[对于关节疾病的预测模型的进步]
概括
预测部疾病 (TMD) 对患者的福祉至关重要. 本综述研究了统计学,机器学习和深度学习模型如何促进TMD预测,突出了它们的优缺点,以获得更好的口腔和面外科手术结果.
科学领域:
- 口腔和牙面部外科手术
- 计算生物学 计算生物学
- 数据科学数据科学数据科学
背景情况:
- 部疾病 (TMD) 很普遍,严重影响患者的生活质量.
- 准确预测和及时治疗TMD在口腔和面外科手术中至关重要.
- TMD预测模型的演变反映了计算方法的进步.
研究的目的:
- 综合审查和评估用于TMD预测的计算方法.
- 在TMD预测中批判性地分析统计,机器学习和深度学习模型的优势和局限性.
- 讨论未来的研究方向,以提高TMD预测的准确性和有效性.
主要方法:
- 审查传统的统计方法来识别TMD的风险因素.
- 对大型TMD数据集中的模式识别机器学习技术的分析.
- 对TMD时间序列数据中的时间模式提取和非线性关系建模的深度学习方法的评估.
主要成果:
- 传统方法提供了可解释性,但需要先前的假设.
- 机器学习优于高维数据,但取决于数据质量和概括性.
- 深度学习捕捉了复杂的时间动态,但需要大量的数据集,并面临可解释性挑战.
结论:
- 每种计算方法都为TMD预测带来了不同的优势和局限性.
- 了解这些权衡是选择适合临床应用的模型的关键.
- 需要进一步的研究来提高TMD预测模型的准确性,概括性和解释性.
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