MLHC 2018 : Machine Learning for Healthcare 2018
Call For Papers
Machine Learning for Healthcare
MLHC is an annual research meeting that exists to bring together two usually insular disciplines: computer scientists with artificial intelligence, machine learning, and big data expertise, and clinicians/medical researchers. MLHC supports the advancement of data analytics, knowledge discovery, and meaningful use of complex medical data by fostering collaborations and the exchange of ideas between members of these often completely separated communities. To pursue this goal, the event includes invited talks, poster presentations, panels, and ample time for thoughtful discussion and robust debate.
MLHC has a rigorous peer-review process and an (optional) archival proceedings through the Journal of Machine Learning Research proceedings track. You can access the inaugural proceedings here: http://www.jmlr.org/proceedings/papers/v56/
Calls for Papers
Researchers in machine learning --- including those working in statistical natural language processing, computer vision and related sub-fields --- when coupled with seasoned clinicians can play an important role in turning complex medical data (e.g., individual patient health records, genomic data, data from wearable health monitors, online reviews of physicians, medical imagery, etc.) into actionable knowledge that ultimately improves patient care. For the last seven years, this meeting has drawn hundreds of clinical and machine learning researchers to frame problems clinicians need solved and discuss machine learning solutions.
This year we are calling for papers in two tracks: a research track and a clinical abstract track.
Deadline for submission: April 20, 2018
Author notification: June 20, 2018
We invite submissions that describe novel methods to address the challenges inherent to health-related data (e.g., sparsity, class imbalance, causality, temporal dynamics, multi-modal data). We also invite articles describing the application and evaluation of state-of-the-art machine learning approaches applied to health data in deployed systems. In particular, we seek high-quality submissions on the following topics:
Predicting individual patient outcomes
Mining, processing and making sense of clinical notes
Patient risk stratification
Parsing biomedical literature
Brain imaging technologies and related models
Learning from sparse/missing/imbalanced data
Time series analysis with medical applications
Efficient, scalable processing of clinical data
Clustering and phenotype discovery
Methods for vitals monitoring
Feature selection/dimensionality reduction
Text classification and mining for biomedical literature
Exploiting and generating ontologies
ML systems that assist with evidence-based medicine
Research Track Proceedings and Review Process: Accepted submissions will be published through the proceedings track of the Journal of Machine Learning Research. All papers will be rigorously peer-reviewed, and research that has been previously published elsewhere or is currently in submission may not be submitted. However, authors will have the option of only archiving the abstract to allow for future submissions to clinical journals, etc.