Patents by Inventor Hsin-Yao Wang

Hsin-Yao Wang has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20210217485
    Abstract: A method of establishing a coronary artery disease (CAD) prediction model for CAD screening includes establishing a data set in a computer equipment; entering the data set and corresponding future CAD condition of asymptomatic individuals into a machine learning component; selecting a plurality of robust variables from the clinical data and the cardiovascular markers of the cardiovascular markers panel by using feature selection methods; establishing the CAD prediction model by using machine learning methods; uploading new clinical data and new results of the cardiovascular markers to the cloud-based platform when any asymptomatic individuals undergo the health examination, and performing calculation and analysis by the CAD prediction model; and notifying the asymptomatic individuals of having a high risk of encountering a CAD event or not in a certain period of follow-up time.
    Type: Application
    Filed: April 1, 2021
    Publication date: July 15, 2021
    Applicants: CHANG GUNG MEMORIAL HOSPITAL, LINKOU, Chang Gung University, CATHAY GENERAL HOSPITAL
    Inventors: Jang-Jih Lu, Chun-Hsien Chen, Hsin-Yao Wang, Yi-Hsin Chan, Wei-Shang Shih
  • Patent number: 11011276
    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.
    Type: Grant
    Filed: July 9, 2018
    Date of Patent: May 18, 2021
    Assignees: CHANG GUNG MEMORIAL HOSPITAL, LINKOU, CHANG GUNG UNIVERSITY
    Inventors: Chun-Hsien Chen, Yi-Ju Tseng, Hsin-Yao Wang, Wan-Ying Lin, Chih-Kuang Chen
  • Publication number: 20210080384
    Abstract: A method of creating characteristic profiles of mass spectra and identification model for analyzing and identifying microorganisms includes obtaining data of MALDI-TOF MS of microorganisms having same features; using a kernel density estimation to generate characteristic profiles of an m/z of the data; creating a characteristic MS profile based on the m/z; repeating above three step until characteristic MS profiles of features of the microorganisms is obtained; comparing m/z of MALDITOF MS spectrum of known microorganisms with the characteristic profiles to obtain first matched vectors; using a machine learning method to establish a feature classification model; using MALDI-TOF MS to analyze microorganisms having unknown features; comparing the m/z of MALDI-TOF MS spectrum of the microorganisms having unknown features with the characteristic MS profiles to obtain second matched vectors; using the feature classification model to analyze the second matched vectors; and identifying the microorganisms having the
    Type: Application
    Filed: March 30, 2020
    Publication date: March 18, 2021
    Applicant: Chang Gung University
    Inventors: Jang-Jih Lu, Hsin-Yao Wang, Chia-Ru Chung, Jorng-Tzong Horng, Tzong-Yi Lee
  • Patent number: 10930371
    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.
    Type: Grant
    Filed: July 10, 2017
    Date of Patent: February 23, 2021
    Assignees: CHANG GUNG MEMORIAL HOSPITAL, LINKOU, CHANG GUNG UNIVERSITY
    Inventors: Jang-Jih Lu, Chun-Hsien Chen, Hsin-Yao Wang, Tsui-Ping Liu
  • Publication number: 20210002689
    Abstract: A method for detecting different properties in a microorganism population by ODEP force includes obtaining a microorganism sample solution having a plurality of microorganisms to be tested; pre-processing the microorganism sample solution to obtain a microorganism sample solution to be tested including the microorganisms having electrical properties differences; placing the microorganism sample solution to be tested in a channel of an ODEP device and activating an optical projection device to form at least one optical projection directed to the ODEP device; flowing the microorganism sample solution to be tested from one end of the channel to the other end thereof, and exerting an ODEP force on the optical projection device to generate a force having a direction different from a flowing direction of the microorganism sample solution to be tested; and detecting heterogeneity of the microorganisms based on strength differences of the ODEP force exerted on the respective microorganisms.
    Type: Application
    Filed: February 3, 2020
    Publication date: January 7, 2021
    Applicant: Chang Gung University
    Inventors: Jang-Jih Lu, Min-Hsien Wu, Chih-Yu Chen, Hsin-Yao Wang
  • Publication number: 20200110097
    Abstract: An immunoassay includes forming first mixtures by reacting a combination reagent with serum of a subject; simultaneously forming second mixtures by reacting randomly selected platelet samples with the serum wherein in each mixture there are immunity compounds formed by combining the platelet antigens with predetermined antibodies in the serum, and other platelet antigens and other antibodies in the serum not forming the immunity compounds; preparing an interception device including receptacles and a filter net; placing each mixture in one receptacle; washing the mixtures wherein the mixtures forming the immunity compounds are intercepted by the filter net with others passing through; adding a signal sensing reagent to each receptacle; reacting the signal sensing reagent with the intercepted mixtures forming the immunity compounds to form final products; and performing a signal sensing to determine whether the final products contain anti-platelet antibodies and determine compatibility of cross matching of resp
    Type: Application
    Filed: September 11, 2019
    Publication date: April 9, 2020
    Applicants: Chang Gung University, CHANG GUNG MEMORIAL HOSPITAL, LINKOU
    Inventors: Tzong-Shi Chiueh, Min-Hsien Wu, Hsin-Yao Wang
  • Publication number: 20200013513
    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.
    Type: Application
    Filed: July 9, 2018
    Publication date: January 9, 2020
    Applicants: CHANG GUNG MEMORIAL HOSPITAL, LINKOU, Chang Gung University
    Inventors: Chun-Hsien Chen, Yi-Ju Tseng, Hsin-Yao Wang, Wan-Ying Lin, Chih-Kuang Chen
  • Publication number: 20190264261
    Abstract: A method and a kit for detecting mycobacterium are provided. The method includes steps of: providing a sample; providing a pair of primers, which is selected from a group consisting of a sequence having about 45% to 100% similar to SEQ ID NO. 1, a sequence having about 60% to 100% similar to SEQ ID NO. 2, a sequence complementary thereof; performing a polymerase chain reaction by using the set of primers and the sample to obtain a product; and analyzing the product to detect the presence of the mycobacterium.
    Type: Application
    Filed: February 27, 2019
    Publication date: August 29, 2019
    Inventors: Chih-Cheng Tsou, Min-Hsien Wu, Wen-Pin Chou, Hsin-Yao Wang, Chien-Ru Lin
  • Publication number: 20190221309
    Abstract: A coronary artery disease (CAD) screening method includes 1) collecting clinical information of asymptomatic individuals and testing a plurality of samples of the individuals by using a cardiovascular markers panel including a plurality of cardiovascular markers; 2) entering the clinical information and the test results and the corresponding CAD states of the individuals into a machine learning platform; 3) selecting a plurality of roust variables from the clinical information and the cardiovascular markers of the cardiovascular markers panel by using feature selection methods; 4) using a machine learning algorithm embedded in the machine learning platform to establish a CAD prediction model; and 5) entering clinical information and sample data obtained by using the cardiovascular markers panel for an individual being screened into the CAD prediction model for calculation and analysis, thereby determining whether the individual being screened has CAD or not.
    Type: Application
    Filed: January 15, 2018
    Publication date: July 18, 2019
    Applicants: CHANG GUNG MEMORIAL HOSPITAL, LINKOU, Chang Gung University, CATHAY GENERAL HOSPITAL
    Inventors: Jang-Jih Lu, Chun-Hsien Chen, Hsin-Yao Wang, Yi-Hsin Chan, Wei-Shang Shih
  • Publication number: 20190216368
    Abstract: A method of predicting daily living activities performance of a person with disabilities includes establishing a rehabilitation assessments panel based on a plurality of rehabilitation evaluation scales and laboratory data; evaluating a plurality of persons with disabilities by the rehabilitation assessments panel; entering assessment results and the corresponding activities of daily living (ADL) performance into a machine learning platform; utilizing variable selection methods to select a plurality of variables having optimal classification performance from the rehabilitation assessments panel; executing a machine learning algorithm to create an ADL prediction model based on the selected variables; evaluating a participant in terms of the rehabilitation assessments panel; and entering assessment results into the ADL prediction model for calculation, thereby obtaining a prediction result of future ADL performance for the participant.
    Type: Application
    Filed: January 13, 2018
    Publication date: July 18, 2019
    Applicants: CHANG GUNG MEMORIAL HOSPITAL, LINKOU, Chang Gung University
    Inventors: Chih-Kuang Chen, Chun-Hsien Chen, Hsin-Yao Wang, Wan-Ying Lin
  • Publication number: 20190147136
    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.
    Type: Application
    Filed: November 13, 2017
    Publication date: May 16, 2019
    Applicants: CHANG GUNG MEMORIAL HOSPITAL, LINKOU, Chang Gung University
    Inventors: Jang-Jih Lu, Chung-Chih Hung, Hsin-Yao Wang, Yi-Ju Tseng
  • Publication number: 20190012430
    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.
    Type: Application
    Filed: July 10, 2017
    Publication date: January 10, 2019
    Applicants: CHANG GUNG MEMORIAL HOSPITAL, LINKOU, Chang Gung University
    Inventors: JANG-JIH LU, CHUN-HSIEN CHEN, HSIN-YAO WANG, TSUI-PING LIU
  • Publication number: 20180173847
    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.
    Type: Application
    Filed: December 16, 2016
    Publication date: June 21, 2018
    Inventors: JANG-JIH LU, CHUN-HSIEN CHEN, HSIN-YAO WANG, YING-HAO WEN
  • Patent number: 9161703
    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.
    Type: Grant
    Filed: October 15, 2013
    Date of Patent: October 20, 2015
    Assignee: CHANG GUNG UNIVERSITY
    Inventors: Min-Hsien Wu, Yi-Yuan Chiu, Hsin-Yao Wang, Song-Bin Huang
  • Publication number: 20140128688
    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.
    Type: Application
    Filed: October 17, 2013
    Publication date: May 8, 2014
    Inventors: Min-Hsien Wu, Yi-Yuan Chiu, Hsin-Yao Wang, Song-Bin Huang
  • Publication number: 20140107452
    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.
    Type: Application
    Filed: October 15, 2013
    Publication date: April 17, 2014
    Inventors: Min-Hsien Wu, Yi-Yuan Chiu, Hsin-Yao Wang, Song-Bin Huang