Leveraging Machine Learning Approaches for Predicting Potential Lyme Disease Cases and Incidence Rates in United States Using Twitter

Boligarla, Srikanth; Laison, Elda K. E.; Li, Jiaxin Fiona; Mahadevan, Raja; Ng, Austen; Lin, Yangming; Thioub, Mamadou Yamar; Huang, Bruce; Ibrahim, Mohamed Hamza; Nasri, Bouchra

doi:10.21203/rs.3.rs-2136402/v1

Cited by 3 publications

(14 citation statements)

References 59 publications

(75 reference statements)

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“…After the removal of 143 duplicates, the screening process found 13 studies that met our inclusion and exclusion criteria (19,(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32). We identified two additional studies via reference screening (33,34).…”

Section: Search Results and Study Selectionmentioning

confidence: 99%

“…Among the notable LLMs are Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT). BERT is particularly effective at grasping the context within language, allowing for more accurate interpretations of text (19). On the other hand, GPT stands out for its ability to produce text that is remarkably similar to human-generated content, a feature that has broad applications in various fields including healthcare (20).…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, studies like Feller et al and Boligarla et al explored innovative applications of NLP in risk assessment and disease surveillance using diverse data sources, including clinical notes and social media (25,26).…”

Section: Resultsmentioning

confidence: 99%

“…Six studies demonstrated a low risk of bias across all domains (21,(31)(32)(33)(34)(35). Overall, ten studies were evaluated as having low risk (21)(22)(23)26,28,(31)(32)(33)(34)(35), four showed high risk (19,24,27,29), and one lacked sufficient data for a conclusive risk assessment (30).…”

Section: Risk Of Biasmentioning

confidence: 99%

“…However, specific concerns were raised in a few studies, particularly regarding their study populations. Several studies used specialized populations, such as patients with hematologic neoplasms or data derived from specific databases, which may limit generalizability (25)(26)(27)(28)(29)35).…”

Section: Risk Of Biasmentioning

confidence: 99%

See 4 more Smart Citations

Utilizing Natural Language Processing and Large Language Models in the Diagnosis and Prediction of Infectious Diseases: A Systematic Review

Omar,

Brin,

Glicksberg

et al. 2024

Preprint

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Background: Natural Language Processing (NLP) and Large Language Models (LLMs) hold largely untapped potential in infectious disease management. This review explores their current use and uncovers areas needing more attention. Methods: This analysis followed systematic review procedures, registered with PROSPERO. We conducted a search across major databases including PubMed, Embase, Web of Science, and Scopus, up to December 2023, using keywords related to NLP, LLM, and infectious diseases. We also employed the QUADAS-2 tool for evaluating the quality and robustness of the included studies. Results: Our review identified 15 studies with diverse applications of NLP in infectious disease management. Notable examples include GPT-4's application in detecting urinary tract infections and BERTweet's use in Lyme Disease surveillance through social media analysis. These models demonstrated effective disease monitoring and public health tracking capabilities. However, the effectiveness varied across studies. For instance, while some NLP tools showed high accuracy in pneumonia detection and high sensitivity in identifying invasive mold diseases from medical reports, others fell short in areas like bloodstream infection management. Conclusion: This review highlights the yet-to-be-fully-realized promise of NLP and LLMs in infectious disease management. It calls for more exploration to fully harness AI's capabilities, particularly in the areas of diagnosis, surveillance, predicting disease courses, and tracking epidemiological trends.

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Section: Search Results and Study Selectionmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Risk Of Biasmentioning

confidence: 99%

Section: Risk Of Biasmentioning

confidence: 99%

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Utilizing Natural Language Processing and Large Language Models in the Diagnosis and Prediction of Infectious Diseases: A Systematic Review

Omar,

Brin,

Glicksberg

et al. 2024

Preprint

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Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis

Laison,

Hamza Ibrahim,

Boligarla

et al. 2023

J Med Internet Res

Self Cite

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Background Lyme disease is among the most reported tick-borne diseases worldwide, making it a major ongoing public health concern. An effective Lyme disease case reporting system depends on timely diagnosis and reporting by health care professionals, and accurate laboratory testing and interpretation for clinical diagnosis validation. A lack of these can lead to delayed diagnosis and treatment, which can exacerbate the severity of Lyme disease symptoms. Therefore, there is a need to improve the monitoring of Lyme disease by using other data sources, such as web-based data. Objective We analyzed global Twitter data to understand its potential and limitations as a tool for Lyme disease surveillance. We propose a transformer-based classification system to identify potential Lyme disease cases using self-reported tweets. Methods Our initial sample included 20,000 tweets collected worldwide from a database of over 1.3 million Lyme disease tweets. After preprocessing and geolocating tweets, tweets in a subset of the initial sample were manually labeled as potential Lyme disease cases or non-Lyme disease cases using carefully selected keywords. Emojis were converted to sentiment words, which were then replaced in the tweets. This labeled tweet set was used for the training, validation, and performance testing of DistilBERT (distilled version of BERT [Bidirectional Encoder Representations from Transformers]), ALBERT (A Lite BERT), and BERTweet (BERT for English Tweets) classifiers. Results The empirical results showed that BERTweet was the best classifier among all evaluated models (average F1-score of 89.3%, classification accuracy of 90.0%, and precision of 97.1%). However, for recall, term frequency-inverse document frequency and k-nearest neighbors performed better (93.2% and 82.6%, respectively). On using emojis to enrich the tweet embeddings, BERTweet had an increased recall (8% increase), DistilBERT had an increased F1-score of 93.8% (4% increase) and classification accuracy of 94.1% (4% increase), and ALBERT had an increased F1-score of 93.1% (5% increase) and classification accuracy of 93.9% (5% increase). The general awareness of Lyme disease was high in the United States, the United Kingdom, Australia, and Canada, with self-reported potential cases of Lyme disease from these countries accounting for around 50% (9939/20,000) of the collected English-language tweets, whereas Lyme disease–related tweets were rare in countries from Africa and Asia. The most reported Lyme disease–related symptoms in the data were rash, fatigue, fever, and arthritis, while symptoms, such as lymphadenopathy, palpitations, swollen lymph nodes, neck stiffness, and arrythmia, were uncommon, in accordance with Lyme disease symptom frequency. Conclusions The study highlights the robustness of BERTweet and DistilBERT as classifiers for potential cases of Lyme disease from self-reported data. The results demonstrated that emojis are effective for enrichment, thereby improving the accuracy of tweet embeddings and the performance of classifiers. Specifically, emojis reflecting sadness, empathy, and encouragement can reduce false negatives.

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Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis (Preprint)

Laison¹,

Ibrahim²,

Boligarla³

et al. 2023

Preprint

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BACKGROUND Lyme disease is the most prevalent tick-borne disease in the Northern Hemisphere. Delayed treatment can exacerbate symptoms and result in more severe cases, making this condition a major public health concern in the coming years. Additionally, the Lyme disease surveillance system relies on healthcare professionals to report cases, which weakens the system's efficiency in having accurate data since only the cases seeking medical attention are reported. Thus, there is a need to enhance the surveillance tools of Lyme disease using other data sources such as web-data. OBJECTIVE Worldwide Twitter data was analyzed to understand its potential and its limitations as a tool for Lyme disease surveillance. The proposed Twitter data system is primarily a transformer-based classifier that leverages self-reported tweets to identify potential cases of Lyme disease. METHODS We first used approximately 20,000 English tweets collected worldwide from a database with more than 1.3 million tweets related to Lyme disease. Because most Lyme disease tweets are from the US, we selected only 20,000 tweets, from which about 10% represented other countries than the US, to capture more variability across countries. After preprocessing and geolocating the tweets, a set of carefully selected keywords was used to manually label a subset of tweets to classify them as potential or non-Lyme disease cases. Emojis were converted to sentiment words and then used in place of emojis in the tweets. The dataset of labelled tweets was then used to train, validate, and test the performance of three transform-based classifier variants, namely ALBERT, DistilBERT, and BERTweet, to classify the remaining and other new tweets. RESULTS The empirical results showed that BERTweet is the best classifier among all classification models evaluated, with the highest average F1-score of 89.3%, classification accuracy of 90.0%, precision of 97.1%, except for the recall where TF-IDF and k-Nearest Neighbors perform better by 93.2 % against 82.6% for BERTweet. When emojis' expressions were used to enrich the tweet embeddings, the recall score for BERTweet increased by 8%, and DistilBERT had a markedly increased F1-score of 93.8% (+4%) and a classification accuracy of 94.1% (+4%), while ALBERT had a F1-score of 93.1% (5%) and a classification accuracy of 93.9% (+5%). CONCLUSIONS This study revealed several key findings. First, that BERTweet and DistilBERT can serve as robust NLP classifiers to identify self-reported potential cases of Lyme disease. Second, emojis are effective as enrichment features to improve the accuracy of the tweet embedding and the performance of transformer-based classifiers. In particular, the emojis reflecting sadness, empathy, and encouragement can help reduce false negatives. Third, the general awareness of Lyme disease is high in the United States, the United Kingdom, Australia, and Canada as self-reported potential cases of Lyme disease on Twitter from these countries accounted for more than 50% of the collected English tweets, while Lyme disease-related tweets are scarce in countries from Africa and Asia. Finally, the most commonly reported symptoms of Lyme disease are rash, fatigue, fever, and arthritis while symptoms such as borrelial lymphocytoma, palpitations, swollen lymph nodes, neck stiffness, and irregular heartbeat are unusual and rare.

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Leveraging Machine Learning Approaches for Predicting Potential Lyme Disease Cases and Incidence Rates in United States Using Twitter

Cited by 3 publications

References 59 publications

Utilizing Natural Language Processing and Large Language Models in the Diagnosis and Prediction of Infectious Diseases: A Systematic Review

Utilizing Natural Language Processing and Large Language Models in the Diagnosis and Prediction of Infectious Diseases: A Systematic Review

Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis

Identifying Potential Lyme Disease Cases Using Self-Reported Worldwide Tweets: Deep Learning Modeling Approach Enhanced With Sentimental Words Through Emojis (Preprint)

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