背景:莱姆病是全球报告最多的蜱传疾病之一,使其成为持续的重大公共卫生问题。有效的莱姆病病例报告系统取决于卫生保健专业人员的及时诊断和报告,和临床诊断验证准确的实验室测试和解释。缺乏这些可能会导致诊断和治疗延迟,这可能会加剧莱姆病症状的严重程度。因此,有必要通过使用其他数据源来改善对莱姆病的监测,例如基于Web的数据。
目的:我们分析了全球Twitter数据,以了解其作为莱姆病监测工具的潜力和局限性。我们提出了一种基于变压器的分类系统,以使用自我报告的推文识别潜在的莱姆病病例。
方法:我们的初始样本包括从全球130多万莱姆病推文数据库中收集的20,000条推文。在预处理和地理定位推文之后,使用精心选择的关键字,将初始样本的子集中的推文手动标记为潜在的莱姆病病例或非莱姆病病例。表情符号被转换成情感词,然后在推文中被替换。这个标记的推文集合用于训练,验证,和DistilBERT的性能测试(BERT的蒸馏版本[来自变压器的双向编码器表示]),ALBERT(精简版BERT),和BERTweet(BERTforEnglishTweets)分类器。
结果:实证结果表明,BERTweet是所有评估模型中最好的分类器(平均F1得分为89.3%,分类准确率为90.0%,精密度为97.1%)。然而,为了召回,术语频率逆文档频率和k最近邻表现更好(93.2%和82.6%,分别)。关于使用表情符号来丰富推文嵌入,BERTweet的召回率增加了(增加了8%),DistilBERT的F1评分提高了93.8%(提高了4%),分类准确率提高了94.1%(提高了4%),ALBERT的F1评分提高了93.1%(提高5%),分类准确率提高了93.9%(提高5%).在美国,人们对莱姆病的普遍认识很高,联合王国,澳大利亚,加拿大,这些国家自我报告的莱姆病潜在病例约占收集到的英语推文的50%(9939/20,000),而与莱姆病相关的推文在非洲和亚洲国家很少见。数据中报告最多的莱姆病相关症状是皮疹,疲劳,发烧,和关节炎,虽然症状,如淋巴结病,心悸,淋巴结肿大,颈部僵硬度,和心律失常,并不常见,根据莱姆病的症状频率。
结论:该研究强调了BERTweet和DistilBERT作为来自自我报告数据的莱姆病潜在病例的分类器的稳健性。结果表明,表情符号是有效的富集,从而提高推文嵌入的准确性和分类器的性能。具体来说,表情符号反映悲伤,同理心,鼓励可以减少假阴性。
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.
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.
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.
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.
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.