NEEDED: Introducing Hierarchical Transformer to Eye Diseases Diagnosis

TL;DR

This paper introduces NEEDED, a hierarchical transformer-based framework for automatic eye disease diagnosis from OEMR documents, addressing challenges like data sparsity, long texts, and the need for explainability, with demonstrated superior performance.

Contribution

The paper proposes a novel hierarchical transformer model with an attention-based predictor for explainable, accurate multi-label eye disease diagnosis from complex OEMR data.

Findings

Outperforms baseline models on real OEMR datasets

Provides traceable and explainable diagnosis results

Effectively handles sparse, long, and mixed-format medical texts

Abstract

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.