Feature Selection based Multivariate Time Series Forecasting: An Application to Antibiotic Resistance Outbreaks Prediction

Publication date: Available online 19 February 2020Source: Artificial Intelligence in MedicineAuthor(s): Fernando Jiménez, José Palma, Gracia Sánchez, David Marín, Francisco Palacios, M.D, Lucía López, M.DAbstractAntimicrobial resistance has become one of the most important health problems and global action plans have been proposed globally. Prevention plays a key role in these actions plan and, in this context, we propose the use of Artificial Intelligence, specifically Time Series Forecasting techniques, for predicting future outbreaks of Methicilin-resistant Staphylococcus aereus (MRSA). Infection incidence forecasting is approached as a Feature Selection based Time Series Forecasting problem using multivariate time series composed of incidence of Staphylococcus Aereus Methicilin-sensible and MRSA infections, influenza incidence and total days of therapy of both of Levoflaxin and Oseltamivir antimicrobials. Data were collected from the University Hospital of Getafe (Spain) from January 2009 to January 2018, using months as time granularity. The main contributions of the work are the following: the applications of wrapper feature selection methods where the search strategy is based on multi-objective evolutionary algorithms (MOEA) along with evaluators based on the most powerful state-of-the-art regression algorithms. The performance of the feature selection methods has been measured using the root mean square error (RMSE) and mean absolute error (MAE) performance met...
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research