Developing a Siamese Network for UTIs Risk Prediction in Immobile Patients Undergoing Stroke...18th World Congress of Medical and Health Informatics, MedInfo 2021 - One World, One Health – Global Partnership for Digital Innovation, 2-4 October, 2021.
Stroke patients tend to suffer from immobility, which increases the possibility of post-stroke complications. Urinary tract infections (UTIs) are one of the complications as an independent predictor of poor prognosis of stroke patients. However, the incidence of new UTIs onsets during hospitalizatio...
| Publicado en: | Studies in Health Technology & Informatics Vol. 290; pp. 714 - 719 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2022
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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=157572046&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157572046 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2022 vid: 290 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 157572046 157572046 157572046 10.3233/SHTI220171 157572046 ppf: 714 ppct: 5 formats: tig: atl: Developing a Siamese Network for UTIs Risk Prediction in Immobile Patients Undergoing Stroke...18th World Congress of Medical and Health Informatics, MedInfo 2021 - One World, One Health – Global Partnership for Digital Innovation, 2-4 October, 2021. aug: au: Zidu Xu Chen Zhu Si Zheng Xiangyu Sun Jing Cao Xinjuan Wu Jiao Li affil: Institute of Medical Information / Library, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China sug: subj: Urinary Tract Infections Risk Factors Risk Assessment Stroke Patients Immobility Stroke Complications Prediction Models Program Development Neural Networks (Computer) Utilization Congresses and Conferences Comparative Studies Machine Learning Chinese Persons Qualitative Studies Safety Quality Improvement Descriptive Statistics Logistic Regression Human Sensitivity and Specificity Length of Stay ab: Stroke patients tend to suffer from immobility, which increases the possibility of post-stroke complications. Urinary tract infections (UTIs) are one of the complications as an independent predictor of poor prognosis of stroke patients. However, the incidence of new UTIs onsets during hospitalization was rare in most datasets with a prevalence of 4%. This imbalanced data distribution sets obstacles to establishing an accurate prediction model. Our study aimed to develop an effective prediction model to identify UTIs risk in immobile stroke patients, and (2) to compare its prediction performance with traditional machine learning models. We tackled this problem by building a Siamese Network leveraging commonly used clinical features to identifying patients with UTIs risk. Model derivation and validation were based on a nationwide dataset including 3982 Chinese patients. Results showed that the Siamese Network performed better than traditional machine learning models in imbalanced datasets (Sensitivity: 0.810; AUC: 0.828). pubtype: Academic Journal doctype: equations & formulas proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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