从人工神经网络中的系统性红斑狼疮的不平衡胎儿结局中学习。
Learning from imbalanced fetal outcomes of systemic lupus erythematosus in artificial neural networks.
机构信息
Department of Obstetrics and Gynecology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
出版信息
BMC Med Inform Decis Mak. 2021 Apr 13;21(1):127. doi: 10.1186/s12911-021-01486-x.
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.
目的
探索一种基于人工神经网络的有效算法，以便从大多数活产孕妇中正确挑选出少数患有 SLE 并发生胎儿丢失结局的孕妇，并训练出一个表现良好的模型作为临床决策辅助工具。
方法
我们将对比研究和聚焦研究的思想融入人工神经网络中，提出了一种针对小数据集不平衡学习的有效算法。
结果
我们收集了 469 名非平凡的 SLE 孕妇，其中 420 名孕妇活产，49 名孕妇发生胎儿丢失。经过充分训练的不平衡学习模型对识别胎儿丢失结局的患者具有很高的敏感性，敏感性为 19/21 ([公式：见正文])。
讨论
两名患者的错误预测是可以解释的。人工神经网络框架中的算法改进增强了不平衡学习问题中的识别能力，外部验证提高了算法的可靠性。
结论
经过充分训练的模型完全有资格帮助医疗保健提供者做出及时、准确的决策。
Zotero 插件