Jing-Hang Ma1,2,3,4, Zhen Feng4, Jia-Yue Wu1,2,3, Yu Zhang2,3, Wen Di5,6,7. 1. Department of Obstetrics and Gynecology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. 2. Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. 3. State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. 4. First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China. 5. Department of Obstetrics and Gynecology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. diwen163@163.com. 6. Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. diwen163@163.com. 7. State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. diwen163@163.com.
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 ([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.
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.
Authors: Eliza F Chakravarty; Iris Colón; Elizabeth S Langen; David A Nix; Yasser Y El-Sayed; Mark C Genovese; Maurice L Druzin Journal: Am J Obstet Gynecol Date: 2005-06 Impact factor: 8.661
Authors: Scott M Lundberg; Bala Nair; Monica S Vavilala; Mayumi Horibe; Michael J Eisses; Trevor Adams; David E Liston; Daniel King-Wai Low; Shu-Fang Newman; Jerry Kim; Su-In Lee Journal: Nat Biomed Eng Date: 2018-10-10 Impact factor: 25.671