Early detection of inflammatory arthritis (IA) is critical to efficient and accurate hospital referral triage for timely treatment and preventing the deterioration of the IA disease course, especially under limited healthcare resources. The manual assessment process is the most common approach in practice for the early detection of IA, but it is extremely labor-intensive and inefficient. A large amount of clinical information needs to be assessed for every referral from General Practice (GP) to the hospitals. Machine learning shows great potential in automating repetitive assessment tasks and providing decision support for the early detection of IA. However, most machine learning-based methods for IA detection rely on blood testing results. But in practice, blood testing data is not always available at the point of referrals, so we need methods to leverage multimodal data such as semi-structured and unstructured data for early detection of IA. In this research, we present fusion and ensemble learning-based methods using multimodal data to assist decision-making in the early detection of IA, and a conformal prediction-based method to quantify the uncertainty of the prediction and detect any unreliable predictions. To the best of our knowledge, our study is the first attempt to utilize multimodal data to support the early detection of IA from GP referrals.

翻译：炎症性关节炎（IA）的早期检测对于高效、准确的医院转诊分诊至关重要，有助于及时治疗并防止IA病程恶化，在医疗资源有限的情况下尤其如此。人工评估是实践中IA早期检测最常用的方法，但其极其耗费人力且效率低下。从全科医疗（GP）向医院转诊的每一例病例都需要评估大量临床信息。机器学习在自动化重复性评估任务和为IA早期检测提供决策支持方面展现出巨大潜力。然而，大多数基于机器学习的IA检测方法依赖于血液检测结果。但在实践中，转诊时并非总能获得血液检测数据，因此我们需要能够利用半结构化和非结构化数据等多模态数据进行IA早期检测的方法。在本研究中，我们提出了基于融合和集成学习的方法，利用多模态数据辅助IA早期检测的决策，以及一种基于保形预测的方法来量化预测的不确定性并检测不可靠的预测。据我们所知，本研究是首次尝试利用多模态数据支持从全科医疗转诊中进行IA早期检测。