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:<br>* Predicting individual patient outcomes<br>* Mining, processing and making sense of clinical notes<br>* Patient risk stratification<br>* Parsing biomedical literature<br>* Bio–marker discovery<br>* Brain imaging technologies and related models<br>* Learning from sparse/missing/imbalanced data<br>* Time series analysis with medical applications<br>* Medical imaging<br>* Efficient, scalable processing of clinical data<br>* Clustering and phenotype discovery<br>* Methods for vitals monitoring<br>* Feature selection/dimensionality reduction<br>* Text classification and mining for biomedical literature<br>* Exploiting and generating ontologies<br>* ML systems that assist with evidence–based medicine<br><div><br></div>
Abbrevation
mlforhc
City
Ann Arbor
Country
United States
Deadline Paper
Start Date
End Date
Abstract