This study introduces an AI-derived pain sign for continuous neonatal pain assessment, addressing the limitations of existing pain scales and computational approaches. Traditional pain scales, though widely used, are hindered by inter-rater variabili...
Surgical castration of males is carried out on a large scale in the US swine industry and the pain resulting from this procedure can be assessed using the Unesp-Botucatu pig composite acute pain scale (UPAPS). We aim to propose a short version of UPA...
Our research introduces a framework that integrates edge computing, quantum transfer learning, and federated learning to revolutionize pain level assessment through ECG signal analysis. The primary focus lies in developing a robust, privacy-preservin...
BACKGROUND: Spinal pain, one of the most common musculoskeletal disorders (MSDs), significantly impacts the quality of life due to chronic pain and disability. Physical activity has shown promise in managing spinal pain, although optimizing adherence...
This study introduces a multimodal sentiment analysis system to assess and recognize human pain sentiments within an Internet of Things (IoT)-enabled healthcare framework. This system integrates facial expressions and speech-audio recordings to evalu...
The use of machine learning to predict postoperative pain and opioid use has likely been catalyzed by the availability of complex patient-level data, computational and statistical advancements, the prevalence and impact of chronic postsurgical pain, ...
BACKGROUND: Acute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pain levels effectively. Current methods of pain assessment often rely on subjective patient reports o...
This study explores the question whether Artificial Intelligence (AI) can outperform human experts in animal pain recognition using sheep as a case study. It uses a dataset of N = 48 sheep undergoing surgery with video recordings taken before (no pai...
Currently, pain assessment using electroencephalogram signals and machine learning methods in clinical studies is of great importance, especially for those who cannot express their pain. Since newborns are among the high-risk group and always experie...
: Assessing pain deception is challenging due to its subjective nature. The main goal of this study was to evaluate the diagnostic value of pain deception using machine learning (ML) analysis with the Minnesota Multiphasic Personality Inventory-2 (MM...
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