Multi-Label Classification of Bilingual Doctor Responses in Online Medical Consultations Using Deep Learning - Juanita, Safitri; Purwitasari, Diana; Purnama, I Ketut Eddy; Raihan, Muhammad; Purnomo, Mauridhi Hery (Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI, 2025)
Online health consultations (OHCs) have become an integral component of modern healthcare delivery. However, significant challenges remain in multilingual and low-resource contexts such as Indonesia, where language barriers and digital disparities hinder effective doctor–patient communication. Ensuring the quality of such interactions requires the identification of six key communicative functions: building relationships, gathering and providing information, decision-making, promoting disease- and treatment-related behaviour, and responding to emotions. While existing research has largely focused on English-language OHCs, studies analysing these communicative functions in Indonesian remain limited due to the lack of annotated datasets and linguistic complexity. To address this gap, we propose a deep learning framework for multi-label classification of communicative functions in bilingual (Indonesian/English) doctor response texts. The dataset used in this study was annotated by medical professionals with six predefined communicative function labels. We conducted a comprehensive comparative evaluation of three deep learning architectures namely Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional Neural Networks (CNN) equipped with cross-language word embedding to improve multilingual generalization. Model performance is evaluated through four complementary perspectives: example-based, label-based, ranking-based, and multifaceted metrics, ensuring a holistic assessment. Result show that the fine-tuned LSTM model achieved the highest precision (0.972) on Indonesian texts, while Bi-LSTM obtained the best results on English texts with 0.890 accuracy and 0.980 precision. The LSTM model also reduced false positives in Indonesian classifications, whereas Bi-LSTM improved diagnostic reliability in English, confirming the models’ cross-lingual adaptability. These findings highlight the potential of deep learning to improve communication effectiveness in bilingual and resource-constrained OHC settings.