Risk prediction in the context of medical, ethical, and legal is crucial for ensuring safety and informed decision-making. This study explores machine learning approaches for the MedNLP-CHAT task, utilizing English-translated datasets from Japanese and German subtasks. The textual data underwent preprocessing, including tokenization, n-gram extraction, and lemmatization, before being modeled using Logistic Regression, Nu-SVC (nu=0.1) 2, Gradient Boosting, and XGB Regressor. Objective risks were framed as a binary classification task, while subjective labels were predicted via regression, ensuring alignment with human-annotated distributions. Performance was evaluated using accuracy, precision, recall, F1-score, and Earth Mover’s Distance (EMD). The findings indicate the model’s strengths and weaknesses, emphasizing the need to enhance how class imbalances and potential overfitting are addressed. This work increases AI-driven risk assessment with applications in regulatory compliance, healthcare, and ethical AI development.
Das et al. (Fri,) studied this question.