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Fig. 6 | Journal of Translational Medicine

Fig. 6

From: An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation

Fig. 6

A demo prediction of patient No.104 by online GBM-based predictor of post-LT AKI. A demo prediction of patient No. 104 made by the online GBM-based predictor of post-LT AKI is shown. To increase clinical applicability, intraoperative average urine output and time of anesthesia were substituted by direct input of weight, total urine output and the time of initiation and terminal of anesthesia. The prediction output for patient No. 104 was “0” with a probability of 97%, that is, the probability of this patient developing post-LT AKI was merely 3%

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