Improper pain management can lead to severe physical or mental consequences, including suffering, and an increased risk of opioid dependency. Assessing the presence and severity of pain is imperative to prevent such outcomes and determine the appropriate intervention. However, the evaluation of pain intensity is challenging because different individuals experience pain differently. To overcome this, researchers have employed machine learning models to evaluate pain intensity objectively. However, these efforts have primarily focused on point estimation of pain, disregarding the inherent uncertainty and variability present in the data and model. Consequently, the point estimates provide only partial information for clinical decision-making. This study presents a neural network-based method for objective pain interval estimation, incorporating uncertainty quantification. This work explores three algorithms: the bootstrap method, lower and upper bound estimation (LossL) optimized by genetic algorithm, and modified lower and upper bound estimation (LossS) optimized by gradient descent algorithm. Our empirical results reveal that LossS outperforms the other two by providing a narrower prediction interval. As LossS outperforms, we assessed its performance in three different scenarios for pain assessment: (1) a generalized approach (single model for the entire population), (2) a personalized approach (separate model for each individual), and (3) a hybrid approach (separate model for each cluster of individuals). Our findings demonstrate the hybrid approach's superior performance, with notable practicality in clinical contexts. It has the potential to be a valuable tool for clinicians, enabling objective pain intensity assessment while taking uncertainty into account. This capability is crucial in facilitating effective pain management and reducing the risks associated with improper treatment.

翻译：不规范的疼痛管理可能导致严重的身心后果，包括痛苦以及阿片类药物依赖风险的增加。评估疼痛的存在和严重程度对于预防此类后果并确定适当的干预措施至关重要。然而，疼痛强度的评估具有挑战性，因为不同个体对疼痛的体验存在差异。为解决这一问题，研究人员已采用机器学习模型客观评估疼痛强度。然而，这些研究主要聚焦于疼痛的点估计，忽略了数据和模型中固有的不确定性和变异性。因此，点估计仅为临床决策提供部分信息。本研究提出一种基于神经网络的客观疼痛区间估计方法，融合不确定性量化。本文探索了三种算法：自助法、基于遗传算法优化的上下界估计（LossL）以及基于梯度下降算法优化的改进型上下界估计（LossS）。实证结果显示，LossS通过提供更窄的预测区间，性能优于其他两种方法。由于LossS表现更优，我们评估了其在三种疼痛评估场景中的性能：（1）通用方法（针对整体人群的单一模型），（2）个性化方法（针对个体的独立模型），以及（3）混合方法（针对个体聚类组的独立模型）。研究结果表明，混合方法具有优越性能，在临床环境中展现出显著实用性。该方法有望成为临床医生的有效工具，在考虑不确定性的同时实现客观疼痛强度评估。这一能力对于促进有效的疼痛管理、降低不当治疗相关风险至关重要。