Predicting Antibiotic Resistance Phenotypes using Neural Networks

Abstract

Antibiotic resistance is one of the biggest threats to human health. Today it causes 700 000 deaths per year, a number that is estimated to rise to 10 million by 2050. Measuring antibiotic resistance is time consuming and while waiting patients may receive ineffective treatment. It is thus interesting to see what previous measurements can tell us about antibiotics that have not been measured. In this thesis neural network based imputation is investigated as a suitable method for prediction of antibiotic resistant phenotypes. This was done using comprehensive data collected by the EARS-Net. The data contains bacteria from 330000 patients that have been tested for resistance against a range of different antibiotics. In this thesis neural networks were trained to predict missing resistant and susceptible phenotypes based on available resistance data. In addition the networks had access to information about the pathogen and the reporting country. After tuning the number of nodes and training the networks on 85% of the available data we received an average error rate of 5% when testing on the remaining data. The average very major error rate, i.e. the error rate on measurements whose true value were resistant, was 11.8%. The error rates varied for different antibiotics and some had very major error rates below 5%. Neural network based imputation shows promise as a method for predicting antibiotic phenotypes and with further research into what affects the neural networks performance it could be a useful tool when measuring antibiotic resistance.