Automatic Differential Diagnosis using Transformer-Based Multi-Label
  Sequence Classification
Authors: name: Abu Adnan Sadi, name: Mohammad Ashrafuzzaman Khan, name: Lubaba Binte Saber

As the field of artificial intelligence progresses, assistive technologies
are becoming more widely used across all industries. The healthcare industry is
no different, with numerous studies being done to develop assistive tools for
healthcare professionals. Automatic diagnostic systems are one such beneficial
tool that can assist with a variety of tasks, including collecting patient
information, analyzing test results, and diagnosing patients. However, the idea
of developing systems that can provide a differential diagnosis has been
largely overlooked in most of these research studies. In this study, we propose
a transformer-based approach for providing differential diagnoses based on a
patient's age, sex, medical history, and symptoms. We use the DDXPlus dataset,
which provides differential diagnosis information for patients based on 49
disease types. Firstly, we propose a method to process the tabular patient data
from the dataset and engineer them into patient reports to make them suitable
for our research. In addition, we introduce two data modification modules to
diversify the training data and consequently improve the robustness of the
models. We approach the task as a multi-label classification problem and
conduct extensive experiments using four transformer models. All the models
displayed promising results by achieving over 97% F1 score on the held-out test
set. Moreover, we design additional behavioral tests to get a broader
understanding of the models. In particular, for one of our test cases, we
prepared a custom test set of 100 samples with the assistance of a doctor. The
results on the custom set showed that our proposed data modification modules
improved the model's generalization capabilities. We hope our findings will
provide future researchers with valuable insights and inspire them to develop
reliable systems for automatic differential diagnosis.