A NLP Pipeline for the Automatic Extraction of Microorganisms Names from Microbiological Notes

Mora, Sara; Attene, Jacopo; Gazzarata, Roberta; Parruti, Giustino

doi:10.3233/shti210589

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…However, this information is currently only available in an unstructured format and thus impractical for analysis. Due to the rapidly increasing number of clinical free texts, various groups made efforts to work towards an automated analysis of such texts by analyzing and classifying clinical free texts by natural language processing (NLP), a subfield of machine learning [3][4][5][6][7][8]. In previous studies, automatic extraction of date and time references [9] and classification into predefined categories [10] were performed on notes of the HerzMobil program.…”

Section: Introductionmentioning

confidence: 99%

Classification of Clinical Notes from a Heart Failure Telehealth Network

Wiesmüller,

Lauschenski,

Baumgartner

et al. 2023

Caring Is Sharing – Exploiting the Value in Data for Health and Innovation

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Heart failure is a common chronic disease which is associated with high re-hospitalization and mortality rates. Within the telemedicine-assisted transitional care disease management program HerzMobil, monitoring data such as daily measured vital parameters and various other heart failure related data are collected in a structured way. Additionally, involved healthcare professionals communicate with one another via the system using free-text clinical notes. Since manual annotation of such notes is too time-consuming for routine care applications, an automated analysis process is needed. In the present study, we established a ground truth classification of 636 randomly selected clinical notes from HerzMobil based on annotations of 9 experts with different professional background (2 physicians, 4 nurses, and 3 engineers). We analyzed the influence of the professional background on the inter annotator reliability and compared the results with the accuracy of an automated classification algorithm. We found significant differences depending on the profession and on the category. These results indicate that different professional backgrounds should be considered when selecting annotators in such scenarios.

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Section: Introductionmentioning

confidence: 99%

Classification of Clinical Notes from a Heart Failure Telehealth Network

Wiesmüller,

Lauschenski,

Baumgartner

et al. 2023

Caring Is Sharing – Exploiting the Value in Data for Health and Innovation

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“…Specifically, the ability to use all EHR parts based on natural language (anamnesis, clinical diary and hospital discharge letter) for research is very promising. In this respect, the interoperability features of the EHR can interface effectively with terminology management systems [41][42][43][44][45][46] and with natural language processing systems.…”

Section: Discussionmentioning

confidence: 99%

An Interoperable Electronic Health Record System for Clinical Cardiology

et al. 2022

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Currently in hospitals, there are several separate information systems that manage, very often autonomously, the patient’s personal, clinical and diagnostic data. An electronic health record system has been specifically developed for a cardiology ward and it has been designed “ab initio” to be fully integrated into the hospital information system and to exchange data with the regional health information infrastructure. All documents have been given as Health Level 7 (HL7) clinical document architecture and messages are sent as HL7-Version 2 (V2) and/or HL7 Fast Healthcare Interoperability Resources (FHIR). Specific decision support sections for specific aspects have also been included. The system has been used for more than three years with a good level of satisfaction by the users. In the future, the system can be the basis for secondary use for clinical studies, further decision support systems and clinical trials.

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“…This paper is an extension of work originally presented at the 18th International Conference on Wearable, Micro, and Nano Technologies for Personalized Health (pHealth 2021) titled "A NLP pipeline for the automatic extraction of microorganisms names from microbiological notes" [28]. The extended version addresses the problem of managing the national and international terminology systems linked to the project and of filtering clinical notes in order to exclude nonsignificant sentences.…”

Section: Introductionmentioning

confidence: 99%

A NLP Pipeline for the Automatic Extraction of a Complete Microorganism’s Picture from Microbiological Notes

Mora

Attene²,

Gazzarata³

et al. 2022

JPM

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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.

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A NLP Pipeline for the Automatic Extraction of Microorganisms Names from Microbiological Notes

Cited by 4 publications

References 3 publications

Classification of Clinical Notes from a Heart Failure Telehealth Network

Classification of Clinical Notes from a Heart Failure Telehealth Network

An Interoperable Electronic Health Record System for Clinical Cardiology

A NLP Pipeline for the Automatic Extraction of a Complete Microorganism’s Picture from Microbiological Notes

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