A Comprehensive Natural Language Processing Pipeline for the Chronic Lupus Disease.
Stud Health Technol Inform
; 316: 909-913, 2024 Aug 22.
Article
in En
| MEDLINE
| ID: mdl-39176940
ABSTRACT
Electronic Health Records (EHRs) contain a wealth of unstructured patient data, making it challenging for physicians to do informed decisions. In this paper, we introduce a Natural Language Processing (NLP) approach for the extraction of therapies, diagnosis, and symptoms from ambulatory EHRs of patients with chronic Lupus disease. We aim to demonstrate the effort of a comprehensive pipeline where a rule-based system is combined with text segmentation, transformer-based topic analysis and clinical ontology, in order to enhance text preprocessing and automate rules' identification. Our approach is applied on a sub-cohort of 56 patients, with a total of 750 EHRs written in Italian language, achieving an Accuracy and an F-score over 97% and 90% respectively, in the three extracted domains. This work has the potential to be integrated with EHR systems to automate information extraction, minimizing the human intervention, and providing personalized digital solutions in the chronic Lupus disease domain.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Natural Language Processing
/
Electronic Health Records
/
Lupus Erythematosus, Systemic
Limits:
Humans
Language:
En
Journal:
Stud Health Technol Inform
Journal subject:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Year:
2024
Document type:
Article
Affiliation country: