Diagnosing growing pains in children by using machine learning: a cross-sectional multicenter study.
Growing pains (GP) are the most common cause of recurrent musculoskeletal pain in children. There are no diagnostic criteria for GP. We aimed at analyzing GP-related characteristics and assisting GP diagnosis by using machine learning (ML). Children with GP and diseased controls were enrolled betwee...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 60; no. 12; pp. 3601 - 3615 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Dec2022
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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=160112206&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160112206 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Dec2022 vid: 60 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 160112206 160112206 NLM36264529 160112206 10.1007/s11517-022-02699-6 NLM36264529 160112206 ppf: 3601 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Diagnosing growing pains in children by using machine learning: a cross-sectional multicenter study. aug: au: Akal, Fuat Batu, Ezgi D. Sonmez, Hafize Emine Karadağ, Şerife G. Demir, Ferhat Ayaz, Nuray Aktay Sözeri, Betül affil: Department of Computer Engineering, Hacettepe University, Ankara, Turkey sug: subj: Lower Extremity Pain Etiology Pain Diagnosis Human Prospective Studies Cross Sectional Studies Child Comparative Studies Multicenter Studies Evaluation Research Validation Studies Arthritis Impact Measurement Scales Child: 6-12 years ab: Growing pains (GP) are the most common cause of recurrent musculoskeletal pain in children. There are no diagnostic criteria for GP. We aimed at analyzing GP-related characteristics and assisting GP diagnosis by using machine learning (ML). Children with GP and diseased controls were enrolled between February and August 2019. ML models were developed by using tenfold cross-validation to classify GP patients. A total of 398 patients with GP (F/M:1.3; median age 102 months) and 254 patients with other diseases causing limb pain were enrolled. The pain was bilateral (86.2%), localized in the lower extremities (89.7%), nocturnal (74%), and led to awakening at night (60.8%) in most GP patients. History of arthritis, trauma, morning stiffness, limping, limitation of activities, and school abstinence were more prevalent among controls than in GP patients (p = 0.016 for trauma; p < 0.001 for others). The experiments with different ML models revealed that the Random Forest algorithm had the best performance with 0.98 accuracy, 0.99 sensitivity, and 0.97 specificity for GP diagnosis. This is the largest cohort study of children with GP and the first study that attempts to diagnose GP by using ML techniques. Our ML model may be used to facilitate diagnosing GP. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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