Personalized Pain Study Platform using Evidence-Based Continuous Learning Tool

Saha, Amit; Ahsan, Golam Mushih Tanimul; Ahamed, Sheikh Iqbal

doi:10.1109/compsac.2019.10254

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…The matrix of success and uncertainty for various generation numbers is tabulated. Shah et al, in (IEEE 2019) [12], created customizable personalized pain study platform and launched that offer a significant collection of data, monitoring of study participants, character intrusion detection, asymmetric encryption of research results, etc. It is also used to analyze the accuracy of pressure current sensing using an evidence-based learning process from facial features data gathered from Bangladesh, Nepal, and the USA, which resulted in approximately 71% classification accuracy.…”

Section: Review Of Literaturementioning

confidence: 99%

Generation of Regular Expressions for Large Clinical Dataset using NLP and Machine Learning Techniques

Puri¹,

Patnaik²

2021

INDJCSE

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Machine learning supervised classification plays major role in large text classification. In health care data it contributes since couple of years for generating the privacy and security. Such kind of electric records might take large data on storage devices, so it needs to optimize with some processing techniques. The Natural Language Processing (NLP) features to represent text as a vector appropriate for use in machine learning algorithms. To describe text as just a vector suitable for use in machine learning algorithms, text classification techniques frequently rely on dictionary counters including bag-of-words as well NLP techniques. As an alternative feature set, editable search queries are proposed in this work. The suggested model utilizes the Smith Waterman (SW) optimization created by Pair -wise alignment to create a set of pattern matching characteristics based on labeled textual information or to train a later part classifier. The Naive Bayes (NB) supervised learning algorithm has used for classification. While a correlation of the special groups and traditional text characteristics shows that a classification algorithm does not make better decisions utilizing generated features, the produced factors can catch patterns that cannot be identified with collective expertise. As a result, it is significantly boost a classifier that combines conventional methods for generated characteristics. In the experimental analysis we evaluate the proposed system results with various existing systems that our system demonstrates effective results than traditional approaches.

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Section: Review Of Literaturementioning

confidence: 99%

Generation of Regular Expressions for Large Clinical Dataset using NLP and Machine Learning Techniques

Puri¹,

Patnaik²

2021

INDJCSE

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Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis

Huo,

Yu,

Lin

et al. 2024

J Med Internet Res

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Background The continuous monitoring and recording of patients’ pain status is a major problem in current research on postoperative pain management. In the large number of original or review articles focusing on different approaches for pain assessment, many researchers have investigated how computer vision (CV) can help by capturing facial expressions. However, there is a lack of proper comparison of results between studies to identify current research gaps. Objective The purpose of this systematic review and meta-analysis was to investigate the diagnostic performance of artificial intelligence models for multilevel pain assessment from facial images. Methods The PubMed, Embase, IEEE, Web of Science, and Cochrane Library databases were searched for related publications before September 30, 2023. Studies that used facial images alone to estimate multiple pain values were included in the systematic review. A study quality assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies, 2nd edition tool. The performance of these studies was assessed by metrics including sensitivity, specificity, log diagnostic odds ratio (LDOR), and area under the curve (AUC). The intermodal variability was assessed and presented by forest plots. Results A total of 45 reports were included in the systematic review. The reported test accuracies ranged from 0.27-0.99, and the other metrics, including the mean standard error (MSE), mean absolute error (MAE), intraclass correlation coefficient (ICC), and Pearson correlation coefficient (PCC), ranged from 0.31-4.61, 0.24-2.8, 0.19-0.83, and 0.48-0.92, respectively. In total, 6 studies were included in the meta-analysis. Their combined sensitivity was 98% (95% CI 96%-99%), specificity was 98% (95% CI 97%-99%), LDOR was 7.99 (95% CI 6.73-9.31), and AUC was 0.99 (95% CI 0.99-1). The subgroup analysis showed that the diagnostic performance was acceptable, although imbalanced data were still emphasized as a major problem. All studies had at least one domain with a high risk of bias, and for 20% (9/45) of studies, there were no applicability concerns. Conclusions This review summarizes recent evidence in automatic multilevel pain estimation from facial expressions and compared the test accuracy of results in a meta-analysis. Promising performance for pain estimation from facial images was established by current CV algorithms. Weaknesses in current studies were also identified, suggesting that larger databases and metrics evaluating multiclass classification performance could improve future studies. Trial Registration PROSPERO CRD42023418181; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=418181

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Application of AI in in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis (Preprint)

Huo,

Yu,

Lin

et al. 2023

Preprint

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BACKGROUND The continuous monitoring and recording of patients’ pain status is a major problem in current research on postoperative pain management. In the large number of original or review articles focusing on different approaches for pain assessment, many researchers have investigated how computer vision (CV) can help by capturing facial expressions. However, there is a lack of proper comparison of results between studies to identify current research gaps. OBJECTIVE The purpose of this systematic review and meta-analysis was to investigate the diagnostic performance of artificial intelligence models for multilevel pain assessment from facial images. METHODS The PubMed, Embase, IEEE, Web of Science, and Cochrane Library databases were searched for related publications before September 30, 2023. Studies that used facial images alone to estimate multiple pain values were included in the systematic review. A study quality assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies, 2nd edition tool. The performance of these studies was assessed by metrics including sensitivity, specificity, log diagnostic odds ratio (LDOR), and area under the curve (AUC). The intermodal variability was assessed and presented by forest plots. RESULTS A total of 45 reports were included in the systematic review. The reported test accuracies ranged from 0.27-0.99, and the other metrics, including the mean standard error (MSE), mean absolute error (MAE), intraclass correlation coefficient (ICC), and Pearson correlation coefficient (PCC), ranged from 0.31-4.61, 0.24-2.8, 0.19-0.83, and 0.48-0.92, respectively. In total, 6 studies were included in the meta-analysis. Their combined sensitivity was 98% (95% CI 96%-99%), specificity was 98% (95% CI 97%-99%), LDOR was 7.99 (95% CI 6.73-9.31), and AUC was 0.99 (95% CI 0.99-1). The subgroup analysis showed that the diagnostic performance was acceptable, although imbalanced data were still emphasized as a major problem. All studies had at least one domain with a high risk of bias, and for 20% (9/45) of studies, there were no applicability concerns. CONCLUSIONS This review summarizes recent evidence in automatic multilevel pain estimation from facial expressions and compared the test accuracy of results in a meta-analysis. Promising performance for pain estimation from facial images was established by current CV algorithms. Weaknesses in current studies were also identified, suggesting that larger databases and metrics evaluating multiclass classification performance could improve future studies. CLINICALTRIAL PROSPERO CRD42023418181; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=418181

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Personalized Pain Study Platform using Evidence-Based Continuous Learning Tool

Cited by 3 publications

References 23 publications

Generation of Regular Expressions for Large Clinical Dataset using NLP and Machine Learning Techniques

Generation of Regular Expressions for Large Clinical Dataset using NLP and Machine Learning Techniques

Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis

Application of AI in in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis (Preprint)

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