Sentetik Verilerle Klinik Psikoloji Veri Seti Oluşturma: Bilişsel Çarpıtmaların NLP ile Sınıflandırılarak Otomatik Tespiti

Yıl 2025, , 83 - 92, 27.03.2025

Hakkı Halil Babacan , Ramazan Oğuz , Yahya Kemal Beyitoğlu

https://doi.org/10.35234/fumbd.1469178

Öz

Bilişsel çarpıtmalar, bireylerin gerçekliği yanıltıcı bir şekilde algılamalarına neden olan düşünce hatalarıdır ve psikopatolojilerle güçlü bir ilişkisi vardır. Bu nedenle, çarpıtmaların doğru bir şekilde belirlenmesi ve sınıflandırılması, bilişsel davranışçı terapinin (CBT) etkinliğini artırabilir. Bu çalışma, bilişsel çarpıtmaların otomatik tespiti için derin öğrenme ve NLP tekniklerinin etkinliğini incelemektedir. GPT-4 ile üretilen İngilizce sentetik veriler (2000 örnek) ve Shreevastava ve Foltz’un veri seti (1590 bilişsel çarpıtma, 933 çarpıtma içermeyen örnek) kullanılarak RoBERTa modeli eğitilmiştir. Üç senaryo test edilmiştir: orijinal veri seti, sentetik veri seti ve bunların kombinasyonu. Sonuçlar, sentetik verilerin güçlü bir kaynak olduğunu göstermiştir. Doğruluk oranları sırasıyla %60,67 (orijinal), %94,51 (sentetik) ve %77,18 (kombine) olarak elde edilmiştir. GPT-4 tabanlı veri seti, özellikle bazı kategorilerde neredeyse mükemmel F1 skorları sağlamıştır. ROC eğrisi analizleri, GPT-4 veri setinin en yüksek AUC değerine (0,80) sahip olduğunu göstermiştir. Çalışma, sentetik veri kullanımının klinik psikolojide yapay zeka uygulamalarının potansiyelini genişlettiğini ve hasta gizliliğini korurken etkili modeller geliştirmenin bir yolunu sunduğunu ortaya koymuştur. Gelecekteki araştırmalar için, sentetik verilerin farklı modellerle test edilmesi ve gerçek klinik verilerle karşılaştırılması önerilmektedir.

Anahtar Kelimeler

Bilişsel çarpıtma, makine öğrenimi, doğal dil işleme, GPT-4, depresyon

Creating a Clinical Psychology Dataset with Synthetic Data: Automatic Detection of Cognitive Distortions Classified with NLP

Öz

Cognitive distortions are thought errors that lead individuals to perceive reality in a misleading way and are strongly associated with psychopathologies. Therefore, accurately identifying and classifying distortions can enhance the effectiveness of cognitive-behavioral therapy (CBT). This study investigates the effectiveness of deep learning and NLP techniques for the automatic detection of cognitive distortions. The RoBERTa model was trained using English synthetic data generated by GPT-4 (2000 examples) and the dataset from Shreevastava and Foltz (1590 cognitive distortion examples, 933 non-distortion examples). Three scenarios were tested: the original dataset, the synthetic dataset, and their combination. The results showed that synthetic data is a strong resource. Accuracy rates were 60.67% (original), 94.51% (synthetic), and 77.18% (combined). The GPT-4-based dataset provided almost perfect F1 scores, particularly in some categories. ROC curve analyses showed that the GPT-4 dataset had the highest AUC value (0.80). The study revealed that using synthetic data expands the potential of AI applications in clinical psychology and offers a way to develop effective models while preserving patient privacy. Future research should test synthetic data with different models and compare it with real clinical data.

Anahtar Kelimeler

Cognitive distortion, ; machine learning, natural language processing, GPT-4, depression

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