Patents by Inventor Jianying Hu

Jianying Hu 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: 20210233662
    Abstract: Systems, computer-implemented methods, and computer program products to facilitate employing a probabilistic model to generate a continuous disease status index based on observational data are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a model component that employs a probabilistic model to generate probability distributions of disease states of a disease of an entity based on observational data of the entity. The computer executable components can further comprise an index component that generates a disease status index of the disease based on the probability distributions of the disease states.
    Type: Application
    Filed: January 24, 2020
    Publication date: July 29, 2021
    Inventors: Zhaonan Sun, Liuyi Yao, Zijun Yao, Jianying Hu
  • Patent number: 11075008
    Abstract: Various embodiments predict drug-disease associations. In one embodiment, a plurality of disease similarity matrices and a plurality of disease similarity matrices are accessed. Each of the plurality of drug similarity matrices is associated with a different drug information source. Each of the plurality of disease similarity matrices is associated with a different disease information source. A known drug-disease association matrix is also accessed. The known drug-disease association matrix indicates if a given drug identified is known to treat a given disease. At least one drug-disease association prediction is generated based on the plurality of drug similarity matrices, the plurality of disease similarity matrices, and the known drug-disease association matrix. The at least one drug-disease association prediction identifies a previously unknown association between a given drug and a given disease, and a probability that the given disease is treatable by the given drug.
    Type: Grant
    Filed: June 25, 2015
    Date of Patent: July 27, 2021
    Assignee: International Business Machines Corporation
    Inventors: Jianying Hu, Fei Wang, Ping Zhang
  • Patent number: 11056218
    Abstract: Aspects of the present invention include a method, system and computer program product. The method includes identifying, by a processor, a set of global risk factors for a target event using training patients, and providing, by the processor, a disease progression timeline with defined time stamps by aligning longitudinal data of the training patients based on the defined time stamp of risk targets. The method also includes positioning, by the processor, a target patient at one of the defined time stamps on the disease progression timeline, and identifying, by the processor, at least one of the training patients similar to the target patient with the same one of the defined time stamps on the disease progression timeline. The method further includes calculating, by the processor, a time-varying predictive pattern of at least a portion of the global set of risk factors for the target patient along the disease progression timeline.
    Type: Grant
    Filed: May 31, 2016
    Date of Patent: July 6, 2021
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Jianying Hu, Kenney Ng, Adam Perer, Fei Wang, Yajuan Wang
  • Patent number: 11037684
    Abstract: Various embodiments predict drug-disease associations. In one embodiment, a plurality of disease similarity matrices and a plurality of disease similarity matrices are accessed. Each of the plurality of drug similarity matrices is associated with a different drug information source. Each of the plurality of disease similarity matrices is associated with a different disease information source. A known drug-disease association matrix is also accessed. The known drug-disease association matrix indicates if a given drug identified is known to treat a given disease. At least one drug-disease association prediction is generated based on the plurality of drug similarity matrices, the plurality of disease similarity matrices, and the known drug-disease association matrix. The at least one drug-disease association prediction identifies a previously unknown association between a given drug and a given disease, and a probability that the given disease is treatable by the given drug.
    Type: Grant
    Filed: November 14, 2014
    Date of Patent: June 15, 2021
    Assignee: International Business Machines Corporation
    Inventors: Jianying Hu, Fei Wang, Ping Zhang
  • Patent number: 11037656
    Abstract: Predicting beneficial drug combinations mitigating adverse drug reactions identifies drug combinations and associated target adverse drug reaction from a spontaneous reporting system containing case reports of drugs and associated adverse drug reactions. Each drug combination comprises a first drug and a second drug, and a propensity score is computed for each drug in each group. This propensity score expresses a probability of being exposed to a given drug based on other co-prescribed drugs and reported indications, which reflect patient characteristics. Associations are computed for each drug as well as drug interaction. Among the associations, the sum of the associations of the second drug and the interaction effect represents the predicted beneficial score expressing whether the second drug alters the chance of developing the target adverse drug reaction for patients on the first drug.
    Type: Grant
    Filed: October 16, 2019
    Date of Patent: June 15, 2021
    Assignee: International Business Machines Corporation
    Inventors: Jianying Hu, Ying Li, Zhaonan Sun, Ping Zhang
  • Patent number: 10978208
    Abstract: A system and method for patient stratification include determining a first set of patient groups from patients in a patient similarity graph based on a similarity structure of the patient similarity graph. A second set of patient groups is identified based on expert domain knowledge associated with the patients. Patients in the first set and the second set are aligned using a processor to stratify patients.
    Type: Grant
    Filed: December 5, 2013
    Date of Patent: April 13, 2021
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Jianying Hu, Buyue Qian, Fei Wang, Jun Wang, Xiang Wang
  • Patent number: 10839936
    Abstract: An embodiment of the invention receives input including a list of drugs, drug characteristics of each drug, and known drug-disease associations including a disease and a drug having a threshold efficacy for treating the disease. For each drug in the list of drugs, a processor predicts whether the drug meets a threshold efficacy for treating a first disease based on the drug characteristics and the drug-disease associations. For each drug in the list of drugs, the processor predicts whether the drug meets a threshold efficacy for treating a second disease based on the drug characteristics and the predicting of whether the drug meets the threshold efficacy for treating the first disease. Output is generated output based on the predictions, the output including an identified drug-disease association, an identified disease-disease association, an identified chemical fingerprint for the first disease, and an identified chemical fingerprint for the second disease.
    Type: Grant
    Filed: November 2, 2015
    Date of Patent: November 17, 2020
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Jianying Hu, Zhaonan Sun, Fei Wang, Ping Zhang
  • Patent number: 10832821
    Abstract: A system and method for providing a temporally dynamic model parameter include building a model parameter by minimizing a loss function based on patient measurements taken at a plurality of time points. Temporally related values of the model parameter are identified, using a processor, having a same type of patient measurement taken at different time points. At least one value of the model parameter and temporally related values of the at least one value are selected to provide a temporally dynamic model parameter.
    Type: Grant
    Filed: August 19, 2013
    Date of Patent: November 10, 2020
    Assignee: International Business Machines Corporation
    Inventors: Shahram Ebadollahi, Jianying Hu, Jimeng Sun, Fei Wang, Jiayu Zhou
  • Patent number: 10803144
    Abstract: A processor-implemented method, computer program product and system are provided for predicting drug-drug interactions based on clinical side effects. The method includes constructing a drug-drug interactions training dataset that includes pharmaceutical, pharmacokinetic or pharmacodynamics drug-drug interactions from multiple data sources for each of a plurality of drugs. The method also includes constructing side effect features for each of the drugs from side effects associated with the drugs. The method further includes building, using the drug-drug interactions training dataset, a drug-drug interactions classifier that predicts adverse drug-drug interactions for drug pairs derivable from the drugs.
    Type: Grant
    Filed: May 6, 2014
    Date of Patent: October 13, 2020
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Jianying Hu, Robert K. Sorrentino, Fei Wang, Ping Zhang
  • Patent number: 10796237
    Abstract: Examples of techniques for patient-level analytics with sequential pattern mining are provided. In one example implementation according to aspects of the present description, a computer-implemented method includes: constructing a patient record; transforming, by a processing system, the patient record into a bitmap representation; and analyzing, by the processing system, the bitmap to identify a sequential pattern within the patient record on a per patient basis.
    Type: Grant
    Filed: June 28, 2016
    Date of Patent: October 6, 2020
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Jianying Hu, Kun Lin
  • Patent number: 10713264
    Abstract: Embodiments include methods, systems and computer program products for reducing feature space in analysis of medical data. Aspects include receiving patient temporal traces. Aspects also include conducting sequential pattern mining on the patient temporal traces to produce sequential features. Aspects also include clustering the sequential features with a similarity metric. Aspects also include analyzing the clustered features to predict a healthcare outcome.
    Type: Grant
    Filed: August 25, 2016
    Date of Patent: July 14, 2020
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Jianying Hu, Kun Lin, Gigi Y. Yuen-Reed, Ping Zhang
  • Patent number: 10692588
    Abstract: A system and method for analyzing chemical data including a processor and one or more classifiers, stored in memory and coupled to the processor, which further includes an indication predictive module configured to predict whether a given chemical treats a particular indication or not and a side effect predictive module configured to predict whether a given chemical causes a side-effect or not. A correlation engine is configured to determine one or more correlations between one or more indications and one or more side effects for the given chemical and a visualization tool is configured to analyze the one or more correlations and to output results of the analysis.
    Type: Grant
    Filed: October 7, 2016
    Date of Patent: June 23, 2020
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Nan Cao, Jianying Hu, Robert K. Sorrentino, Fei Wang, Ping Zhang
  • Patent number: 10685741
    Abstract: A system and method for analyzing chemical data including a processor and one or more classifiers, stored in memory and coupled to the processor, which further includes an indication predictive module configured to predict whether a given chemical treats a particular indication or not and a side effect predictive module configured to predict whether a given chemical causes a side-effect or not. A correlation engine is configured to determine one or more correlations between one or more indications and one or more side effects for the given chemical and a visualization tool is configured to analyze the one or more correlations and to output results of the analysis.
    Type: Grant
    Filed: October 7, 2016
    Date of Patent: June 16, 2020
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Nan Cao, Jianying Hu, Robert K. Sorrentino, Fei Wang, Ping Zhang
  • Publication number: 20200051670
    Abstract: Predicting beneficial drug combinations mitigating adverse drug reactions identifies drug combinations and associated target adverse drug reaction from a spontaneous reporting system containing case reports of drugs and associated adverse drug reactions. Each drug combination comprises a first drug and a second drug, and a propensity score is computed for each drug in each group. This propensity score expresses a probability of being exposed to a given drug based on other co-prescribed drugs and reported indications, which reflect patient characteristics. Associations are computed for each drug as well as drug interaction. Among the associations, the sum of the associations of the second drug and the interaction effect represents the predicted beneficial score expressing whether the second drug alters the chance of developing the target adverse drug reaction for patients on the first drug.
    Type: Application
    Filed: October 16, 2019
    Publication date: February 13, 2020
    Inventors: Jianying HU, Ying LI, Zhaonan SUN, Ping ZHANG
  • Patent number: 10535424
    Abstract: An apparatus, method and computer program product for proactive comprehensive generic risk screening. The method performs proactive comprehensive generic risk screening by implementing steps of training comprising steps of receiving cross domain risks and features, optimizing linkage regularization using the received features and the received cross domain risks, said linkage regularization comprising multi-task predictive model training, feature selection and ranking, risk association learning and risk association selection, and outputting patient risk scores, identified high risk patients, risk factors for risks and risk groups, and risk groups and risk associations and calculating risk score for an individual patient comprising steps of receiving individual features comprising patient information, performing said linkage regularization using the received individual features and outputting patient risk scores for said individual patient, and high risk for said individual patient.
    Type: Grant
    Filed: February 19, 2016
    Date of Patent: January 14, 2020
    Assignee: International Business Machines Corporation
    Inventors: Jianying Hu, Zhaonan Sun, Fei Wang
  • Publication number: 20190384889
    Abstract: Techniques are described that facilitate determining potential cancer gene therapy targets by joint modeling of cancer survival events. In one embodiment, a computer-implemented comprises employing, by a device operatively coupled to a processor, a multi-task learning model to determine active genetic factors respectively associated with different types of cancer based on cancer survival data and patient genomic data for groups of patients that respectively survived the different types of cancer. The computer-implemented method further comprises, determining, by the device, common active genetic factors of the active genetic factors that are shared between two or more types of cancer of the different types of cancer.
    Type: Application
    Filed: June 18, 2018
    Publication date: December 19, 2019
    Inventors: Zhaonan Sun, Zach Shahn, Ping Zhang, Fei Wang, Jianying Hu
  • Patent number: 10490301
    Abstract: Predicting beneficial drug combinations mitigating adverse drug reactions identifies drug combinations and associated target adverse drug reaction from a spontaneous reporting system containing case reports of drugs and associated adverse drug reactions. Each drug combination comprises a first drug and a second drug, and a propensity score is computed for each drug in each group. This propensity score expresses a probability of being exposed to a given drug based on other co-prescribed drugs and reported indications, which reflect patient characteristics. Associations are computed for each drug as well as drug interaction. Among the associations, the sum of the associations of the second drug and the interaction effect represents the predicted beneficial score expressing whether the second drug alters the chance of developing the target adverse drug reaction for patients on the first drug.
    Type: Grant
    Filed: September 6, 2016
    Date of Patent: November 26, 2019
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Jianying Hu, Ying Li, Zhaonan Sun, Ping Zhang
  • Patent number: 10452961
    Abstract: In one embodiment, a computer-implemented method includes transforming a plurality of electronic health records into a plurality of temporal graphs indicating an order in which events observed in the plurality of electronic health records occur and learning a temporal pattern from the plurality of temporal graphs, wherein the temporal pattern indicates an order of events that is observed to occur repeatedly across the plurality of temporal graphs.
    Type: Grant
    Filed: October 23, 2015
    Date of Patent: October 22, 2019
    Assignee: International Business Machines Corporation
    Inventors: Jianying Hu, Yajuan Wang, Fei Wang, Chuanren Liu
  • Publication number: 20190130070
    Abstract: A system (or method) for generation and employment of disease progression model(s) that facilitates identifying and indexing discriminative features for disease progression in observational data. The disease progression prediction system comprises a processor that executes computer executable components stored in memory. A receiving component receives and learns observational patient data. A model generation component builds a preliminary disease progression model. An identification component identifies discriminative clinical features for different disease stages. A ranking component ranks discriminative powers of clinical features for respective pairs of disease stages; wherein the model generation component employs the ranked features to generate a final disease progression model.
    Type: Application
    Filed: October 31, 2017
    Publication date: May 2, 2019
    Inventors: Yu Cheng, Soumya Ghosh, Jianying Hu, Ying Li, Zhaonan Sun
  • Publication number: 20190095584
    Abstract: Embodiments include methods, systems, and computer program products for generating a mechanism of action hypothesis. Aspects include receiving a drug candidate data along with a plurality of predicted adverse drug reactions (ADRs) associated with the drug candidate data. Aspects include receiving a drug pathway data for the drug candidate and adverse drug reaction pathway data for each of the plurality of predicted adverse drug reactions. Aspects include building a pathway network, wherein the pathway network includes a plurality of drug pathway nodes, a plurality of ADR pathway nodes, and a plurality of pathway connections. Aspects also include generating a pathway output.
    Type: Application
    Filed: September 26, 2017
    Publication date: March 28, 2019
    Inventors: Jianying HU, Heng LUO, Janu VERMA, Ping ZHANG