A classification framework for exploiting sparse multi-variate temporal features with application to adverse drug event detection in medical records

Francesco Bagattini, Isak Karlsson, Jonathan Rebane, Panagiotis Papapetrou
2019 BMC Medical Informatics and Decision Making  
Adverse drug events (ADEs) as well as other preventable adverse events in the hospital setting incur a yearly monetary cost of approximately $3.5 billion, in the United States alone. Therefore, it is of paramount importance to reduce the impact and prevalence of ADEs within the healthcare sector, not only since it will result in reducing human suffering, but also as a means to substantially reduce economical strains on the healthcare system. One approach to mitigate this problem is to employ
more » ... dictive models. While existing methods have been focusing on the exploitation of static features, limited attention has been given to temporal features.
doi:10.1186/s12911-018-0717-4 pmid:30630486 pmcid:PMC6327495 fatcat:hu3g2o7y4zaflag4avi7ya4a7i