  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.
