Natural Language Processing applied to electronic records: surveillance and detection of health events

Text fields in medical records are a valuable source for Public Health Surveillance but remain underutilized. This study describes the use of natural language processing (NLP) to enhance the identification of suspected cases and monitor disease trends in electronic records from the Urgency and Emergency Network (Rede de Urgência e Emergência - RUE), in the municipality of Rio de Janeiro (MRJ). Texts were pre-processed, and rules were applied to identify individual (measles and rubella) and collective (diarrhea and influenza-like syndrome) events, comparing the results with ICD-10 data from January 2023 to September 2024. A total of 28 suspected measles cases and 33 suspected rubella cases were identified through ICD, while the NLP technique detected an additional 30 suspected cases of measles and 17 of rubella based on patient complaints. Time series of diarrhea and influenza-like syndrome (síndrome gripal - SG), stemming from ICD and complaints, showed a cross-correlation above 0.93 at lag 0. Complaint analysis, particularly after the discontinuation of nonspecific SG ICD codes by RUE management, revealed a greater stability and expanded detection of suspected cases, demonstrating the potential of NLP in epidemiological surveillance in MRJ.