Data integration and trend analysis for surveillance of antimicrobial resistance

Teodoro, Douglas

doi:10.13097/archive-ouverte/unige:23962

Advanced search

Browse by...

Deposit

Highlights

More informations

Title		Data integration and trend analysis for surveillance of antimicrobial resistance
Author		Teodoro, Douglas
Directors		Ruch, Patrick Appel, Ron David
Defense		Thèse de doctorat : Univ. Genève, 2012 - Sc. 4479 - 2012/10/17
Abstract		Antimicrobial resistance is a major worldwide public health problem. The misuse of antimicrobial agents and the delay in spotting emerging and outbreak resistances in current biosurveillance and monitoring systems are regarded by health bodies as underlying causes of increasing resistance. In this thesis, we explore novel methods to monitor and analyze antimicrobial resistance trends to improve existing biosurveillance systems. More specifically, we investigate the use of semantic technologies to foster integration and interoperability of interinstitutional and cross-border microbiology laboratory databases. Additionally, we research an original, fully data-driven trend analysis method based on trend extraction and machine learning forecasting to enhance antimicrobial resistance analyses.
Keywords		Data integration — Semantic web — Machine learning — Forecasting — Biosurveillance — Antimicrobial resistance
Identifiers		DOI: 10.13097/archive-ouverte/unige:23962 URN: urn:nbn:ch:unige-239627
Full text		Thesis (9 MB) - Free access
Structures		Faculté des sciences / Département d'informatique Centres et instituts / Centre universitaire d'informatique Faculté de médecine / Section de médecine clinique / Département de radiologie et informatique médicale
Citation (ISO format)		TEODORO, Douglas. Data integration and trend analysis for surveillance of antimicrobial resistance. Université de Genève. Thèse, 2012. doi: 10.13097/archive-ouverte/unige:23962 https://archive-ouverte.unige.ch/unige:23962