{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T07:26:28Z","timestamp":1767165988839,"version":"build-2238731810"},"reference-count":45,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T00:00:00Z","timestamp":1686614400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Wound Journal"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Pressure injury (PI), or local damage to soft tissues and skin caused by prolonged pressure, remains controversial in the medical world. Patients in intensive care units (ICUs) were frequently reported to suffer PIs, with a heavy burden on their life and expenditures. Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in nursing practice and is increasingly used for diagnosis, complications, prognosis, and recurrence prediction. This study aims to investigate hospital\u2010acquired PI (HAPI) risk predictions in ICU based on a ML algorithm by R programming language analysis. The former evidence was gathered through PRISMA guidelines. The logical analysis was applied via an R programming language. ML algorithms based on usage rate included logistic regression (LR), Random Forest (RF), Distributed tree (DT), Artificial neural networks (ANN), SVM (Support Vector Machine), Batch normalisation (BN), GB (Gradient Boosting), expectation\u2013maximisation (EM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). Six cases were related to risk predictions of HAPI in the ICU based on an ML algorithm from seven obtained studies, and one study was associated with the Detection of PI risk. Also, the most estimated risksSerum Albumin, Lack of Activity, mechanical ventilation (MV), partial pressure of oxygen (PaO2), Surgery, Cardiovascular adequacy, ICU stay, Vasopressor, Consciousness, Skin integrity, Recovery Unit, insulin and oral antidiabetic (INS&amp;OAD), Complete blood count (CBC), acute physiology and chronic health evaluation (APACHE) II score, Spontaneous bacterial peritonitis (SBP), Steroid, Demineralized Bone Matrix (DBM), Braden score, Faecal incontinence, Serum Creatinine (SCr) and age. In sum, HAPI prediction and PI risk detection are two significant areas for using ML in PI analysis. Also, the current data showed that the ML algorithm, including LR and RF, could be regarded as the practical platform for developing AI tools for diagnosing, prognosis, and treating PI in hospital units, especially ICU.<\/jats:p>","DOI":"10.1111\/iwj.14275","type":"journal-article","created":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T02:49:58Z","timestamp":1686710998000},"page":"3768-3775","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["RETRACTED: Risk predictions of hospital\u2010acquired pressure injury in the intensive care unit based on a machine learning algorithm"],"prefix":"10.1111","volume":"20","author":[{"given":"Pooya M.","family":"Tehrany","sequence":"first","affiliation":[{"name":"Department of Orthopaedic Surgery, Faculty of Medicine National University of Malaysia  Bani Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad Reza","family":"Zabihi","sequence":"additional","affiliation":[{"name":"Department of Immunology, School of Medicine Tehran University of Medical Sciences  Tehran Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3365-2681","authenticated-orcid":false,"given":"Pooyan","family":"Ghorbani Vajargah","sequence":"additional","affiliation":[{"name":"Burn and Regenerative Medicine Research Center Guilan University of Medical Sciences  Rasht Iran"},{"name":"Student Research Committee, Department of Medical\u2010Surgical Nursing, School of Nursing and Midwifery Guilan University of Medical Sciences  Rasht Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6606-4160","authenticated-orcid":false,"given":"Pegah","family":"Tamimi","sequence":"additional","affiliation":[{"name":"Center for Research and Training in Skin Diseases and Leprosy Tehran University of Medical Sciences  Tehran Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aliasghar","family":"Ghaderi","sequence":"additional","affiliation":[{"name":"Center for Research and Training in Skin Diseases and Leprosy Tehran University of Medical Sciences  Tehran Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Narges","family":"Norouzkhani","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics, Faculty of Medicine Mashhad University of Medical Sciences  Mashhad Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Morteza","family":"Zaboli Mahdiabadi","sequence":"additional","affiliation":[{"name":"Student Research Committee Shahid Sadoughi University of Medical Sciences  Yazd Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9193-9176","authenticated-orcid":false,"given":"Samad","family":"Karkhah","sequence":"additional","affiliation":[{"name":"Burn and Regenerative Medicine Research Center Guilan University of Medical Sciences  Rasht Iran"},{"name":"Student Research Committee, Department of Medical\u2010Surgical Nursing, School of Nursing and Midwifery Guilan University of Medical Sciences  Rasht Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2539-8751","authenticated-orcid":false,"given":"Mohammad","family":"Akhoondian","sequence":"additional","affiliation":[{"name":"Department of Physiology, School of Medicine, Cellular and the Molecular Research Center Guilan University of Medical Science  Rasht Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7347-6574","authenticated-orcid":false,"given":"Ramyar","family":"Farzan","sequence":"additional","affiliation":[{"name":"Department of Plastic &amp; Reconstructive Surgery, School of Medicine Guilan University of Medical Sciences  Rasht Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,6,13]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaad.2018.12.069"},{"key":"e_1_2_12_3_1","first-page":"39","article-title":"Results of the 2008\u20132009 international pressure ulcer prevalence\u2122 survey and a 3\u2010year, acute care, unit\u2010specific analysis","volume":"55","author":"Catherine VanGilder M","year":"2009","journal-title":"Ostomy Wound Manag"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-020-01371-z"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijnurstu.2018.08.005"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/NNR.0000000000000258"},{"issue":"12","key":"e_1_2_12_7_1","article-title":"Characteristics of hospital acquired pressure ulcer and factors affecting its development: a retrospective study","volume":"12","author":"Mortada H","year":"2020","journal-title":"Cureus"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1097\/WON.0000000000000292"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/iwj.13071"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/nop2.1429"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.1159\/000516891"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygyno.2020.10.004"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104305"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2021.104510"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.2196\/25704"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1097\/EDE.0b013e3181fe7825"},{"key":"e_1_2_12_17_1","volume-title":"Beginning R: the Statistical Programming Language","author":"Gardener M","year":"2012"},{"key":"e_1_2_12_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.annonc.2020.10.204"},{"issue":"2","key":"e_1_2_12_19_1","first-page":"744","article-title":"Identification of aberrantly methylated\u2010differentially expressed genes and gene ontology in prostate cancer","volume":"21","author":"Wang L","year":"2020","journal-title":"Mol Med Rep"},{"key":"e_1_2_12_20_1","doi-asserted-by":"publisher","DOI":"10.1097\/CIN.0000000000000444"},{"issue":"5","key":"e_1_2_12_21_1","first-page":"352","article-title":"Logistic regression and artificial neural network classification models: a methodology review","volume":"35","author":"Stephan D","year":"2002","journal-title":"J Biomed Inform"},{"issue":"5","key":"e_1_2_12_22_1","first-page":"546","article-title":"Ordinal logistic regression in medical research","volume":"31","author":"Bender R","year":"1997","journal-title":"J R Coll Physicians Lond"},{"key":"e_1_2_12_23_1","unstructured":"LumleyT KronmalR MaS.Relative Risk Regression in Medical Research: Models Contrasts Estimators and Algorithms.2006."},{"key":"e_1_2_12_24_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph20010828"},{"key":"e_1_2_12_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2738069"},{"key":"e_1_2_12_26_1","doi-asserted-by":"crossref","unstructured":"VijiyaKumarK LavanyaB NirmalaI CarolineSS.Random forest algorithm for the prediction of diabetes. In: Proceedings of the 2019 IEEE international conference on system computation automation and networking (ICSCAN). 2019.","DOI":"10.1109\/ICSCAN.2019.8878802"},{"key":"e_1_2_12_27_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2022\/3882425","article-title":"Efficacy analysis of team\u2010based nursing compliance in young and middle\u2010aged diabetes mellitus patients based on random Forest algorithm and logistic regression","volume":"2022","author":"Qian D","year":"2022","journal-title":"Comput Math Methods Med"},{"key":"e_1_2_12_28_1","unstructured":"ZanzottoFM Dell'ArcipreteL.Distributed tree kernels.arXiv preprint arXiv:12064607.2012."},{"key":"e_1_2_12_29_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-021-07907-5"},{"key":"e_1_2_12_30_1","doi-asserted-by":"publisher","DOI":"10.1038\/ngeo2400"},{"key":"e_1_2_12_31_1","first-page":"265","volume-title":"28th European Symposium on Computer Aided Process Engineering","author":"Ahmed S","year":"2018"},{"key":"e_1_2_12_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-815553-0.00013-6"},{"issue":"3","key":"e_1_2_12_33_1","first-page":"112","article-title":"Is there equality of nursing care insurance coverage for ambulatory and non\u2010ambulatory clients according to German law?","volume":"61","author":"Michel E","year":"1999","journal-title":"Gesundheitswesen (Bundesverband der Arzte Des Offentlichen Gesundheitsdienstes [Germany])"},{"key":"e_1_2_12_34_1","doi-asserted-by":"crossref","unstructured":"SricharanC SudhanKM SreenivasraoD DeviBV.Mortality Prediction in ICU Using Artificial Neural Networks. 2022.","DOI":"10.22214\/ijraset.2022.44269"},{"key":"e_1_2_12_35_1","doi-asserted-by":"crossref","unstructured":"JenT\u2010H ChienT\u2010W YehY\u2010T LeeH\u2010F.An App Developed for Predicting Nurse Intention to Quit the Job (NIQJ) Using the Artificial Neural Networks (ANN) in Microsoft Excel: Population\u2010Based Questionnaire Study.2021.","DOI":"10.21203\/rs.3.rs-152558\/v1"},{"key":"e_1_2_12_36_1","doi-asserted-by":"crossref","unstructured":"IslamMT RaihanM FarzanaF RajuMGM HossainMB.An empirical study on diabetes mellitus prediction for typical and non\u2010typical cases using machine learning approaches. In: Proceedings of the 2019 10th International Conference on Computing Communication and Networking Technologies (ICCCNT). 2019.","DOI":"10.1109\/ICCCNT45670.2019.8944528"},{"key":"e_1_2_12_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2018.12.032"},{"key":"e_1_2_12_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2019.100204"},{"key":"e_1_2_12_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2020.e04612"},{"key":"e_1_2_12_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nut.2011.05.018"},{"key":"e_1_2_12_41_1","doi-asserted-by":"publisher","DOI":"10.4103\/0970-9185.130064"},{"key":"e_1_2_12_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bpa.2011.07.006"},{"key":"e_1_2_12_43_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtv.2021.04.002"},{"key":"e_1_2_12_44_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2702.2008.02554.x"},{"key":"e_1_2_12_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcrc.2009.09.002"},{"key":"e_1_2_12_46_1","doi-asserted-by":"publisher","DOI":"10.3906\/sag-1504-139"}],"updated-by":[{"DOI":"10.1111\/iwj.70256","type":"retraction","label":"Retraction","source":"retraction-watch","updated":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"record-id":"62540"},{"DOI":"10.1111\/iwj.70256","type":"retraction","label":"Retraction","source":"publisher","updated":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000}}],"container-title":["International Wound Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/iwj.14275","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T06:23:28Z","timestamp":1754288608000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/iwj.14275"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,13]]},"references-count":45,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["10.1111\/iwj.14275"],"URL":"https:\/\/doi.org\/10.1111\/iwj.14275","archive":["Portico"],"relation":{},"ISSN":["1742-4801","1742-481X"],"issn-type":[{"value":"1742-4801","type":"print"},{"value":"1742-481X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,13]]},"assertion":[{"value":"2023-04-29","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-05-31","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-06-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}