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. 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.

通过融合和集成学习的方法，利用多模态数据来支持从普通科转诊中早期检测关节炎，是我们所知的第一次尝试。

Early detection of inflammatory arthritis to improve referrals using
  multimodal machine learning from blood testing, semi-structured and
  unstructured patient records

基于血液检测、半结构化和非结构化患者记录的多模式机器学习提高炎症性关节炎的早期检测和转诊