This research delves into Musculoskeletal Disorder (MSD) risk factors, using a blend of Natural Language Processing (NLP) and mode-based ranking. The aim is to refine understanding, classification, and prioritization for focused prevention and treatment. Eight NLP models are evaluated, combining pre-trained transformers, cosine similarity, and distance metrics to categorize factors into personal, biomechanical, workplace, psychological, and organizational classes. BERT with cosine similarity achieves 28% accuracy; sentence transformer with Euclidean, Bray-Curtis, and Minkowski distances scores 100%. With 10-fold cross-validation, statistical tests ensure robust results. Survey data and mode-based ranking determine severity hierarchy, aligning with the literature. "Working posture" is the most severe, highlighting posture's role. Survey insights emphasize "Job insecurity," "Effort reward imbalance," and "Poor employee facility" as significant contributors. Rankings offer actionable insights for MSD prevention. The study suggests targeted interventions, workplace improvements, and future research directions. This integrated NLP and ranking approach enhances MSD comprehension and informs occupational health strategies.

翻译：本研究深入探讨了肌肉骨骼疾病（MSD）风险因素，融合了自然语言处理（NLP）与基于众数的排序方法。旨在优化对风险因素的理解、分类与优先级排序，以实现针对性预防和治疗。研究评估了八种NLP模型，结合预训练Transformer、余弦相似度及距离度量，将因素划分为个人、生物力学、工作场所、心理及组织五类。其中，BERT结合余弦相似度达到28%的准确率；而句子Transformer结合欧氏距离、Bray-Curtis距离与闵可夫斯基距离实现了100%的准确率。通过10折交叉验证与统计检验确保结果稳健性。调查数据与基于众数的排序确定了严重性等级，与文献结论一致。"工作姿势"被评为最严重的风险因素，凸显了姿势的关键作用。调查反馈强调"工作不安全感"、"付出-回报失衡"及"不良员工设施"为重要影响因素。该排序为MSD预防提供了可操作的见解。本研究建议采取针对性干预措施、改善工作环境，并指出了未来研究方向。这种融合NLP与排序的方法增强了人们对MSD的理解，并为职业健康策略提供了依据。