Ameli Nazila, Firoozi Tahereh, Gibson Monica, Lai Hollis
Mike Petryk School of Dentistry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada.
Department of Periodontology, School of Dentistry, University of Indiana, Indianapolis, United States of America.
PLOS Digit Health. 2024 Dec 13;3(12):e0000692. doi: 10.1371/journal.pdig.0000692. eCollection 2024 Dec.
Periodontitis is a complex and microbiome-related inflammatory condition impacting dental supporting tissues. Emphasizing the potential of Clinical Decision Support Systems (CDSS), this study aims to facilitate early diagnosis of periodontitis by extracting patients' information collected as dental charts and notes. We developed a CDSS to predict the stage and grade of periodontitis using natural language processing (NLP) techniques including bidirectional encoder representation for transformers (BERT). We compared the performance of BERT with that of a baseline feature-engineered model. A secondary data analysis was conducted using 309 anonymized patient periodontal charts and corresponding clinician's notes obtained from the university periodontal clinic. After data preprocessing, we added a classification layer on top of the pre-trained BERT model to classify the clinical notes into their corresponding stage and grades. Then, we fine-tuned the pre-trained BERT model on 70% of our data. The performance of the model was evaluated on 32 unseen new patients' clinical notes. The results were compared with the output of a baseline feature-engineered algorithm coupled with MLP techniques to classify the stage and grade of periodontitis. Our proposed BERT model predicted the patients' stage and grade with 77% and 75% accuracy, respectively. MLP model showed that the accuracy of correct classification of stage and grade of the periodontitis on a set of 32 new unseen data was 59.4% and 62.5%, respectively. The BERT model could predict the periodontitis stage and grade on the same new dataset with higher accuracy (66% and 72%, respectively). The utilization of BERT in this context represents a groundbreaking application in dentistry, particularly in CDSS. Our BERT model outperformed baseline models, even with reduced information, promising efficient review of patient notes. This integration of advanced NLP techniques with CDSS frameworks holds potential for timely interventions, preventing complications and reducing healthcare costs.
牙周炎是一种与微生物群相关的复杂炎症性疾病，会影响牙齿支持组织。本研究强调临床决策支持系统（CDSS）的潜力，旨在通过提取作为牙科图表和记录收集的患者信息，促进牙周炎的早期诊断。我们开发了一种CDSS，使用包括双向编码器表征变换器（BERT）在内的自然语言处理（NLP）技术来预测牙周炎的阶段和等级。我们将BERT的性能与基线特征工程模型的性能进行了比较。使用从大学牙周诊所获得的309份匿名患者牙周图表和相应的临床医生记录进行了二次数据分析。在数据预处理后，我们在预训练的BERT模型之上添加了一个分类层，将临床记录分类到相应的阶段和等级。然后，我们在70%的数据上对预训练的BERT模型进行了微调。在32份未见过的新患者临床记录上评估了模型的性能。将结果与结合多层感知器（MLP）技术的基线特征工程算法的输出进行比较，以对牙周炎的阶段和等级进行分类。我们提出的BERT模型预测患者阶段和等级的准确率分别为77%和75%。MLP模型显示，在一组32份新的未见过的数据上，牙周炎阶段和等级正确分类的准确率分别为59.4%和62.5%。BERT模型可以在相同的新数据集上以更高的准确率预测牙周炎阶段和等级（分别为66%和72%）。在这种情况下使用BERT代表了牙科领域的一项开创性应用，特别是在CDSS中。我们的BERT模型即使在信息减少的情况下也优于基线模型，有望对患者记录进行高效审查。这种将先进的NLP技术与CDSS框架相结合具有实现及时干预、预防并发症和降低医疗成本的潜力。
