Chen, Chun-YouChun-YouChenChang, Te-ITe-IChangChen, Cheng-HsienCheng-HsienChenHsu, Shih-ChangShih-ChangHsuChu, Yen-LingYen-LingChuHuang, Nai-JenNai-JenHuangSue, Yuh-MouYuh-MouSueChen, Tso-HsiaoTso-HsiaoChenLin, Feng-YenFeng-YenLinShih, Chun-MingChun-MingShihHuang, Po-HsunPo-HsunHuangHUI-LING HSIEHLiu, Chung-TeChung-TeLiu2026-04-222026-04-222025-11https://scholars.lib.ntu.edu.tw/handle/123456789/737445Computerized diagnostic algorithms could achieve early detection of acute kidney injury (AKI) only with available baseline serum creatinine (SCr). To tackle this weakness, we tried to construct a machine learning model for AKI diagnosis based on point-of-care clinical features regardless of baseline SCr. Patients with SCr > 1.3 mg/dL were recruited retrospectively from Wan Fang Hospital, Taipei. A Dataset A ( = 2846) was used as the training dataset and a Dataset B ( = 1331) was used as the testing dataset. Point-of-care features, including laboratory data and physical readings, were inputted into machine learning models. The repeated machine learning models randomly used 70% and 30% of Dataset A as training dataset and testing dataset for 1000 rounds, respectively. The single machine learning models used Dataset A as training dataset and Dataset B as testing dataset. A computerized algorithm for AKI diagnosis based on 1.5× increase in SCr and clinician's AKI diagnosis compared to machine learning models. On an independent, unbalanced test set ( = 1331), our machine learning models achieved AUROC values ranging from 0.67 to 0.74. A pre-existing computerized algorithm performed best (AUROC = 0.94). Crucially, all machine learning models significantly outperformed the routine clinician's diagnosis (AUROC ~0.74 vs. 0.53, < 0.05). For context, a pre-existing computerized algorithm, which requires available baseline SCr data, achieved an AUROC of 0.94 on a relevant subset of the data, highlighting the performance benchmark when baseline data is available. Formal statistical comparisons revealed that the top-performing models (e.g., Random Forest, SVM) were often statistically indistinguishable. Model performance was highly dependent on the test scenario, with precision and F1 scores improving markedly on a balanced dataset. In the absence of baseline SCr, machine learning models can diagnose AKI with significantly greater accuracy than routine clinical diagnoses. Our robust statistical analysis suggests that several advanced algorithms achieve a similarly high level of performance.enacute kidney injury (AKI)artificial intelligence (AI)chronic kidney disease (CKD)creatinineelectronic alertshospitalizationintensive care units (ICU)machine learningjournal article10.3390/diagnostics1521280141226092