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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

Model 2 included 11 variables that were used in the classification system developed by Park et al [9], including age, sex, emergency operation, operation duration, diabetes, ACEi or ARB usage, blood levels of albumin, hemoglobin, sodium, e GFR, and urine dipstick protein. In this model, light GBM (AUC=0.81) and DNN (AUC=0.8) showed the highest performance.

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

Analysis of Metabolic and Quality-of-Life Factors in Patients With Cancer for a New Approach to Classifying Walking Habits: Secondary Analysis of a Randomized Controlled Trial

Analysis of Metabolic and Quality-of-Life Factors in Patients With Cancer for a New Approach to Classifying Walking Habits: Secondary Analysis of a Randomized Controlled Trial

Demographic characteristics of participants. a The exchange rate calculation is based on the initial data analysis period of June-July 2023, and US $1 was calculated as 1300 won. Table 3 presents the impact of walking activity on body measurements, blood sugar levels, and body composition for 2 groups: the AWG and IWG. HDL cholesterol and triglyceride levels showed statistically significant differences between baseline and 6 months for both the AWG and the IWG.

Yae Won Tak, Junetae Kim, Haekwon Chung, Sae Byul Lee, In Ja Park, Sei Won Lee, Min-Woo Jo, Jong Won Lee, Seunghee Baek, Yura Lee

J Med Internet Res 2025;27:e52694