1. Machine learning models predicting multidrug resistant urinary tract infections using 'DsaaS'
- Author
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Leonardo Vito, Elisa Marcelli, Emanuela Merelli, Alessio Mancini, Renato De Leone, Sandra Pucciarelli, and Marco Piangerelli
- Subjects
Male ,Support Vector Machine ,Computer science ,Big data ,computer.software_genre ,Biochemistry ,Multi drug resistance ,Nosocomial infection ,0302 clinical medicine ,Structural Biology ,Drug Resistance, Multiple, Bacterial ,lcsh:QH301-705.5 ,Antibiotic stewardship ,0303 health sciences ,Artificial neural network ,Applied Mathematics ,Middle Aged ,Classification ,Regression ,Computer Science Applications ,Italy ,Area Under Curve ,030220 oncology & carcinogenesis ,Urinary Tract Infections ,Unsupervised learning ,lcsh:R858-859.7 ,Female ,F1 score ,Algorithms ,Urinary system ,Machine learning ,lcsh:Computer applications to medicine. Medical informatics ,Models, Biological ,03 medical and health sciences ,Antibiotic resistance ,Humans ,Molecular Biology ,Aged ,030304 developmental biology ,Receiver operating characteristic ,business.industry ,Research ,Support vector machine ,Multiple drug resistance ,Statistical classification ,Data science pipeline ,ROC Curve ,lcsh:Biology (General) ,Neural Networks, Computer ,Artificial intelligence ,business ,computer - Abstract
Background The scope of this work is to build a Machine Learning model able to predict patients risk to contract a multidrug resistant urinary tract infection (MDR UTI) after hospitalization. To achieve this goal, we used different popular Machine Learning tools. Moreover, we integrated an easy-to-use cloud platform, called DSaaS (Data Science as a Service), well suited for hospital structures, where healthcare operators might not have specific competences in using programming languages but still, they do need to analyze data as a continuous process. Moreover, DSaaS allows the validation of data analysis models based on supervised Machine Learning regression and classification algorithms. Results We used DSaaS on a real antibiotic stewardship dataset to make predictions about antibiotic resistance in the Clinical Pathology Operative Unit of the Principe di Piemonte Hospital in Senigallia, Marche, Italy. Data related to a total of 1486 hospitalized patients with nosocomial urinary tract infection (UTI). Sex, age, age class, ward and time period, were used to predict the onset of a MDR UTI. Machine Learning methods such as Catboost, Support Vector Machine and Neural Networks were utilized to build predictive models. Among the performance evaluators, already implemented in DSaaS, we used accuracy (ACC), area under receiver operating characteristic curve (AUC-ROC), area under Precision-Recall curve (AUC-PRC), F1 score, sensitivity (SEN), specificity and Matthews correlation coefficient (MCC). Catboost exhibited the best predictive results (MCC 0.909; SEN 0.904; F1 score 0.809; AUC-PRC 0.853, AUC-ROC 0.739; ACC 0.717) with the highest value in every metric. Conclusions the predictive model built with DSaaS may serve as a useful support tool for physicians treating hospitalized patients with a high risk to acquire MDR UTIs. We obtained these results using only five easy and fast predictors accessible for each patient hospitalization. In future, DSaaS will be enriched with more features like unsupervised Machine Learning techniques, streaming data analysis, distributed calculation and big data storage and management to allow researchers to perform a complete data analysis pipeline. The DSaaS prototype is available as a demo at the following address: https://dsaas-demo.shinyapps.io/Server/
- Published
- 2020
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