KULLANICI YORUMLARIYLA İLAÇ YAN ETKİSİ TESPİTİ: TRANSFORMATÖR TABANLI DUYGU ANALİZİ ÇIKARIMI

Abstract

Sağlık alanında, özellikle ilaç incelemeleriyle ilgili olarak kullanıcı tarafından oluşturulan içeriğin artan hacmi, olumsuz ilaç reaksiyonlarını (ADR'ler) ve kullanıcı duygularını analiz etmek için önemli fırsatlar sunmaktadır. Bu çalışmada, duygu analizi yapmak için ilaç yan etkileriyle ilgili kullanıcı yorumlarını içeren birleştirilmiş CADEC+ADE veri kümesini kullanıyoruz. Bu çalışma, bir hasta perspektifinden ilaçların etkisini daha iyi anlamak amacıyla kullanıcı geri bildirimlerinin olumlu ve olumsuz duygularının ‘yan etkisi var’ ya da ‘yan etkisi yok’ şeklinde ikili bir sınıflandırmasını içerir. Bunun için beş farklı yöntemin performansını değerlendiriyoruz ve karşılaştırıyoruz: BiLSTM BioBERT, MBERT, SVM ve XLM-R. SVM dışındaki diğer modeller biyomedikal metinler üzerinde önceden eğitilmiş transformatör tabanlı model oldukları ve biyomedikal terminolojiye ilişkin üstün anlayışları için kullanılırken, SVM karşılaştırma için klasik makine öğrenimi temellerini sağlar. Modeller, yan etkilerine ve algılanan etkinliğe odaklanarak, ilaçlarla ilgili kullanıcı tarafından bildirilen deneyimleri içeren birleştirilmiş CADEC+ADE veri kümesinde eğitildi ve değerlendirildi. Sonuçlar BioBERT modelinin %99,2 doğruluk değeriyle bu alan için duygu analizinde diğer tekil yöntemlerden daha etkili olduğunu ortaya koyuyor. Buna ek olarak BERT modelleri ile oluşturulan topluluk öğrenmesi modelinde ise %99,6 oranında doğruluk değeri elde edilmiş olup, bu yöntem ile en yüksek doğruluk oranına ulaşılmıştır.

Keywords

• ADE Corpus
• Bert
• CADEC
• Olumsuz İlaç Reaksiyonu
• Transformer Model

ADVERSE DRUG REACTION DETECTION FROM USER COMMENTS: A TRANSFORMER-BASED SENTIMENT ANALYSIS APPROACH

Abstract

In the healthcare domain, the increasing volume of user-generated content, especially related to drug reviews, offers significant opportunities to analyze adverse drug reactions (ADRs) and user sentiment. In this study, we utilize the combined CADEC+ ADE dataset, which contains user comments related to adverse drug reactions to perform sentiment analysis. This work aims to better understand the impact of medications from a patient perspective by conducting a binary classification of user feedback into “has adverse drug reaction" or ‘not’ based on positive and negative sentiments. To achieve this, we evaluate and compare the performance of five different methods: BiLSTM, BioBERT, MBERT, SVM, and XLM-R. While SVM provides a classical machine learning baseline for comparison, the other models are transformer-based architectures pre-trained on biomedical texts, chosen for their superior understanding of biomedical terminology. The models were trained and evaluated on the combined CADEC_ADE++ dataset, which includes user-reported experiences related to drugs with a focus on adverse drug reaction and perceived efficacy. The results reveal that the ensemble learning model created with BERT models, an accuracy of 99.6% was obtained and the highest accuracy rate was achieved with this method.

Keywords

• ADE Corpus
• Adverse Drug Reaction
• Bert
• CADEC
• Transformer Model

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