Specialty detection in the context of telemedicine in a highly imbalanced multiclass distribution
Metadatos
Mostrar el registro completo del ítemEditorial
Public Library of Science (PLOS)
Fecha
2023-11-16Referencia bibliográfica
Alomari A, Faris H, Castillo PA (2023) Specialty detection in the context of telemedicine in a highly imbalanced multi-class distribution. PLoS ONE 18(11): e0290581. https://doi.org/10.1371/journal.pone.0290581
Patrocinador
Ministerio Español de Ciencia e Innovación under project number: PID2020-115570GB-C22 MCIN/AEI/10.13039/501100011033; Cátedra de Empresa Tecnología para las Personas (UGRFujitsu)Resumen
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.