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Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study

Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study

Neonatal factors included gestational age, birth weight, PMA at initial ROP treatment, fetal distress, sex, small for gestational age, Apgar scores (1 and 5 min), multiple births, asphyxia, sepsis, respiratory distress syndrome, bronchopulmonary dysplasia, pneumonia, intraventricular hemorrhage, necrotizing enterocolitis, hypoxic-ischemic encephalopathy, atrial septal defect, patent foramen ovale, patent ductus arteriosus, and hyperbilirubinemia.

Rong Wu, Yu Zhang, Peijie Huang, Yiying Xie, Jianxun Wang, Shuangyong Wang, Qiuxia Lin, Yichen Bai, Songfu Feng, Nian Cai, Xiaohe Lu

J Med Internet Res 2025;27:e60367

A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation

A Risk Prediction Model (CMC-AKIX) for Postoperative Acute Kidney Injury Using Machine Learning: Algorithm Development and Validation

Nonbinary data were one-hot encoded, a method for rearranging categorical data into binary variables, and numerical data were normalized using min-max scaling. This would convert all numeric values between or equal to a value of 0 and 1. Min-max scaling is given by: One-hot encoding, min-max scaling, and dataset splitting were accomplished using the Scikit-Learn library (version 0.24.2) [24]. These steps are required to improve the performance of machine learning models and training stability.

Ji Won Min, Jae-Hong Min, Se-Hyun Chang, Byung Ha Chung, Eun Sil Koh, Young Soo Kim, Hyung Wook Kim, Tae Hyun Ban, Seok Joon Shin, In Young Choi, Hye Eun Yoon

J Med Internet Res 2025;27:e62853