Machine Learning for Health Care Applications
an ICML 2008 workshop
July 9, 2008, Helsinki, Finland
Health-care applications have been and continue to be the
source of inspiration for many areas of artificial intelligence
research. Many advances in various sub-specialties of AI have been
inspired by challenges posed by medical problems. A new challenge for
AI in general, but machine learning in particular, arises from the
wealth and variety of data
generated in modern medical and health-care settings. Extensive
electronic health and medical records---with thousands of fields
recording patient conditions, diagnostic tests, treatments, outcomes,
and so on---provide an unprecedented source of information that
can provide clues leading to potential improvements in disease
detection, chronic disease management, design of clinical trials, and
other aspects of health-care. The purpose of this workshop is to bring
together machine learning researchers interested in problems and
applications in health-care, with the goal of exchanging ideas and
perspectives, identifying important and challenging applications, and
raising awareness of potential health-care applications in the machine
The workshop program will consist of presentations by invited speakers, and oral and poster presentations by authors
of extended abstracts submitted to the workshop. Confirmed invited speakers for the workshop are:
Riccardo Bellazzi, University of Pavia, Italy,
and Jeff Schneider, Carnegie Mellon University, USA.
The workshop and invited speakers are supported by AICML.
- Full papers due: June 27, 2008
- Workshop: July 9, 2008
Contact e-mail: firstname.lastname@example.org.
Room: S5, 3rd floor
9:00-9:50 Invited Talk: Methods and Tools for Mining Multivariate Temporal Data in Clinical and Research Applications
9:50-10:10 Machine Learning Techniques in Intensive Care Monitoring
Wiebke Sieben, Karen Schettlinger, Silvia Kuhls, Michael Imhoff, Ursula Gather
10:10-10:30 Probabilistic Modeling of Sensor Artifacts in Critical Care
Norm Aleks, Stuart Russell, Michael G. Madden, Diane Morabito,
Kristan Staudenmayer, Mitchell Cohen, Geoffrey Manley
10:30-11:00 Coffee break
11:00-11:20 Machine Learning to Automate the Assignment of Diagnosis Codes to Free-text Radiology Reports: a Method Description
Hanna Suominen, Filip Ginter, Sampo Pyysalo, Antti Airola, Tapio Pahikkal,
Sanna Salanter, Tapio Salakoski
11:20-11:40 Conditional Anomaly Detection Methods for Patient-Management Alert Systems
Michal Valko, Gregory Cooper, Melissa Saul, Amy Seybert, Shyam Visweswaran,
11:40-12:00 Bayesian Modelling of Multi-View Mammography
Nivea Ferreira, Marina Velikova, Peter Lucas
12:00-12:20 Facilitating Clinico-Genomic Knowledge Discovery by Automatic Selection of KDD Processes
Natalja Punko, Stefan RŠuping
14:30-15:20 Invited Talk: Machine Learning for in vivo Central Nervous System (CNS) Drug Discovery
15:20-15:40 Identifying Active Compounds from Chinese Medicinal Plants via Causal Variable Selection
15:40-16:00 Optimizing Treatment Strategies for Epilepsy Using Reinforcement Learning
Joelle Pineau, Arthur Guez, Robert D. Vincent, Massimo Avoli
16:00-16:30 Coffee break
16:30-18:00 Poster session
Detection of Keratoconus by Semi-Supervised Learning
Deepthi Cheboli, Balaraman Ravindran
Machine Learning for Personalized Medicine: Will This Drug Give Me a Heart Attack?
Jesse Davis, Eric Lantz, David Page, Jan Struyf, Peggy Peissig, Humberto Vidaillet, Michael Caldwell
Pattern Discovery in Intensive Care Data through Sequence Alignment of Qualitative Trends Data:
Proof of Concept on a Diuresis Data Set
Martijn Devisscher, Bernard De Baets, Ingmar Nopens, Johan Decruyenaere, Dominique Benoit
Improving Medical Predictive Models via Likelihood Gamble Pricing
Glenn Fung, Harald Steck, Shipeng Yu, Phan Giang
Explaining Artificial Neural Network Ensembles: A Case Study with Electrocardiograms from Chest Pain Patients
Michael Green, Ulf Ekelund, Lars Edenbrandt, Jonas Bjork, Jakob Lundager Forberg, Mattias Ohlsson
Learning Outbreak Regions in Bayesian Spatial Scan Statistics
Maxim Makatchev, Daniel B. Neill
Detecting Heartbeats in the Ballistocardiogram with Clustering
Joonas Paalasmaa, Mika Ranta
Classification of Normal and Hypoxic Fetuses using System Identification from Intra-Partum Cardiotocography
Philip A. Warrick, Emily F. Hamilton, Robert E. Kearney, Doina Precup
Visualisation of High-Dimensional Data for Very Large Data Sets
David Wong, Iain Strachan, Lionel Tarassenko
Riccardo Bellazzi, PhD, University of Pavia, Italy.
Methods and tools for mining multivariate temporal data in clinical and
Abstract. In all human activities, automatic data collection pushes towards the
development of tools that are able to handle and analyze data in a
computer-supported fashion. In the majority of the application areas, this
task cannot be accomplished without using the available knowledge on the
domain or on the data analysis process. This need becomes essential in
biomedical applications, since medical decision making needs to be
supported by arguments based on medical and pharmacological
knowledge. It is therefore important to study the computational methods
for data analysis aimed to narrow the gap between data gathering and data
comprehension, as well as their applications in medicine, health care,
biology and pharmacology. Methods for analyzing data by integrating the
available knowledge on the domain (Intelligent Data Analysis) and for
extracting knowledge from large data-bases (Data Mining) have been
investigated over the last few years. In this talk I will deal with the
methods for dealing with multivariate temporal data. I will describe in detail two
approaches that have been successfully applied in different application
problems: the extraction of temporal association rules and the automated
construction of dynamic probabilistic models called Dynamic Bayesian
Networks. I will show their application in the analysis of hemodialysis
monitoring time series, health care administrative data and gene
Jeff Schneider, PhD, Carnegie Mellon University, USA.
Machine Learning for in vivo CNS Drug Discovery.
Abstract. Researchers in machine learning have made great strides in modeling and
optimization of commercial/industrial processes. A more recent trend is to
observe that the scientific method is a process that can be modeled and
optimized with similar techniques. In this talk we consider a specific
example of that: discovery of central nervous system drugs (e.g.
antidepressants antipsychotics, anxiolytics, etc.) using in vivo behavioral
testing. Algorithms will be discussed in the following areas: the use of
kernel density estimators to provide improved posterior probabilities in
multi-class applications; the use of semi-supervised learning to handle
training data with uncertain class labels; and the use of active learning
to control experimentation in the discovery process.
Instructions for full paper submissions
The final papers are due on June 27, 2008. The papers should be at most eight
pages long and follow the ICML submission format for final papers.
The ICML formatting instructions can be found
here. Please email your submissions directly to Csaba Szepesvari, a co-chair
of the workshop, at email@example.com. The preferred submission
format is pdf.
When writing the final paper you should take into account all of the
reviewers' feedback and suggestions. In addition, all papers, in
particular those that are more theoretical, must clearly establish the
relevance of the work to health care.
Please note the final version of the paper will be reviewed by workshop
chairs to assure the high quality standard. A failure to address any of
the above points may result in your paper not being published on the
workshop website before the workshop.
Instructions for the workshop presenters
- Oral presentations for the Health Care Applications workshop will be 20 minutes long.
Your talks should take at most 17 minutes, with a minimum of 3 minutes reserved for questions.
- Posters presentations Follow the ICML instructions to be found here.
Call for papers
We seek paper submissions developing new or applying existing ML methods to medical and health-care
applications. The topics of interest include, but are not limited to:
Submissions addressing theoretical problems should
clearly outline the expected impact of the proposed solution to the medical field.
- disease modeling and disease detection
- patient monitoring and alarm systems
- treatment outcome predictions
- optimization of patient-management workflows
- biomedical text mining
- patient record anonymisation
- integration of biomedical data sources and domain knowledge
- translational bioinformatics
- design of clinical trials
Please submit an extended abstract (1 to 3 pages in two-column ICML
format) to the workshop email address firstname.lastname@example.org.
The abstract should include author names, affiliations, and contact
information. Papers will be reviewed by the members of the program
committee and decisions on the acceptance together with the reviewers'
feedback will be emailed back to authors on May 17, 2008. Authors of
accepted extended abstracts are encouraged to submit a full version of their paper. All submissions will be published on the conference web site.
In addition to authors of accepted papers presenting their works we are
planning on having 4-5 invited talks.
Past Important Dates
- Extended abstract submission deadline: May 1, 2008
- Acceptance notification: May 17, 2008
- Constantin Aliferis
- Gregory Cooper
- Russ Greiner
- Milos Hauskrecht
- David Heckerman
- Peter Lucas
- Subramani Mani
- Lucila Ohno-Machado
- Nils Peek
- Pascal Poupart
- Marco Ramoni
- Stuart Russell
- Dale Schuurmans
- Csaba Szepesvari
- Shyam Viswesvaran
- Chris Williams
- Blaz Zupan
Upcoming Related Events
If you know of any other events please send us an e-mail at email@example.com.
The First Louhi Conference on Text and Data Mining of Clinical Documents,
September 3-4, 2008
Third International Symposium on Semantic Mining in Biomedicine,
September 1-3, 2008
- MMD Workshop KDD 2008
Workshop on Mining Medical Data (KDD 2008),
August 24-27, 2008
Intelligent Data Analysis in Biomedicine and Pharmacology
- A colloquium in conjunction with the American Medical Informatics Association Annual Symposium 2008,
November 7, 2008
- AIME'09 - Artificial Intelligence in Medicine 2009
July 18-22, 2009