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.Comment: 26 pages, 5 figures, 9 table