ArticleBiology2022
Classification Model for Diabetic Foot, Necrotizing Fasciitis, and Osteomyelitis.
Article in Biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Explainable machine learning differentiates necrotizing fasciitis and osteomyelitis via routine blood biomarkers.NPJ digital medicine · 2026Article
- Explainable machine learning for differential diagnosis of diabetic foot infection and osteomyelitis: a two-center study and clinically applicable web calculator using routine blood biomarkers.BMC medical informatics and decision making · 2025Article
- Application of Antimicrobial Peptides (AMPs) in Treatment of Osteomyelitis in Human and Veterinary Orthopedics.Journal of functional biomaterials · 2025Review
- Rare Breast Emergency: A Case of Necrotizing Fasciitis of the Breast in a Lactating Patient.European journal of breast health · 2024Article
- A transformer-based deep learning model for identifying the occurrence of acute hematogenous osteomyelitis and predicting blood culture results.Frontiers in microbiology · 2024Article
- Classification Model for Epileptic Seizure Using Simple Postictal Laboratory Indices.Journal of clinical medicine · 2023Article
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Authors and funding
6 authors.
Funding
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Abstract
Diabetic foot ulcers (DFUs) and their life-threatening complications, such as necrotizing fasciitis (NF) and osteomyelitis (OM), increase the healthcare cost, morbidity and mortality in patients with diabetes mellitus. While the early recognition of these complications could improve the clinical outcome of diabetic patients, it is not straightforward to achieve in the usual clinical settings. In this study, we proposed a classification model for diabetic foot, NF and OM. To select features for the classification model, multidisciplinary teams were organized and data were collected based on a literature search and automatic platform. A dataset of 1581 patients (728 diabetic foot, 76 NF, and 777 OM) was divided into training and validation datasets at a ratio of 7:3 to be analyzed. The final prediction models based on training dataset exhibited areas under the receiver operating curve (AUC) of the 0.80 and 0.73 for NF model and OM model, respectively, in validation sets. In conclusion, our classification models for NF and OM showed remarkable discriminatory power and easy applicability in patients with DFU.
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