| Machine learning-based prediction models for pressure injury: A systematic review and meta-analysis | |
| Pei, Juhong1; Guo, Xiaojing2; Tao, Hongxia1; Wei, Yuting2; Zhang, Hongyan3; Ma, Yuxia2; Han, Lin1,3,4 | |
| 2023-06-20 | |
| Online publication date | 2023-06 |
| Source Publication | International Wound Journal Impact Factor & Quartile |
| ISSN | 1742-4801 |
| page numbers | 12 |
| Abstract | Despite the fact that machine learning (ML) algorithms to construct predictive models for pressure injury development are widely reported, the performance of the model remains unknown. The goal of the review was to systematically appraise the performance of ML models in predicting pressure injury. PubMed, Embase, Cochrane Library, Web of Science, CINAHL, Grey literature and other databases were systematically searched. Original journal papers were included which met the inclusion criteria. The methodological quality was assessed independently by two reviewers using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed with Metadisc software, with the area under the receiver operating characteristic curve, sensitivity and specificity as effect measures. Chi-squared and I-2 tests were used to assess the heterogeneity. A total of 18 studies were included for the narrative review, and 14 of them were eligible for meta-analysis. The models achieved excellent pooled AUC of 0.94, sensitivity of 0.79 (95% CI [0.78-0.80]) and specificity of 0.87 (95% CI [0.88-0.87]). Meta-regressions did not provide evidence that model performance varied by data or model types. The present findings indicate that ML models show an outstanding performance in predicting pressure injury. However, good-quality studies should be conducted to verify our results and confirm the clinical value of ML in pressure injury development. |
| Keyword | machine learning algorithm meta-analysis predictive modelling pressure injury |
| Publisher | WILEY |
| DOI | 10.1111/iwj.14280 |
| Indexed By | SCIE |
| Language | 英语 |
| WOS Research Area | Dermatology ; Surgery |
| WOS Subject | Dermatology ; Surgery |
| WOS ID | WOS:001011554000001 |
| Original Document Type | Review ; Early Access |
| PMID | 3734052 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | https://ir.lzu.edu.cn/handle/262010/530023 |
| Collection | 兰州大学 |
| Corresponding Author | Han, Lin |
| Affiliation | 1.Lanzhou Univ, Clin Med Coll 1, Sch Nursing, Lanzhou, Peoples R China; 2.Lanzhou Univ, Sch Nursing, Lanzhou, Peoples R China; 3.Gansu Prov Hosp, Dept Nursing, Lanzhou, Peoples R China; 4.Lanzhou Univ, Clin Med Coll 1, 204 Donggang Rd, Lanzhou, Gansu, Peoples R China |
| Recommended Citation GB/T 7714 | Pei, Juhong,Guo, Xiaojing,Tao, Hongxia,et al. Machine learning-based prediction models for pressure injury: A systematic review and meta-analysis[J]. International Wound Journal,2023. |
| APA | Pei, Juhong.,Guo, Xiaojing.,Tao, Hongxia.,Wei, Yuting.,Zhang, Hongyan.,...&Han, Lin.(2023).Machine learning-based prediction models for pressure injury: A systematic review and meta-analysis.International Wound Journal. |
| MLA | Pei, Juhong,et al."Machine learning-based prediction models for pressure injury: A systematic review and meta-analysis".International Wound Journal (2023). |
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