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.

本研究提出了基于神经网络的方法来进行客观疼痛区间估计，并将不确定性量化纳入考虑。通过三种算法：bootstrap 方法、遗传算法优化的上下界估计（LossL）和梯度下降算法优化的改进上下界估计（LossS），实证结果表明 LossS 方法在提供更窄的预测区间方面优于其他两种方法。我们评估了 LossS 方法在三种不同的疼痛评估场景中的性能，包括广义方法（整个人群使用单一模型）、个人化方法（每个个体使用单独模型）和混合方法（每个个体群集使用单独模型），结果显示混合方法具有卓越的性能，在临床环境中具有显著的实用性。该方法有潜力成为临床医生的有价值工具，能够在进行客观疼痛强度评估时考虑不确定性，从而促进有效的疼痛管理，降低治疗不当所带来的风险。