Neural Network Based Forecasting of Bacteria-Antibiotic Interactions For Infectious Disease Control
نویسندگان
چکیده
Bacterial sensitivity and resistivity to any antibiotic tends to undergo temporal fluctuations. The clinical consequence of such behaviour is reduced effectiveness of a particular antibiotic to treat a specific bacterial infection. The forecasting system described in this paper uses a backpropagation neural network to model time series derived from bacteria-antibiotic sensitivity and resistivity. Our objective is to obtain forecasted values for the sensitivity and resistivity parameters, which could then be used to guide physicians with regards to choice of medication (mainly antibiotic) to treat a particular infection. The data used in our analysis was obtained from long-term clinical observation of 13 types of bacterial infections, each treated using a range of 10 to 15 different antibiotics. Preliminary results indicate our system is capable of generating highly accurate forecasts given sufficient past data on bacteria-antibiotic interaction. The system features a client-server based WWW interface that allows for projections to be requested for and displayed
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