Abstract: An electronic device including a substrate, a first electrode layer, a photodiode, an insulating layer, a second electrode layer, and a first transparent conductive layer is provided. The first electrode layer is disposed on the substrate. The photodiode is disposed on the first electrode layer and is electrically connected to the first electrode layer. The insulating layer is disposed on the photodiode. The second electrode layer is disposed on the insulating layer and is electrically connected to the photodiode. The first transparent conductive layer is disposed on the insulating layer and contacts the second electrode layer. A manufacturing method of an electronic device is also provided.

Abstract: A method of establishing a clinical decision support system for SPC risk evaluation among patients with colorectal cancer includes combining cancer characteristics into a characteristic assembly of SPC risk evaluation; obtaining clinical data of first participants to establish a database of SPC risk evaluation; entering the database into a machine learning algorithm; using the machine learning algorithms to establish a SPC risk evaluation model; using a characteristic interpreter to analyze the model; calculating a risk value of each cancer characteristic; presenting the risk values in graphics to establish a clinical decision support system; obtaining clinical data of second participants and inputting same into the clinical decision support system; using the machine learning algorithm for comparison and analysis; predicting risk for SPC; calculating a risk value of each cancer characteristic; presenting the risk values on the clinical decision support system; giving suggestions of decreasing risk; and monito

Abstract: A method for establishing a computer-aided data interpretation model for immune diseases by immunomarkers and visualization is revealed. First combine a plurality of immunomarkers into an immunomarker panel. Then collect test data of a plurality of subjects measured by the immunomarker panel, and disease diagnosis information of the subjects for establishment of an immunomarker-panel testing database. Next new subjects are tested by the immunomarker panel. The data obtained and the corresponding information in the immunomarker-panel testing database are processed by unsupervised machine learning algorithm to get a computer-aided data interpretation model showing comparison of case distribution patterns. The method provides real-time analysis of multiple data to medical professionals for their reference. Thereby the correctness, the timeliness and the reproducibility of the interpretation result for the diagnosis and treatment of immune diseases are all improved.

Abstract: A method for establishing a computer-aided data interpretation model for immune diseases by immunomarkers and visualization is revealed. First combine a plurality of immunomarkers into an immunomarker panel. Then collect test data of a plurality of subjects measured by the immunomarker panel, and disease diagnosis information of the subjects for establishment of an immunomarker-panel testing database. Next new subjects are tested by the immunomarker panel. The data obtained and the corresponding information in the immunomarker-panel testing database are processed by unsupervised machine learning algorithm to get a computer-aided data interpretation model showing comparison of case distribution patterns. The method provides real-time analysis of multiple data to medical professionals for their reference. Thereby the correctness, the timeliness and the reproducibility of the interpretation result for the diagnosis and treatment of immune diseases are all improved.

Abstract: A method of using machine learning algorithms in analyzing laboratory test results of body fluid to detect microbes in the body fluid includes using a body fluid detection module for analytic measurements in body fluid of a person to create biological samples; sending the biological samples of a plurality of persons and corresponding microbes infection statuses to perform machine learning algorithms to establish a microbes in body fluid prediction model; and sending data obtained from the body fluid detection of a patient for testing to the microbes in body fluid prediction model for operation and analysis in order to determine whether the microbes is present in body fluid.