Patents by Inventor JANG-JIH LU

JANG-JIH LU 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
  • 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: 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: 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