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.

Abstract: A method of creating characteristic peak profiles of mass spectra and identification model for analyzing and identifying microorganisms are provided. MALDI-TOF MS data of microorganisms having the same feature are gathered. Discretization of the data is performed. Density-based clustering is used to find m/z values of spectral peaks with high probability of occurrence from the discretized data. A characteristic MS peak profile is created for every specific feature of microorganisms. Every such a characteristic profile forms a feature template. The mass spectrum of each known isolate is matched against all the feature templates and a number of matched vectors are obtained. The matched vectors are then concatenated into a single “integrated vector.” Then, a machine learning method and the integrated vectors generated from all known isolates are used to create a classification model for microorganism identification.

Abstract: A method of establishing a machine learning model for cancer anticipation includes collecting test results of a plurality of tumor markers of a plurality of eligible individuals and corresponding conditions of cancer; performing a variable selection process on the collected data to select a plurality of robust variables; and using the selected variables, numerals, and conditions of cancer by cooperating with a machine learning method to establish a cancer anticipation model. A method of detecting cancer by using a plurality of tumor markers in a machine learning model for cancer anticipation is also provided.

Abstract: An integrated bioinformatics sensing apparatus includes a piezoelectric sensing layer, an upper conductive layer, a bottom conductive layer and an information transmission controller. The piezoelectric sensing layer senses a physiological rhythm of a living organism to output a physiological rhythm signal, and the upper and bottom conductive layers sense a physiological electrical signal on a body surface of the living organism. The information transmission controller receives and processes the physiological rhythm signal and the physiological electrical signal to generate and store the sensed bioinformatics, or transmit the signals to the external processing device to display the sensed bioinformatics. The simple-structured sensing apparatus can be attached onto the body surface of the living organism conveniently.

Abstract: A physiological electrical signal and living organism movement signal sensing apparatus includes at least one electrode element, a piezoelectric sensing layer, a connecting layer and a control unit. The connecting layer is connected to the at least one electrode element, and the electrode element measures a physiological electrical signal of a living organism to generate a physiological sensing signal and the piezoelectric sensing layer measures a living organism movement signal to generate a living organism movement sensing signal, and the control unit receives the physiological sensing signal and the living organism movement sensing signal to determine and display the physiological status and movement of the living organism. The sensing apparatus has the features of providing highly integrated functions and simple structure.

Abstract: An integrated bioinformatics sensing apparatus includes a piezoelectric sensing layer, an upper conductive layer, a bottom conductive layer and an information transmission controller. The piezoelectric sensing layer senses a physiological rhythm of a living organism to output a physiological rhythm signal, and the upper and bottom conductive layers sense a physiological electrical signal on a body surface of the living organism, and the information transmission controller receives and processes the physiological rhythm signal and the physiological electrical signal to generate and store the sensed bioinformatics, or transmit the signals to the external processing device to display the sensed bioinformatics. The simple-structured sensing apparatus can be attached onto the body surface of the living organism conveniently.