Multimodal Pain Recognition in Postoperative Patients: Machine Learning Approach
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 or behavioral pain observation tools, which can lead to inconsi...
Gespeichert in:
| Veröffentlicht in: | JMIR formative research Jg. 9; S. e67969 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Canada
JMIR Publications
27.01.2025
|
| Schlagworte: | |
| ISSN: | 2561-326X, 2561-326X |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | 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 or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems. However, most previous work has focused on healthy subjects in controlled environments, with limited attention to real-world postoperative pain scenarios. This gap necessitates the development of robust, multimodal approaches capable of addressing the unique challenges associated with assessing pain in clinical settings, where factors like motion artifacts, imbalanced label distribution, and sparse data further complicate pain monitoring.
This study aimed to develop and evaluate a multimodal machine learning-based framework for the objective assessment of pain in postoperative patients in real clinical settings using biosignals such as electrocardiogram, electromyogram, electrodermal activity, and respiration rate (RR) signals.
The iHurt study was conducted on 25 postoperative patients at the University of California, Irvine Medical Center. The study captured multimodal biosignals during light physical activities, with concurrent self-reported pain levels using the Numerical Rating Scale. Data preprocessing involved noise filtering, feature extraction, and combining handcrafted and automatic features through convolutional and long-short-term memory autoencoders. Machine learning classifiers, including support vector machine, random forest, adaptive boosting, and k-nearest neighbors, were trained using weak supervision and minority oversampling to handle sparse and imbalanced pain labels. Pain levels were categorized into baseline and 3 levels of pain intensity (1-3).
The multimodal pain recognition models achieved an average balanced accuracy of over 80% across the different pain levels. RR models consistently outperformed other single modalities, particularly for lower pain intensities, while facial muscle activity (electromyogram) was most effective for distinguishing higher pain intensities. Although single-modality models, especially RR, generally provided higher performance compared to multimodal approaches, our multimodal framework still delivered results that surpassed most previous works in terms of overall accuracy.
This study presents a novel, multimodal machine learning framework for objective pain recognition in postoperative patients. The results highlight the potential of integrating multiple biosignal modalities for more accurate pain assessment, with particular value in real-world clinical settings. |
|---|---|
| AbstractList | 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 or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems. However, most previous work has focused on healthy subjects in controlled environments, with limited attention to real-world postoperative pain scenarios. This gap necessitates the development of robust, multimodal approaches capable of addressing the unique challenges associated with assessing pain in clinical settings, where factors like motion artifacts, imbalanced label distribution, and sparse data further complicate pain monitoring.Objective:This study aimed to develop and evaluate a multimodal machine learning–based framework for the objective assessment of pain in postoperative patients in real clinical settings using biosignals such as electrocardiogram, electromyogram, electrodermal activity, and respiration rate (RR) signals.Methods:The iHurt study was conducted on 25 postoperative patients at the University of California, Irvine Medical Center. The study captured multimodal biosignals during light physical activities, with concurrent self-reported pain levels using the Numerical Rating Scale. Data preprocessing involved noise filtering, feature extraction, and combining handcrafted and automatic features through convolutional and long-short-term memory autoencoders. Machine learning classifiers, including support vector machine, random forest, adaptive boosting, and k-nearest neighbors, were trained using weak supervision and minority oversampling to handle sparse and imbalanced pain labels. Pain levels were categorized into baseline and 3 levels of pain intensity (1-3).Results:The multimodal pain recognition models achieved an average balanced accuracy of over 80% across the different pain levels. RR models consistently outperformed other single modalities, particularly for lower pain intensities, while facial muscle activity (electromyogram) was most effective for distinguishing higher pain intensities. Although single-modality models, especially RR, generally provided higher performance compared to multimodal approaches, our multimodal framework still delivered results that surpassed most previous works in terms of overall accuracy.Conclusions:This study presents a novel, multimodal machine learning framework for objective pain recognition in postoperative patients. The results highlight the potential of integrating multiple biosignal modalities for more accurate pain assessment, with particular value in real-world clinical settings. 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 or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems. However, most previous work has focused on healthy subjects in controlled environments, with limited attention to real-world postoperative pain scenarios. This gap necessitates the development of robust, multimodal approaches capable of addressing the unique challenges associated with assessing pain in clinical settings, where factors like motion artifacts, imbalanced label distribution, and sparse data further complicate pain monitoring.BACKGROUNDAcute 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 or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems. However, most previous work has focused on healthy subjects in controlled environments, with limited attention to real-world postoperative pain scenarios. This gap necessitates the development of robust, multimodal approaches capable of addressing the unique challenges associated with assessing pain in clinical settings, where factors like motion artifacts, imbalanced label distribution, and sparse data further complicate pain monitoring.This study aimed to develop and evaluate a multimodal machine learning-based framework for the objective assessment of pain in postoperative patients in real clinical settings using biosignals such as electrocardiogram, electromyogram, electrodermal activity, and respiration rate (RR) signals.OBJECTIVEThis study aimed to develop and evaluate a multimodal machine learning-based framework for the objective assessment of pain in postoperative patients in real clinical settings using biosignals such as electrocardiogram, electromyogram, electrodermal activity, and respiration rate (RR) signals.The iHurt study was conducted on 25 postoperative patients at the University of California, Irvine Medical Center. The study captured multimodal biosignals during light physical activities, with concurrent self-reported pain levels using the Numerical Rating Scale. Data preprocessing involved noise filtering, feature extraction, and combining handcrafted and automatic features through convolutional and long-short-term memory autoencoders. Machine learning classifiers, including support vector machine, random forest, adaptive boosting, and k-nearest neighbors, were trained using weak supervision and minority oversampling to handle sparse and imbalanced pain labels. Pain levels were categorized into baseline and 3 levels of pain intensity (1-3).METHODSThe iHurt study was conducted on 25 postoperative patients at the University of California, Irvine Medical Center. The study captured multimodal biosignals during light physical activities, with concurrent self-reported pain levels using the Numerical Rating Scale. Data preprocessing involved noise filtering, feature extraction, and combining handcrafted and automatic features through convolutional and long-short-term memory autoencoders. Machine learning classifiers, including support vector machine, random forest, adaptive boosting, and k-nearest neighbors, were trained using weak supervision and minority oversampling to handle sparse and imbalanced pain labels. Pain levels were categorized into baseline and 3 levels of pain intensity (1-3).The multimodal pain recognition models achieved an average balanced accuracy of over 80% across the different pain levels. RR models consistently outperformed other single modalities, particularly for lower pain intensities, while facial muscle activity (electromyogram) was most effective for distinguishing higher pain intensities. Although single-modality models, especially RR, generally provided higher performance compared to multimodal approaches, our multimodal framework still delivered results that surpassed most previous works in terms of overall accuracy.RESULTSThe multimodal pain recognition models achieved an average balanced accuracy of over 80% across the different pain levels. RR models consistently outperformed other single modalities, particularly for lower pain intensities, while facial muscle activity (electromyogram) was most effective for distinguishing higher pain intensities. Although single-modality models, especially RR, generally provided higher performance compared to multimodal approaches, our multimodal framework still delivered results that surpassed most previous works in terms of overall accuracy.This study presents a novel, multimodal machine learning framework for objective pain recognition in postoperative patients. The results highlight the potential of integrating multiple biosignal modalities for more accurate pain assessment, with particular value in real-world clinical settings.CONCLUSIONSThis study presents a novel, multimodal machine learning framework for objective pain recognition in postoperative patients. The results highlight the potential of integrating multiple biosignal modalities for more accurate pain assessment, with particular value in real-world clinical settings. BackgroundAcute 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 or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems. However, most previous work has focused on healthy subjects in controlled environments, with limited attention to real-world postoperative pain scenarios. This gap necessitates the development of robust, multimodal approaches capable of addressing the unique challenges associated with assessing pain in clinical settings, where factors like motion artifacts, imbalanced label distribution, and sparse data further complicate pain monitoring. ObjectiveThis study aimed to develop and evaluate a multimodal machine learning–based framework for the objective assessment of pain in postoperative patients in real clinical settings using biosignals such as electrocardiogram, electromyogram, electrodermal activity, and respiration rate (RR) signals. MethodsThe iHurt study was conducted on 25 postoperative patients at the University of California, Irvine Medical Center. The study captured multimodal biosignals during light physical activities, with concurrent self-reported pain levels using the Numerical Rating Scale. Data preprocessing involved noise filtering, feature extraction, and combining handcrafted and automatic features through convolutional and long-short-term memory autoencoders. Machine learning classifiers, including support vector machine, random forest, adaptive boosting, and k-nearest neighbors, were trained using weak supervision and minority oversampling to handle sparse and imbalanced pain labels. Pain levels were categorized into baseline and 3 levels of pain intensity (1-3). ResultsThe multimodal pain recognition models achieved an average balanced accuracy of over 80% across the different pain levels. RR models consistently outperformed other single modalities, particularly for lower pain intensities, while facial muscle activity (electromyogram) was most effective for distinguishing higher pain intensities. Although single-modality models, especially RR, generally provided higher performance compared to multimodal approaches, our multimodal framework still delivered results that surpassed most previous works in terms of overall accuracy. ConclusionsThis study presents a novel, multimodal machine learning framework for objective pain recognition in postoperative patients. The results highlight the potential of integrating multiple biosignal modalities for more accurate pain assessment, with particular value in real-world clinical settings. 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 or behavioral pain observation tools, which can lead to inconsistencies in pain management. Multimodal pain assessment, integrating physiological and behavioral data, presents an opportunity to create more objective and accurate pain measurement systems. However, most previous work has focused on healthy subjects in controlled environments, with limited attention to real-world postoperative pain scenarios. This gap necessitates the development of robust, multimodal approaches capable of addressing the unique challenges associated with assessing pain in clinical settings, where factors like motion artifacts, imbalanced label distribution, and sparse data further complicate pain monitoring. This study aimed to develop and evaluate a multimodal machine learning-based framework for the objective assessment of pain in postoperative patients in real clinical settings using biosignals such as electrocardiogram, electromyogram, electrodermal activity, and respiration rate (RR) signals. The iHurt study was conducted on 25 postoperative patients at the University of California, Irvine Medical Center. The study captured multimodal biosignals during light physical activities, with concurrent self-reported pain levels using the Numerical Rating Scale. Data preprocessing involved noise filtering, feature extraction, and combining handcrafted and automatic features through convolutional and long-short-term memory autoencoders. Machine learning classifiers, including support vector machine, random forest, adaptive boosting, and k-nearest neighbors, were trained using weak supervision and minority oversampling to handle sparse and imbalanced pain labels. Pain levels were categorized into baseline and 3 levels of pain intensity (1-3). The multimodal pain recognition models achieved an average balanced accuracy of over 80% across the different pain levels. RR models consistently outperformed other single modalities, particularly for lower pain intensities, while facial muscle activity (electromyogram) was most effective for distinguishing higher pain intensities. Although single-modality models, especially RR, generally provided higher performance compared to multimodal approaches, our multimodal framework still delivered results that surpassed most previous works in terms of overall accuracy. This study presents a novel, multimodal machine learning framework for objective pain recognition in postoperative patients. The results highlight the potential of integrating multiple biosignal modalities for more accurate pain assessment, with particular value in real-world clinical settings. |
| Author | Liljeberg, Pasi Rahmani, Amir M Cao, Rui Subramanian, Ajan Salanterä, Sanna Nelson, Ariana M Aqajari, Seyed Amir Hossein Naeini, Emad Kasaeyan Calderon, Michael-David Dutt, Nikil Zheng, Kai Hughes, Thomas D |
| AuthorAffiliation | 8 Turku University Hospital University of Turku Turku Finland 9 Department of Anesthesiology and Pain Medicine University of California, Irvine Irvine, CA United States 2 Department of Electrical Engineering and Computer Science University of California, Irvine Irvine, CA United States 1 Department of Computer Science University of California, Irvine Irvine, CA United States 4 College of Medicine Kansas City University Kansas City, MO United States 5 Department of Informatics University of California, Irvine Irvine, CA United States 10 Institute for Future Health University of California, Irvine Irvine, CA United States 6 Department of Computing University of Turku Turku Finland 3 School of Nursing University of California, Irvine Irvine, CA United States 7 Department of Nursing Science University of Turku Turku Finland |
| AuthorAffiliation_xml | – name: 4 College of Medicine Kansas City University Kansas City, MO United States – name: 5 Department of Informatics University of California, Irvine Irvine, CA United States – name: 8 Turku University Hospital University of Turku Turku Finland – name: 10 Institute for Future Health University of California, Irvine Irvine, CA United States – name: 9 Department of Anesthesiology and Pain Medicine University of California, Irvine Irvine, CA United States – name: 3 School of Nursing University of California, Irvine Irvine, CA United States – name: 7 Department of Nursing Science University of Turku Turku Finland – name: 1 Department of Computer Science University of California, Irvine Irvine, CA United States – name: 2 Department of Electrical Engineering and Computer Science University of California, Irvine Irvine, CA United States – name: 6 Department of Computing University of Turku Turku Finland |
| Author_xml | – sequence: 1 givenname: Ajan orcidid: 0000-0003-3253-1300 surname: Subramanian fullname: Subramanian, Ajan – sequence: 2 givenname: Rui orcidid: 0000-0001-9295-8299 surname: Cao fullname: Cao, Rui – sequence: 3 givenname: Emad Kasaeyan orcidid: 0000-0002-7438-2641 surname: Naeini fullname: Naeini, Emad Kasaeyan – sequence: 4 givenname: Seyed Amir Hossein orcidid: 0000-0003-1747-6980 surname: Aqajari fullname: Aqajari, Seyed Amir Hossein – sequence: 5 givenname: Thomas D orcidid: 0000-0002-0651-9394 surname: Hughes fullname: Hughes, Thomas D – sequence: 6 givenname: Michael-David orcidid: 0000-0001-6824-6945 surname: Calderon fullname: Calderon, Michael-David – sequence: 7 givenname: Kai orcidid: 0000-0003-4121-4948 surname: Zheng fullname: Zheng, Kai – sequence: 8 givenname: Nikil orcidid: 0000-0002-3060-8119 surname: Dutt fullname: Dutt, Nikil – sequence: 9 givenname: Pasi orcidid: 0000-0002-9392-3589 surname: Liljeberg fullname: Liljeberg, Pasi – sequence: 10 givenname: Sanna orcidid: 0000-0003-2529-6699 surname: Salanterä fullname: Salanterä, Sanna – sequence: 11 givenname: Ariana M orcidid: 0000-0003-1575-1635 surname: Nelson fullname: Nelson, Ariana M – sequence: 12 givenname: Amir M orcidid: 0000-0003-0725-1155 surname: Rahmani fullname: Rahmani, Amir M |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39869898$$D View this record in MEDLINE/PubMed |
| BookMark | eNpdkm9rFDEQxoNUbK33FWRBBKGcJpvdbOIbKaVq4YqHKPguzGZnrzn2km2SLfjtzfZa6fVN_kx-PDN5Zl6TI-cdErJg9GPJlPgkGiXUC3JS1oIteSn-HD05H5NFjFtKaclYBvkrcsyVFEoqeULW19OQ7M53MBRrsK74icZvnE3WuyJf1z4mP2KAZO8wE8miS_FzcQ3mxjosVgjBWbcpzscx-Bx8Q172MERcPOyn5PfXy18X35erH9-uLs5XS1PVMi17pRpGAXraq4YqVan8j5Yr0bAemQBamwaN5JQqib0pJbCqM2hEnVfeAz8lV3vdzsNWj8HuIPzVHqy-D_iw0RCSNQNqoYBiB7ylXVt1tZSs7Vte0kZl22qYtb7stcap3WFO4FKA4UD08MXZG73xd5oxmT0VZVb48KAQ_O2EMemdjQaHARz6KWrOBK1Etr_K6Ltn6NZPwWWvZqriVNT1TL19WtL_Wh47l4GzPWCCjzFgr41NMLctV2gHzaieR0Pfj0am3z-jHwUPuX96CbZv |
| CitedBy_id | crossref_primary_10_56294_cid2025127 crossref_primary_10_56294_cid2025154 crossref_primary_10_1038_s41598_025_12476_8 |
| Cites_doi | 10.1109/CYBConf.2013.6617456 10.1145/3341105.3373945 10.1002/bjs.11477 10.2196/17783 10.2196/25258 10.1109/ICSMC.2005.1571679 10.1109/TAFFC.2017.2784832 10.1109/tbme.2015.2474131 10.1145/2388676.2388688 10.1109/ICPR.2014.784 10.14778/3157794.3157797 10.48550/arXiv.1708.08755 10.1109/EMBC44109.2020.9175247 10.1016/1058-9139(92)90001-S 10.1093/pm/pnx019 10.1016/j.jpainsymman.2018.12.333 10.1016/j.jpain.2015.12.008 10.1109/EMBC46164.2021.9630002 10.48550/arXiv.2112.08176 10.5244/C.27.119 10.1613/jair.953 10.1016/j.pmn.2019.07.005 10.1007/s00482-020-00501-w 10.1109/imtc.2011.5944249 10.1016/j.procs.2021.03.021 10.1097/j.pain.0000000000000160 10.1016/s0005-7967(01)00072-9 10.1016/j.neunet.2014.09.003 10.1109/jsen.2020.3023656 10.2196/25079 10.1007/s11916-009-0009-x 10.1109/SAS.2017.7894053 |
| ContentType | Journal Article |
| Copyright | Ajan Subramanian, Rui Cao, Emad Kasaeyan Naeini, Seyed Amir Hossein Aqajari, Thomas D Hughes, Michael-David Calderon, Kai Zheng, Nikil Dutt, Pasi Liljeberg, Sanna Salanterä, Ariana M Nelson, Amir M Rahmani. Originally published in JMIR Formative Research (https://formative.jmir.org), 27.01.2025. 2025. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Ajan Subramanian, Rui Cao, Emad Kasaeyan Naeini, Seyed Amir Hossein Aqajari, Thomas D Hughes, Michael-David Calderon, Kai Zheng, Nikil Dutt, Pasi Liljeberg, Sanna Salanterä, Ariana M Nelson, Amir M Rahmani. Originally published in JMIR Formative Research (https://formative.jmir.org), 27.01.2025. 2025 |
| Copyright_xml | – notice: Ajan Subramanian, Rui Cao, Emad Kasaeyan Naeini, Seyed Amir Hossein Aqajari, Thomas D Hughes, Michael-David Calderon, Kai Zheng, Nikil Dutt, Pasi Liljeberg, Sanna Salanterä, Ariana M Nelson, Amir M Rahmani. Originally published in JMIR Formative Research (https://formative.jmir.org), 27.01.2025. – notice: 2025. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: Ajan Subramanian, Rui Cao, Emad Kasaeyan Naeini, Seyed Amir Hossein Aqajari, Thomas D Hughes, Michael-David Calderon, Kai Zheng, Nikil Dutt, Pasi Liljeberg, Sanna Salanterä, Ariana M Nelson, Amir M Rahmani. Originally published in JMIR Formative Research (https://formative.jmir.org), 27.01.2025. 2025 |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7RV 7X7 7XB 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. KB0 M0S NAPCQ PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.2196/67969 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Nursing & Allied Health Database Health & Medical Collection ProQuest Central (purchase pre-March 2016) ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Korea Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Database (Alumni Edition) ProQuest Health & Medical Collection Nursing & Allied Health Premium ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea ProQuest Central (New) ProQuest One Academic Eastern Edition ProQuest Nursing & Allied Health Source ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) Nursing & Allied Health Premium ProQuest Health & Medical Complete ProQuest One Academic UKI Edition ProQuest Nursing & Allied Health Source (Alumni) ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7RV name: Nursing & Allied Health Database url: https://search.proquest.com/nahs sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine |
| EISSN | 2561-326X |
| ExternalDocumentID | oai_doaj_org_article_69a0eda3b0db4d5881bfb320797965aa PMC11811662 39869898 10_2196_67969 |
| Genre | Journal Article |
| GeographicLocations | United States--US California |
| GeographicLocations_xml | – name: United States--US – name: California |
| GroupedDBID | 53G 7RV 7X7 8FI 8FJ AAFWJ AAYXX ABUWG ADBBV AFFHD AFKRA AFPKN ALMA_UNASSIGNED_HOLDINGS AOIJS ARCSS BCNDV BENPR CCPQU CITATION FYUFA GROUPED_DOAJ HMCUK HYE M~E NAPCQ OK1 PGMZT PHGZM PHGZT PIMPY PPXIY RPM UKHRP ALIPV CGR CUY CVF ECM EIF NPM 3V. 7XB 8FK AZQEC DWQXO K9. PJZUB PKEHL PQEST PQQKQ PQUKI PRINS 7X8 5PM |
| ID | FETCH-LOGICAL-c458t-f99710aaf0f9709949196b39671fe16a05c7ec830098efc28a14dcec65dce3fa3 |
| IEDL.DBID | 7X7 |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001447515800046&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2561-326X |
| IngestDate | Mon Nov 10 04:35:04 EST 2025 Tue Nov 04 02:04:58 EST 2025 Sun Nov 09 13:25:33 EST 2025 Tue Oct 07 06:54:09 EDT 2025 Tue May 06 01:31:32 EDT 2025 Sat Nov 29 04:49:54 EST 2025 Tue Nov 18 22:36:19 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | machine learning approach pain recognition acute pain signal processing pain monitoring pain assessment electrodermal activity pain intensity multimodal machine learning–based framework electromyogram self-reported pain level weak supervision multimodal information fusion machine learning–based framework clinical pain management electrocardiogram pain measurement pain intensity recognition behavioral pain health care |
| Language | English |
| License | Ajan Subramanian, Rui Cao, Emad Kasaeyan Naeini, Seyed Amir Hossein Aqajari, Thomas D Hughes, Michael-David Calderon, Kai Zheng, Nikil Dutt, Pasi Liljeberg, Sanna Salanterä, Ariana M Nelson, Amir M Rahmani. Originally published in JMIR Formative Research (https://formative.jmir.org), 27.01.2025. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c458t-f99710aaf0f9709949196b39671fe16a05c7ec830098efc28a14dcec65dce3fa3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0003-1747-6980 0000-0003-4121-4948 0000-0003-0725-1155 0000-0002-3060-8119 0000-0001-9295-8299 0000-0002-7438-2641 0000-0003-1575-1635 0000-0003-3253-1300 0000-0002-9392-3589 0000-0001-6824-6945 0000-0003-2529-6699 0000-0002-0651-9394 |
| OpenAccessLink | https://www.proquest.com/docview/3164306554?pq-origsite=%requestingapplication% |
| PMID | 39869898 |
| PQID | 3164306554 |
| PQPubID | 4997113 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_69a0eda3b0db4d5881bfb320797965aa pubmedcentral_primary_oai_pubmedcentral_nih_gov_11811662 proquest_miscellaneous_3160461674 proquest_journals_3164306554 pubmed_primary_39869898 crossref_citationtrail_10_2196_67969 crossref_primary_10_2196_67969 |
| PublicationCentury | 2000 |
| PublicationDate | 20250127 |
| PublicationDateYYYYMMDD | 2025-01-27 |
| PublicationDate_xml | – month: 1 year: 2025 text: 20250127 day: 27 |
| PublicationDecade | 2020 |
| PublicationPlace | Canada |
| PublicationPlace_xml | – name: Canada – name: Toronto – name: Toronto, Canada |
| PublicationTitle | JMIR formative research |
| PublicationTitleAlternate | JMIR Form Res |
| PublicationYear | 2025 |
| Publisher | JMIR Publications |
| Publisher_xml | – name: JMIR Publications |
| References | ref13 ref35 ref12 ref15 ref37 ref14 ref36 ref31 ref33 ref10 ref2 Merskey, HA (ref1) 1979; 6 ref17 ref16 ref38 ref19 ref18 Bonica, JJ (ref4) 1984 Ross, A (ref39) 2009 ref24 Le, QV (ref30) 2015 ref23 ref26 ref25 ref20 ref41 ref22 ref21 ref28 ref27 ref29 ref8 ref7 Kohavi, R (ref34) 1995 ref9 ref3 ref6 ref5 ref40 Staton, LJ (ref11) 2007; 99 Lemaître, G (ref32) 2017; 18 |
| References_xml | – ident: ref14 doi: 10.1109/CYBConf.2013.6617456 – ident: ref22 doi: 10.1145/3341105.3373945 – ident: ref7 doi: 10.1002/bjs.11477 – ident: ref20 doi: 10.2196/17783 – ident: ref25 doi: 10.2196/25258 – ident: ref35 doi: 10.1109/ICSMC.2005.1571679 – volume: 18 start-page: 559 issue: 1 year: 2017 ident: ref32 publication-title: J Mach Learn Res – ident: ref41 doi: 10.1109/TAFFC.2017.2784832 – ident: ref26 doi: 10.1109/tbme.2015.2474131 – ident: ref16 doi: 10.1145/2388676.2388688 – ident: ref17 doi: 10.1109/ICPR.2014.784 – ident: ref19 – ident: ref33 doi: 10.14778/3157794.3157797 – ident: ref36 doi: 10.48550/arXiv.1708.08755 – start-page: 1137 year: 1995 ident: ref34 publication-title: Proc. 14th IJCAI – ident: ref37 doi: 10.1109/EMBC44109.2020.9175247 – volume: 6 start-page: 249 issue: 3 year: 1979 ident: ref1 publication-title: Pain – ident: ref9 doi: 10.1016/1058-9139(92)90001-S – ident: ref3 doi: 10.1093/pm/pnx019 – ident: ref12 doi: 10.1016/j.jpainsymman.2018.12.333 – ident: ref6 doi: 10.1016/j.jpain.2015.12.008 – ident: ref27 doi: 10.1109/EMBC46164.2021.9630002 – ident: ref40 doi: 10.48550/arXiv.2112.08176 – volume: 99 start-page: 532 issue: 5 year: 2007 ident: ref11 publication-title: J Natl Med Assoc – ident: ref15 doi: 10.5244/C.27.119 – ident: ref31 doi: 10.1613/jair.953 – ident: ref8 doi: 10.1016/j.pmn.2019.07.005 – ident: ref13 doi: 10.1007/s00482-020-00501-w – ident: ref28 doi: 10.1109/imtc.2011.5944249 – year: 2009 ident: ref39 publication-title: Fusion, Feature-Level – ident: ref21 – ident: ref24 doi: 10.1016/j.procs.2021.03.021 – ident: ref5 doi: 10.1097/j.pain.0000000000000160 – year: 1984 ident: ref4 publication-title: Pain in the Cancer Patient: Pathogenesis, Diagnosis and Therapy – ident: ref10 doi: 10.1016/s0005-7967(01)00072-9 – ident: ref29 doi: 10.1016/j.neunet.2014.09.003 – ident: ref38 doi: 10.1109/jsen.2020.3023656 – ident: ref23 doi: 10.2196/25079 – ident: ref2 doi: 10.1007/s11916-009-0009-x – ident: ref18 doi: 10.1109/SAS.2017.7894053 – start-page: 1 year: 2015 ident: ref30 publication-title: Google Brain |
| SSID | ssj0002116793 |
| Score | 2.2948399 |
| Snippet | Acute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pain levels... Background:Acute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pain levels... BackgroundAcute pain management is critical in postoperative care, especially in vulnerable patient populations that may be unable to self-report pain levels... |
| SourceID | doaj pubmedcentral proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | e67969 |
| SubjectTerms | Adult Aged Behavior Chronic pain Data collection Datasets Dementia Electrocardiography Electroencephalography Female Heart rate Humans Machine Learning Male Medical research Middle Aged Minority & ethnic groups Original Paper Pain Measurement - methods Pain, Postoperative - diagnosis Patients Physiology Software |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3fS8MwED5EZAgi_rY6RwVfi22TNolvUxQf3Bii4FtJk1QFbYeb_v1e0q6sIvjiSyG9e0jukua75vIdwBk1TFMlKK40rSyptgm4DvPA0JwqbAkhHYnrHRuP-dOTmCyV-rI5YTU9cG2481TI0GhJ8lDnVCccYVaRkzhkgok0kQ4aYWMpmLLf4NgdL5AebNhcZ5xl5_Z_iehsPo6j_zdg-TM_cmnDudmCzQYp-sO6h9uwYsod6I2as_BdmLi7s--VRqUJxvf-_SIXqCp9bNoyvNXU1MzeqOH4U2cX_silTxq_YVZ99ocNrfgePN5cP1zdBk19hEDRhM-DQgjEB1IWYSEYIj0qcKA5ESmLChOlMkwUM4oTyxlqChVzGVEck0oTfJJCkn1YLavSHIIviYploWksEVJFSnOEiQiNuFI5kZwmHpwtDJephjzc1rB4yzCIsPbNnH09GLRq05ot46fCpbV6K7Tk1u4FujxrXJ795XIP-gufZc2Km2UE4z4MfxAdeXDainGt2AMQWZrq0-lYfvmUoc5B7eK2J0RwV0vTA95xfqerXUn5-uL4uO3d3ShN46P_GNwxrMe2xHAYBTHrw-r849OcwJr6mr_OPgZuln8DR0AEdQ priority: 102 providerName: Directory of Open Access Journals |
| Title | Multimodal Pain Recognition in Postoperative Patients: Machine Learning Approach |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/39869898 https://www.proquest.com/docview/3164306554 https://www.proquest.com/docview/3160461674 https://pubmed.ncbi.nlm.nih.gov/PMC11811662 https://doaj.org/article/69a0eda3b0db4d5881bfb320797965aa |
| Volume | 9 |
| WOSCitedRecordID | wos001447515800046&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2561-326X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002116793 issn: 2561-326X databaseCode: DOA dateStart: 20170101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2561-326X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002116793 issn: 2561-326X databaseCode: M~E dateStart: 20170101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 2561-326X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002116793 issn: 2561-326X databaseCode: 7X7 dateStart: 20170101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: Nursing & Allied Health Database customDbUrl: eissn: 2561-326X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002116793 issn: 2561-326X databaseCode: 7RV dateStart: 20170101 isFulltext: true titleUrlDefault: https://search.proquest.com/nahs providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2561-326X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002116793 issn: 2561-326X databaseCode: BENPR dateStart: 20170101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2561-326X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002116793 issn: 2561-326X databaseCode: PIMPY dateStart: 20170101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3fb9MwED7BhqZJiN-DwIiCtNdoSezENi9oQ5tAolVUASpPkWM7oxIkpe34-7lz3UInxAsvkVyfIst3l3w9X74P4IQ7YblRHDPNGiLVdqm0WZs63nKDI6W0J3H9IMZjOZ2qOhTclqGtcvNM9A9qOxiqkZ8yxPWkcl7yN_MfKalG0elqkNC4Dfskm01xLqZiW2Mp_CEDO4C71PGMsXZKVRO18wryTP1_g5c3uyT_eO1c3v_fBT-AewFwJmfrCHkIt1z_CA5G4Uj9MdT-E9zvg0WjWs_6ZLJpKRr6BIek5jvM3ZogHC08DevydTLyXZguCQStV8lZYCd_Ap8uLz6-fZcGmYXU8FKu0k4phBlad1mnBAJGrnCnWqYqkXcur3RWGuGMZEQ96jpTSJ1z3BRTlXhlnWZHsNcPvXsGiWam0J3lhUZklhsrEW0iwpLGtExLXkZwstn5xgQOcpLC-NbgfxFyUOMdFEG8NZuvSTduGpyT27aTxJHtfxgWV01IuaZSOnNWszazLbelRIDetazIhMJblFpHcLxxXBMSd9n89loEr7bTmHJ0jqJ7N1x7G6KprwTaPF3HyHYlTEkvyRmB3ImenaXuzvSzr57Wmz4BzquqeP7vdb2Aw4I0iLM8LcQx7K0W1-4l3DE_V7PlIsYEmHyOfRr4q4xh__xiXE9iX23AUf1-VH_5BeylGBo |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3bbtQwEB2VLSpIiFsLDZQSpPIYNbGdxEZCqFyqrrq7ilCRylNwbKesBMmyuy3ip_hGxs4FtkK89YGXSIlHlpMcj8f2-ByAPWZSzZRg2NO0sqTaJuA6LALDCqbwTgjpSFxH6WTCT09FtgY_u7MwNq2y84nOUeta2TXyfYpxvVU5j9mr2bfAqkbZ3dVOQqOBxbH58R2nbIuXw7f4f58Tcvju5M1R0KoKBIrFfBmUQuCoKmUZliLF-IgJBGFBRZJGpYkSGcYqNYpTy7RpSkW4jJhWRiUxXmkpKdZ7DdaZBfsA1rPhOPvYr-oQt61BN-CWzbHGivftOo1YGfScNsDfAtrLeZl_DHSHd_63T3QXbrchtX_Q9IF7sGaq-7AxbpMGNiFzh4y_1hqNMjmt_Pdd0lRd-Xhr9YrrmWko0NHCEc0uXvhjl2dq_JaC9sw_aPnXt-DDlbzPAxhUdWW2wZdUEVlqRiTGnpHSHONpjCG5UgWVnMUe7HV_Olcty7oV-_iS42zLAiJ3gPBgtzebNbQilw1eW5j0hZYF3D2o52d561TyRMjQaEmLUBdMxxynIGVBSZgKrCKW0oOdDih565oW-W-UePCsL0anYneKZGXqc2djifiTFG0eNpjsW0IFd6KjHvAVtK40dbWkmn52xOX2kHOUJOTRv9v1FG4cnYxH-Wg4OX4MN4lVXA6jgKQ7MFjOz80TuK4ultPFfLftfj58umo4_wKo23EJ |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1db9MwFL0aHaqQ0PiGwBhBGo9RE9tJbCSEBqOi2lpFCKTxFBzbGZUgKW0H4q_t13HtfEAnxNseeImU-MpykuPra_v6HIB9ZlLNlGDY07SypNom4DosAsMKpvBOCOlIXI_T2YyfnIhsC867szA2rbLzic5R61rZNfIRxbjeqpzHbFS2aRHZ4fjl4ltgFaTsTmsnp9FA5Mj8_IHTt9WLySH-62eEjN-8f_02aBUGAsVivg5KIXCElbIMS5FirMQEArKgIkmj0kSJDGOVGsWpZd00pSJcRkwro5IYr7SUFOu9AtsYkjMygO1sMs0-9is8xG1x0CFct_nWWPHIrtmIjQHQ6QT8Lbi9mKP5x6A3vvE_f66bsNOG2v5B0zduwZapbsNw2iYT3IHMHT7-Wms0yuS88t91yVR15eOt1TGuF6ahRkcLR0C7eu5PXf6p8Vtq2lP_oOVlvwsfLuV97sGgqivzAHxJFZGlZkRiTBopzTHOxtiSK1VQyVnswX7313PVsq9bEZAvOc7CLDhyBw4P9nqzRUM3ctHglYVMX2jZwd2Denmat84mT4QMjZa0CHXBdMxxalIWlISpwCpiKT3Y7UCTty5rlf9GjAdP-2J0NnYHSVamPnM2lqA_SdHmfoPPviVUcCdG6gHfQO5GUzdLqvlnR2huDz9HSUIe_rtdT2CIGM6PJ7OjR3CNWCHmMApIuguD9fLMPIar6vt6vlrutT3Rh0-XjeZfdnZ5yQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Multimodal+Pain+Recognition+in+Postoperative+Patients%3A+Machine+Learning+Approach&rft.jtitle=JMIR+formative+research&rft.au=Subramanian%2C+Ajan&rft.au=Cao%2C+Rui&rft.au=Naeini%2C+Emad+Kasaeyan&rft.au=Seyed+Amir+Hossein+Aqajari&rft.date=2025-01-27&rft.pub=JMIR+Publications&rft.eissn=2561-326X&rft.volume=9&rft.spage=e67969&rft_id=info:doi/10.2196%2F67969&rft.externalDBID=HAS_PDF_LINK |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2561-326X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2561-326X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2561-326X&client=summon |