Learning from imbalanced fetal outcomes of systemic lupus erythematosus in artificial neural networks
BMC medical informatics and decision making(2021)
Abstract
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 (90.8%) 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.
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Key words
Systemic lupus erythematosus,Imbalanced data,Fetal outcome,Artificial neural networks,Clinical decision assistant
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