The Italian "Istituto Superiore di Sanità" (ISS) identifies hospital-acquired infections (HAIs) as the most frequent and serious complications in healthcare. HAIs constitute a real health emergency and, therefore, require decisive action from both local and national health organizations. Information about the causative microorganisms of HAIs is obtained from the results of microbiological cultures of specimens collected from infected body sites, but microorganisms' names are sometimes reported only in the notes field of the culture reports. The objective of our work was to build a NLP-based pipeline for the automatic information extraction from the notes of microbiological culture reports. We analyzed a sample composed of 499 texts of notes extracted from 1 month of anonymized laboratory referral. First, our system filtered texts in order to remove nonmeaningful sentences. Thereafter, it correctly extracted all the microorganisms' names according to the expert's labels and linked them to a set of very important metadata such as the translations into national/international vocabularies and standard definitions. As the major result of our pipeline, the system extracts a complete picture of the microorganism.

1st Medical Faculty Charles University Prague 12800 Prague Czech Republic

Department of Health Sciences University of Genoa 16132 Genoa Italy

Department of Infectious Diseases AUSL Pescara 65124 Pescara Italy

Department of Informatics Bioengineering Robotics and System Engineering University of Genoa 16145 Genoa Italy

eHealth Competence Center Bavaria Deggendorf Institute of Technology 94469 Deggendorf Germany

Healthropy Corso Italia 15 6 17100 Savona Italy

HL7 Europe 1000 Brussels Belgium

Infectious Diseases Unit IRCCS San Martino Polyclinic Hospital 16132 Genoa Italy

Medical Faculty University of Regensburg 93053 Regensburg Germany

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