An Ensemble of Psychological and Physical Health Indices Discriminates Between Individuals with Chronic Pain and Healthy Controls with High Reliability: A Machine Learning Study

Antonucci, Linda A.; Taurino, Alessandro; Laera, Domenico; Taurisano, Paolo; Losole, Jolanda; Lutricuso, S.; Abbatantuono, Chiara; Giglio, Mariateresa; Maria, De; Varrassi, Giustino; Puntillo, Filomena

doi:10.1007/s40122-020-00191-3

Cited by 8 publications

(8 citation statements)

References 39 publications

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“…A higher sensitivity and specificity was achieved when the machine was trained with multivariate fMRI patterns using SVM. The current review agrees with the previous studies on the SVM classification algorithm that gave stronger classification accuracy for biomedical problems 8,9 (see Table 1). In addition to SVM, we also observed that the Deep learning neural network also exhibits greater accuracy for predicting and classifying pain (see Table 1).…”

Section: Resultssupporting

confidence: 91%

“…Concerning patient-reported outcomes Mano et al 8 reported an accuracy of 63%, with 68% after cross-validation of all data. Antonucci et al 9 reported an accuracy of 86.5%, and Rodriguez et al 10 an accuracy of 87.1% (Table 1) Mano et al 8 likely reported lower scores because of the relative deficiency of data types compared to the studies by the other two authors. Simply put, they didn’t address enough dimensions to be specific.…”

Section: Resultsmentioning

confidence: 94%

“…Patient-reported outcomes are subjective. Of the compared authors, Antonucci et al 9 alone didn’t balance this with objective outcome measures. The implication is that more data provides more clarity on whether it is objective or not.…”

Section: Resultsmentioning

confidence: 99%

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Role of Artificial Intelligence and Machine Learning in the prediction of the pain: A scoping systematic review

Sankaran

Kumar

Parasuram

2022

Proc Inst Mech Eng H

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Artificial Intelligence in healthcare is growing quickly in diagnostics and treatment management. Despite the quantity and variety of studies its role in clinical care is not clear. To identify the evidence gaps and characteristics of the Artificial Intelligence and Machine Learning techniques in predicting/diagnosing the pain? Pubmed/Embase were searched from the inception to October 2021 for articles without any language restrictions specifically addressing the following: the accuracy of AI in pain considering Brain Imaging, Patient-reported measures, and Electrophysiology, the ability of AI to differentiate stratify severity/types of pain, the ability of AI to predict pain and lastly the most accurate AI technique for given inputs. All the included studies were on humans. Eight hundred forty abstracts were reviewed, and 23 articles were finally included. Identified records were independently screened and relevant data was extracted. We performed conceptual synthesis by grouping the studies using available concepts of AL/ML techniques in diagnosing pain. Then we summarized the number of features/physiological measurements. Structured tabulation synthesis was used to show patterns predictions along with a narrative commentary. A total of 23 articles, published between 2015 and 2020 from 12 countries were included. Most studies were experimental in design. The most common design was cross sectional. Chronic or acute pains were predicted more often. Compared to control, the pain prediction was in the range of 57%–96% by AI techniques. Support Vector Machine and deep learning showed higher accuracy for classifying pain. From this study, it can be inferred that AI/ML can be used to differentiate healthy controls from patients. It can also facilitate categorizing them into new and different clinical subgroups. Lastly, it can predict future pain. The limitations are with respect to studies done after the search period. AL/ ML has a supportive role in pain diagnostics.

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Section: Resultssupporting

confidence: 91%

Section: Resultsmentioning

confidence: 94%

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Role of Artificial Intelligence and Machine Learning in the prediction of the pain: A scoping systematic review

Sankaran

Kumar

Parasuram

2022

Proc Inst Mech Eng H

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“…For comparison, acute pain detection studies have shown accuracy scores up to 82.4% (hit rate) [26] using the UNBC-McMaster Shoulder Dataset and 95% for multimodal infant pain detection using a custom dataset by [31]. Chronic pain detection using psychological inventories have achieved 86.5% (cross-validated balanced accuracy) using a support vector machine [58].…”

Section: Experiments Resultsmentioning

confidence: 99%

Intelligent Sight and Sound: A Chronic Cancer Pain Dataset

Ordun¹,

Cha²,

Raff³

et al. 2022

Preprint

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Cancer patients experience high rates of chronic pain throughout the treatment process. Assessing pain for this patient population is a vital component of psychological and functional well-being, as it can cause a rapid deterioration of quality of life. Existing work in facial pain detection often have deficiencies in labeling or methodology that prevent them from being clinically relevant. This paper introduces the first chronic cancer pain dataset, collected as part of the Intelligent Sight and Sound (ISS) clinical trial, guided by clinicians to help ensure that model findings yield clinically relevant results. The data collected to date consists of 29 patients, 509 smartphone videos, 189,999 frames, and self-reported affective and activity pain scores adopted from the Brief Pain Inventory (BPI). Using static images and multi-modal data to predict self-reported pain levels, early models show significant gaps between current methods available to predict pain today, with room for improvement. Due to the especially sensitive nature of the inherent Personally Identifiable Information (PII) of facial images, the dataset will be released under the guidance and control of the National Institutes of Health (NIH).35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks.

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“…Frequently, pharmacological treatment, even if prescribed on a rational basis, [ 14 ] is not enough. Physical and psychological indices are very important to manage pain [ 15 ], and when a peripheral neuropathy is present, non-pharmacological management may be helpful [ 16 ].…”

Section: Discussionmentioning

confidence: 99%

Influence of Physical Activity on Pain, Depression and Quality of Life of Patients in Palliative Care: A Proof-of-Concept Study

Myrcik

Statowski

Trzepizur

et al. 2021

JCM

Self Cite

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Introduction: Palliative care not only focuses on physical ailments associated with the disease, but also considers the psychological, social and spiritual needs of the patients. The aim of this study is to assess the impact of physical activity on palliative care patients, with special regard to the subjective assessment of severity of total pain and quality of life. Materials and methods: The study was conducted on 92 palliative care patients either in a hospice or at home. The tool used to assess the patients was an original questionnaire focusing on the area of their independence and motor abilities. The study attempted to understand whether an appropriate physical activity and the instruction of palliative care patients and their families in the field of independence would improve the quality of life and reduce the intensity of total pain in the patients. Results: All of the patients were at an advanced stage of cancer. The survey at time “0”, conducted before the start of the instructions for patients and their relatives, showed that a majority of patients (47, 51.09%) often experienced limitations during the performance of daily activities. In the fourth visit, conducted one week after the fourth educational session, there was a significant increase in patients who did not experience any limitations in performing their daily activities or experienced them just sometimes. Conclusions: The ultimate effect of the proposed educational program on physical activity was an increase in the quality of life, a reduction in pain and a mood improvement. These results would need confirmation with more extensive studies.

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An Ensemble of Psychological and Physical Health Indices Discriminates Between Individuals with Chronic Pain and Healthy Controls with High Reliability: A Machine Learning Study

Cited by 8 publications

References 39 publications

Role of Artificial Intelligence and Machine Learning in the prediction of the pain: A scoping systematic review

Role of Artificial Intelligence and Machine Learning in the prediction of the pain: A scoping systematic review

Intelligent Sight and Sound: A Chronic Cancer Pain Dataset

Influence of Physical Activity on Pain, Depression and Quality of Life of Patients in Palliative Care: A Proof-of-Concept Study

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