ArticleBMC medical informatics and decision making2021
Learning from imbalanced fetal outcomes of systemic lupus erythematosus in artificial neural networks.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Review
- Article
- Prediction of low Apgar score at five minutes following labor induction intervention in vaginal deliveries: machine learning approach for imbalanced data at a tertiary hospital in North Tanzania.BMC pregnancy and childbirth · 2022Article
- Computer-aided diagnosis of cervical dysplasia using colposcopic images.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo 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.
methodsWe 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.
resultsWe 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.
conclusionThe well-trained model was fully qualified to assist healthcare providers to make timely and accurate decisions.
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