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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
* Bio-marker discovery
* Brain imaging technologies and related models
* Learning from sparse/missing/imbalanced data
* Time series analysis with medical applications
* Medical imaging
* 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
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