Abstract: An acute kidney injury predicting system and a method thereof are proposed. A processor reads the data to be tested, the detection data, the machine learning algorithm and the risk probability comparison table from a main memory. The processor trains the detection data according to the machine learning algorithm to generate an acute kidney injury prediction model, and inputs the data to be tested into the acute kidney injury prediction model to generate an acute kidney injury characteristic risk probability and a data sequence table. The data sequence table lists the data to be tested in sequence according to a proportion of each of the data to be tested in the acute kidney injury characteristics. The processor selects one of the medical treatment data from the risk probability comparison table according to the acute kidney injury characteristic risk probability.

Abstract: A method may include forming a mask layer on top of a first dielectric layer formed on a first source/drain and a second source/drain, and creating an opening in the mask layer and the first dielectric layer that exposes portions of the first source/drain and the second source/drain. The method may include filling the opening with a metal layer that covers the exposed portions of the first source/drain and the second source/drain, and forming a gap in the metal layer to create a first metal contact and a second metal contact. The first metal contact may electrically couple to the first source/drain and the second metal contact may electrically couple to the second source/drain. The gap may separate the first metal contact from the second metal contact by less than nineteen nanometers.

Abstract: An acute kidney injury predicting system and a method thereof are proposed. A processor reads the data to be tested, the detection data, the machine learning algorithm and the risk probability comparison table from a main memory. The processor trains the detection data according to the machine learning algorithm to generate an acute kidney injury prediction model, and inputs the data to be tested into the acute kidney injury prediction model to generate an acute kidney injury characteristic risk probability and a data sequence table. The data sequence table lists the data to be tested in sequence according to a proportion of each of the data to be tested in the acute kidney injury characteristics. The processor selects one of the medical treatment data from the risk probability comparison table according to the acute kidney injury characteristic risk probability.