  With the development of natural language processing techniques(NLP),
automatic diagnosis of eye diseases using ophthalmology electronic medical
records (OEMR) has become possible. It aims to evaluate the condition of both
eyes of a patient respectively, and we formulate it as a particular multi-label
classification task in this paper. Although there are a few related studies in
other diseases, automatic diagnosis of eye diseases exhibits unique
characteristics. First, descriptions of both eyes are mixed up in OEMR
documents, with both free text and templated asymptomatic descriptions,
resulting in sparsity and clutter of information. Second, OEMR documents
contain multiple parts of descriptions and have long document lengths. Third,
it is critical to provide explainability to the disease diagnosis model. To
overcome those challenges, we present an effective automatic eye disease
diagnosis framework, NEEDED. In this framework, a preprocessing module is
integrated to improve the density and quality of information. Then, we design a
hierarchical transformer structure for learning the contextualized
representations of each sentence in the OEMR document. For the diagnosis part,
we propose an attention-based predictor that enables traceable diagnosis by
obtaining disease-specific information. Experiments on the real dataset and
comparison with several baseline models show the advantage and explainability
of our framework.
