@article {1641, title = {Use of Large Language Models to Classify Epidemiological Characteristics in Synthetic and Real-World Social Media Posts About Conjunctivitis Outbreaks: Infodemiology Study.}, journal = {J Med Internet Res}, volume = {27}, year = {2025}, month = {2025 Jul 02}, pages = {e65226}, abstract = {

BACKGROUND: The use of web-based search and social media can help identify epidemics, potentially earlier than clinical methods or even potentially identifying unreported outbreaks. Monitoring for eye-related epidemics, such as conjunctivitis outbreaks, can facilitate early public health intervention to reduce transmission and ocular comorbidities. However, monitoring social media content for conjunctivitis outbreaks is costly and laborious. Large language models (LLMs) could overcome these barriers by assessing the likelihood that real-world outbreaks are being described. However, public health actions for likely outbreaks could benefit more by knowing additional epidemiological characteristics, such as outbreak type, size, and severity.

OBJECTIVE: We aimed to assess whether and how well LLMs can classify epidemiological features from social media posts beyond conjunctivitis outbreak probability, including outbreak type, size, severity, etiology, and community setting. We used a validation framework comparing LLM classifications to those of other LLMs and human experts.

METHODS: We wrote code to generate synthetic conjunctivitis outbreak social media posts, embedded with specific preclassified epidemiological features to simulate various infectious eye disease outbreak and control scenarios. We used these posts to develop effective LLM prompts and test the capabilities of multiple LLMs. For top-performing LLMs, we gauged their practical utility in real-world epidemiological surveillance by comparing their assessments of Twitter/X, forum, and YouTube conjunctivitis posts. Finally, human raters also classified the posts, and we compared their classifications to those of a leading LLM for validation. Comparisons entailed correlation or sensitivity and specificity statistics.

RESULTS: We assessed 7 LLMs for effectively classifying epidemiological data from 1152 synthetic posts, 370 Twitter/X posts, 290 forum posts, and 956 YouTube posts. Despite some discrepancies, the LLMs demonstrated a reliable capacity for nuanced epidemiological analysis across various data sources and compared to humans or between LLMs. Notably, GPT-4 and Mixtral 8x22b exhibited high performance, predicting conjunctivitis outbreak characteristics such as probability (GPT-4: correlation=0.73), size (Mixtral 8x22b: correlation=0.82), and type (infectious, allergic, or environmentally caused); however, there were notable exceptions. Assessing synthetic and real-world posts for etiological factors, infectious eye disease specialist validations revealed that GPT-4 had high specificity (0.83-1.00) but variable sensitivity (0.32-0.71). Interrater reliability analyses showed that LLM-expert agreement exceeded expert-expert agreement for severity assessment (intraclass correlation coefficient=0.69 vs 0.38), while agreement varied by condition type (κ=0.37-0.94).

CONCLUSIONS: This investigation into the potential of LLMs for public health infoveillance suggests effectiveness in classifying key epidemiological characteristics from social media content about conjunctivitis outbreaks. Future studies should further explore LLMs{\textquoteright} potential to support public health monitoring through the automated assessment and classification of potential infectious eye disease or other outbreaks. Their optimal role may be to act as a first line of documentation, alerting public health organizations for the follow-up of LLM-detected and -classified small, early outbreaks, with a focus on the most severe ones.

}, keywords = {Conjunctivitis, Disease Outbreaks, Humans, Language, Large Language Models, Social Media}, issn = {1438-8871}, doi = {10.2196/65226}, author = {Deiner, Michael S and Deiner, Russell Y and Fathy, Cherie and Deiner, Natalie A and Hristidis, Vagelis and McLeod, Stephen D and Bukowski, Thomas J and Doan, Thuy and Seitzman, Gerami D and Lietman, Thomas M and Porco, Travis C} } @article {126, title = {Unbiased Pathogen Detection and Host Gene Profiling for Conjunctivitis.}, journal = {Ophthalmology}, volume = {126}, year = {2019}, month = {2019 08}, pages = {1090-1094}, abstract = {

PURPOSE: The etiology of conjunctivitis is often misdiagnosed. An ideal diagnostic test would identify all possible infectious causes. In this study, we apply unbiased metagenomic RNA deep sequencing (MDS) to identify pathogens causing conjunctivitis.

DESIGN: Molecular study of prospectively collected conjunctival swabs from patients with presumed infectious conjunctivitis.

PARTICIPANTS: Patients with presumed acute infectious conjunctivitis.

METHODS: Conjunctival swabs were collected from patients presenting with acute conjunctivitis. Swabs were processed for MDS. Pathogens were identified using a rapid computational pipeline to analyze the nonhost sequences obtained from MDS. Differential gene expression analysis was performed to evaluate for host transcriptome signatures for infectious types. Clinical samples were deidentified, and laboratory personnel handling the samples and interpreting the data were masked.

MAIN OUTCOME MEASURES: Pathogens and differential transcripts identified by MDS.

RESULTS: Metagenomic RNA deep sequencing detected pathogens in 86\% (12/14) of the patients tested. Swabs from 10 of 14 patients were positive for human adenovirus (HAdV) while swabs from 2 of 14 patients were positive for Vittaforma corneae (a parasitic fungal species of the microsporidia group). Samples positive for HAdV by RNA-seq were independently verified in a CLIA-certified laboratory. Pathogen-directed polymerase chain reaction confirmed the presence of V.\ corneae genome in the samples positive by RNA-seq. Local host transcriptome analysis identified 12 differentially expressed genes that provided distinct expression signatures for patients infected with HAdV compared with V.\ corneae.

CONCLUSIONS: Metagenomic RNA deep sequencing can reliably detect and quantify common and rare pathogens causing conjunctivitis, and identify strains. The unbiased nature of metagenomic RNA deep sequencing allowed an expanded scope of pathogen detection, including fungal species not commonly associated with acute conjunctivitis. In addition, the identification of infection type-specific local host transcriptome signatures may allow for pathogen detection even when the pathogen load is too low for direct identification.

}, keywords = {Adult, Aged, Conjunctivitis, DNA, Bacterial, DNA, Fungal, DNA, Viral, Female, High-Throughput Nucleotide Sequencing, Humans, Male, Metagenomics, Middle Aged, Young Adult}, issn = {1549-4713}, doi = {10.1016/j.ophtha.2019.03.039}, author = {Lalitha, Prajna and Seitzman, Gerami D and Kotecha, Ritesh and Hinterwirth, Armin and Chen, Cindi and Zhong, Lina and Cummings, Susie and Lebas, Elodie and Sahoo, Malaya K and Pinsky, Benjamin A and Lietman, Thomas M and Doan, Thuy} }