Specialty detection in the context of telemedicine in a highly
  imbalanced multi-class distribution
Alaa Alomari Hossam Faris Pedro A. Castillo
The Covid-19 pandemic has led to an increase in the awareness of and demand
for telemedicine services, resulting in a need for automating the process and
relying on machine learning (ML) to reduce the operational load. This research
proposes a specialty detection classifier based on a machine learning model to
automate the process of detecting the correct specialty for each question and
routing it to the correct doctor. The study focuses on handling multiclass and
highly imbalanced datasets for Arabic medical questions, comparing some
oversampling techniques, developing a Deep Neural Network (DNN) model for
specialty detection, and exploring the hidden business areas that rely on
specialty detection such as customizing and personalizing the consultation flow
for different specialties. The proposed module is deployed in both synchronous
and asynchronous medical consultations to provide more real-time
classification, minimize the doctor effort in addressing the correct specialty,
and give the system more flexibility in customizing the medical consultation
flow. The evaluation and assessment are based on accuracy, precision, recall,
and F1-score. The experimental results suggest that combining multiple
techniques, such as SMOTE and reweighing with keyword identification, is
necessary to achieve improved performance in detecting rare classes in
imbalanced multiclass datasets. By using these techniques, specialty detection
models can more accurately detect rare classes in real-world scenarios where
imbalanced data is common.