A Transformer-based representation-learning model with unified
  processing of multimodal input for clinical diagnostics
Hong-Yu Zhou Yizhou Yu Chengdi Wang Shu Zhang Yuanxu Gao Jia Pan Jun Shao Guangming Lu Kang Zhang Weimin Li
During the diagnostic process, clinicians leverage multimodal information,
such as chief complaints, medical images, and laboratory-test results.
Deep-learning models for aiding diagnosis have yet to meet this requirement.
Here we report a Transformer-based representation-learning model as a clinical
diagnostic aid that processes multimodal input in a unified manner. Rather than
learning modality-specific features, the model uses embedding layers to convert
images and unstructured and structured text into visual tokens and text tokens,
and bidirectional blocks with intramodal and intermodal attention to learn a
holistic representation of radiographs, the unstructured chief complaint and
clinical history, structured clinical information such as laboratory-test
results and patient demographic information. The unified model outperformed an
image-only model and non-unified multimodal diagnosis models in the
identification of pulmonary diseases (by 12% and 9%, respectively) and in the
prediction of adverse clinical outcomes in patients with COVID-19 (by 29% and
7%, respectively). Leveraging unified multimodal Transformer-based models may
help streamline triage of patients and facilitate the clinical decision
process.