Learning from imbalanced fetal outcomes of systemic lupus erythematosus in artificial neural networks.
Objective: To explore an effective algorithm based on artificial neural network to pick correctly the minority of pregnant women with SLE suffering fetal loss outcomes from the majority with live birth and train a well behaved model as a clinical decision assistant.Methods: We integrated the thought...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 21; no. 1; pp. 1 - 12 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
4/13/2021
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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=149786362&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149786362 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 4/13/2021 vid: 21 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 149786362 149786362 NLM33845834 149786362 10.1186/s12911-021-01486-x NLM33845834 149786362 ppf: 1 ppct: 11 formats: tig: atl: Learning from imbalanced fetal outcomes of systemic lupus erythematosus in artificial neural networks. aug: au: Ma, Jing-Hang Feng, Zhen Wu, Jia-Yue Zhang, Yu Di, Wen affil: Department of Obstetrics and Gynecology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China sug: subj: Lupus Erythematosus, Systemic Pregnancy Complications Pregnancy Human Reproducibility of Results Female Prenatal Care Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales Female ab: Objective: To explore an effective algorithm based on artificial neural network to pick correctly the minority of pregnant women with SLE suffering fetal loss outcomes from the majority with live birth and train a well behaved model as a clinical decision assistant.Methods: We integrated the thoughts of comparative and focused study into the artificial neural network and presented an effective algorithm aiming at imbalanced learning in small dataset.Results: We collected 469 non-trivial pregnant patients with SLE, where 420 had live-birth outcomes and the other 49 patients ended in fetal loss. A well trained imbalanced-learning model had a high sensitivity of 19/21 ([Formula: see text]) for the identification of patients with fetal loss outcomes.Discussion: The misprediction of the two patients was explainable. Algorithm improvements in artificial neural network framework enhanced the identification in imbalanced learning problems and the external validation increased the reliability of algorithm.Conclusion: The well-trained model was fully qualified to assist healthcare providers to make timely and accurate decisions. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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