Extraction of medical knowledge from a full-text description for predicting resistant bacteria infection

Sandi Mikuš (2018) Extraction of medical knowledge from a full-text description for predicting resistant bacteria infection. MSc thesis.

Preview

PDF Download (12Mb)

Abstract

Resistant microorganisms are causing more and more problems in healthcare. Number of antibiotic-resistant microorganisms is growing faster than the number of newly discovered antibiotics. Wrongfully chosen antibiotics during treatment can also result in a greater resilience of the microorganisms. There exist several resistant microorganisms but we will focus on one, Escherichia coli which produces ESBL enzymes. Patients usually start receiving proper antibiotic treatment when doctors get microbiological reports (which takes around two days). We try to predict if a patient has the previously mentioned bacteria E. coli which produces ESBL enzymes, by using a medical report written in a natural language (which consists of the patient's history and status). Even if we achieved high specificity (90% with SVM and 86% with Naive Bayesian classifier) we can not use our models due to too low sensitivity (28% with SVM and 33% with Naive Bayesian classifier). Due to seriousness of the problem with resistant microorganisms it is required to have both metrics (specificity and sensitivity) high. In order to build better models we have to increase number of medical examination reports and maybe include additional results from other medical examinations.

Actions (login required)