, 1, 2017
> Conference Venue: Universita Mediterrranea di Reggio Calabria
> Information Reggio
> Lonely Planet Reggio
International Scientific Committee
Each paper will be reviewed by at least three reviewers from the international expert network HCI-KDD and will ensure the highest possible scientific quality (the paper acceptance rate of the last special session was 35 %).
Information about IFIP
Today’s highly interconnected and interdependent world of information systems need a multidisciplinary view to be ready for future research challenges and delivering business solutions. This IFIP supported conference therefore is focused on multidisciplinary aspects across the breadth of Information Systems – fostering integrated machine learning approaches at the core.
The aim of this conference is to foster a forum for researchers and practitioners for discussing and presenting recent research ideas and results across the multiple research domains.
IFIP WG 8.4 (E-Business: Multi-disciplinary research and practice)
IFIP WG 8.9 (Enterprise Information Systems)
IFIP TC 5 (Information Technology Applications)
Call for Papers
MALH-call-for-papers-2017 (pdf, 76kB)
MALH-call-for-papers-2017 (Word docx 41 kB)
MALH-call-for-papers-2017 (txt 4 kB)