Healing profiles in patients with a chronic diabetic foot ulcer: An exploratory study with machine learning.
Diabetic foot ulcers (DFU) are one of the most frequent and debilitating complications of diabetes. DFU wound healing is a highly complex process, resulting in significant medical, economic and social challenges. Therefore, early identification of patients with a high‐risk profile would be important...
| Publicado en: | Wound Repair & Regeneration Vol. 31; no. 6; pp. 793 - 804 |
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| Autores principales: | , , , , , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Nov/Dec2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=174563086&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174563086 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10671927 DPV jtl: Wound Repair & Regeneration issn: 10671927 maglogo: Y pubinfo: dt: Nov/Dec2023 vid: 31 iid: 6 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 174563086 174563086 174563086 10.1111/wrr.13141 174563086 ppf: 793 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Healing profiles in patients with a chronic diabetic foot ulcer: An exploratory study with machine learning. aug: au: Pereira, M. Graça Vilaça, Margarida Braga, Diogo Madureira, Ana Da Silva, Jéssica Santos, Diana Carvalho, Eugénia affil: Psychology Research Center (CIPsi), School of Psychology, University of Minho, Braga, Portugal sug: subj: Wound Healing Evaluation Diabetic Patients Psychosocial Factors Diabetic Foot Therapy Diabetic Foot Prognosis Diabetes Mellitus Complications Machine Learning Utilization Human Male Female Exploratory Research Decision Trees Descriptive Statistics MicroRNA Attitude to Illness Sociodemographic Factors Prediction Models Funding Source Male Female ab: Diabetic foot ulcers (DFU) are one of the most frequent and debilitating complications of diabetes. DFU wound healing is a highly complex process, resulting in significant medical, economic and social challenges. Therefore, early identification of patients with a high‐risk profile would be important to adequate treatment and more successful health outcomes. This study explores risk assessment profiles for DFU healing and healing prognosis, using machine learning predictive approaches and decision tree algorithms. Patients were evaluated at baseline (T0; N = 158) and 2 months later (T1; N = 108) on sociodemographic, clinical, biochemical and psychological variables. The performance evaluation of the models comprised F1‐score, accuracy, precision and recall. Only profiles with F1‐score >0.7 were selected for analysis. According to the two profiles generated for DFU healing, the most important predictive factors were illness representations on T1 IPQ‐B (IPQ‐B ≤ 9.5 and < 10.5) and the DFU duration (≤ 13 weeks). The two predictive models for DFU healing prognosis suggest that biochemical factors are the best predictors of a favorable healing prognosis, namely IL‐6, microRNA‐146a‐5p and PECAM‐1 at T0 and angiopoietin‐2 at T1. Illness perception at T0 (IPQ‐B ≤ 39.5) also emerged as a relevant predictor for healing prognosis. The results emphasize the importance of DFU duration, illness perception and biochemical markers as predictors of healing in chronic DFUs. Future research is needed to confirm and test the obtained predictive models. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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