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：本研究深入探讨肌肉骨骼疾病（MSD）风险因素，结合自然语言处理（NLP）与基于众数的排序方法。其目标在于完善对风险因素的理解、分类与优先级排序，以实现有针对性的预防与治疗。研究评估了八种NLP模型，结合预训练的Transformer模型、余弦相似度及多种距离度量，将风险因素归类为个人、生物力学、工作场所、心理及组织五大类别。其中，BERT结合余弦相似度达到28%的准确率；而句子Transformer结合欧几里得距离、布雷-柯蒂斯距离和闵可夫斯基距离的模型取得了100%的准确率。通过十折交叉验证及统计检验确保了结果的稳健性。结合调查数据与基于众数的排序方法，研究确定了风险因素的严重性层级，该结果与现有文献一致。"工作姿势"被确定为最严重的风险因素，突显了姿势在MSD中的关键作用。调查数据进一步强调了"工作不安全感"、"付出-回报失衡"及"员工设施条件差"是重要的风险贡献因素。本研究提供的排序结果为MSD预防提供了可操作的见解。研究建议采取针对性干预措施、改善工作环境，并指出了未来的研究方向。这种融合NLP与排序方法的综合途径增强了对MSD的理解，并为职业健康策略的制定提供了信息。