公开(公告)号：US11263223B2

公开(公告)日：2022-03-01

申请号：US16168129

申请日：2018-10-23

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Jian Min Jiang ,  En Liang Xu ,  Bibo Hao ,  Eryu Xia ,  Jing Li ,  Ke Wang

IPC: G06F16/2457 ,  G06N5/02 ,  G06N20/00 ,  G06F16/28

Abstract: Methods and systems for using machine learning to determine electronic document similarity include extracting entities and corresponding relationships from each of two electronic documents of a corpus of electronic documents based on word embedding, computing an entity distance between the extracted entities and a relationship distance between the extracted relationships based on knowledge graph embedding, combining the entity and relationship distances to generate a similarity score between the electronic documents, and implementing the similarity score to perform a task associated with the electronic documents.

公开(公告)号：US20210157850A1

公开(公告)日：2021-05-27

申请号：US16691907

申请日：2019-11-22

Applicant: International Business Machines Corporation

Inventor： Sui Jun Tong ,  WEN SUN ,  YI QIN YU ,  Eryu Xia ,  Yong Qin

IPC: G06F16/901 ,  G06F16/28 ,  G06F16/903

Abstract: Techniques for augmenting relational databases with graph database capabilities are described. A graph database query requesting data from a graph database is received. The graph database includes a plurality of vertices and a plurality of edges. The graph database query is translated into a relational database query using one or more computer processors. The relational database query references a vertex table and an edge table in a relational database. Result data corresponding with the graph database query is retrieved from the relational database by executing the relational database query against the relational database.

公开(公告)号：US10881463B2

公开(公告)日：2021-01-05

申请号：US15690436

申请日：2017-08-30

Applicant: International Business Machines Corporation

Inventor： Jing Mei ,  Shi Wan Zhao ,  Gang Hu ,  Jing Li ,  Eryu Xia ,  En Liang Xu

IPC: G06N3/08 ,  G16H50/50 ,  G06N3/04 ,  G06N3/00 ,  G16H50/20 ,  A61B34/10 ,  G06F19/00 ,  G06F16/903

Abstract: Patient treatment may be optimized using Recurrent Neural Network (RNN) based state simulation and Reinforcement learning (RL) techniques to simulate future states and actions. A RNN state simulator and a RL action generator may be trained using patient data such as historical states and actions. The RL action generator may be optimized by applying the RNN state simulator to simulating future states and applying the RL action generator to generate recommended actions based on the simulated future states. This process may be iteratively performed until a computational convergence is reached by the RL action generator which may indicate that the RL action generator has been optimized. A patient state may be fed into the optimized RL action generator to generate an optimal recommended treatment action.

公开(公告)号：US11587647B2

公开(公告)日：2023-02-21

申请号：US16542938

申请日：2019-08-16

Applicant: International Business Machines Corporation

Inventor： Bibo Hao ,  Shouyu Yan ,  Eryu Xia ,  Shi Lei Zhang

IPC: G16H10/20 ,  G16H50/30 ,  G16H50/20 ,  G16H10/60

Abstract: Techniques for candidate evaluation and filtering are provided. Enrollment criteria for a clinical trial are received, where the enrollment criteria include a plurality of conditions. A plurality of cost vectors is constructed for the plurality of conditions. A set of values for one or more of the plurality of conditions is determined for a candidate, where the set of values does not include a value for at least a first condition of the plurality of conditions. A utilized cost is generated for the candidate, based on the first set of values and the plurality of cost vectors. The candidate is then ranked based on the utilized cost.

公开(公告)号：US20190065687A1

公开(公告)日：2019-02-28

申请号：US15690436

申请日：2017-08-30

Applicant: International Business Machines Corporation

Inventor： Jing Mei ,  Shi Wan Zhao ,  Gang Hu ,  Jing Li ,  Eryu Xia ,  En Liang Xu

IPC: G06F19/00 ,  G06N3/08 ,  G06F17/30

Abstract: Patient treatment may be optimized using Recurrent Neural Network (RNN) based state simulation and Reinforcement learning (RL) techniques to simulate future states and actions. A RNN state simulator and a RL action generator may be trained using patient data such as historical states and actions. The RL action generator may be optimized by applying the RNN state simulator to simulating future states and applying the RL action generator to generate recommended actions based on the simulated future states. This process may be iteratively performed until a computational convergence is reached by the RL action generator which may indicate that the RL action generator has been optimized. A patient state may be fed into the optimized RL action generator to generate an optimal recommended treatment action.

公开(公告)号：US20190059998A1

公开(公告)日：2019-02-28

申请号：US15820237

申请日：2017-11-21

Applicant: International Business Machines Corporation

Inventor： Jing Mei ,  Shi Wan Zhao ,  Gang Hu ,  Jing Li ,  Eryu Xia ,  En Liang Xu

IPC: G06N3/08 ,  G06F17/30 ,  G06F19/00

Abstract: Patient treatment may be optimized using Recurrent Neural Network (RNN) based state simulation and Reinforcement learning (RL) techniques to simulate future states and actions. A RNN state simulator and a RL action generator may be trained using patient data such as historical states and actions. The RL action generator may be optimized by applying the RNN state simulator to simulating future states and applying the RL action generator to generate recommended actions based on the simulated future states. This process may be iteratively performed until a computational convergence is reached by the RL action generator which may indicate that the RL action generator has been optimized. A patient state may be fed into the optimized RL action generator to generate an optimal recommended treatment action.

公开(公告)号：US11380443B2

公开(公告)日：2022-07-05

申请号：US16144327

申请日：2018-09-27

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Jing Mei ,  Chia Yeow Khiang ,  Roslyn Hickson ,  Eryu Xia ,  Shiwan Zhao

IPC: G16H50/50 ,  G16H50/70 ,  G06Q50/00 ,  G06N20/00 ,  G06F16/901

Abstract: A computer-implemented method for predicting non-communicable diseases with infectious risk factors using artificial intelligence includes detecting one or more risk factors associated with a non-communicable disease based on a graph associated with person-to-person links, generating a data structure for compactly representing the graph to compute at least one person-to-person distance, and performing a machine learning technique with regularization of the at least one person-to-person distance.

公开(公告)号：US11120014B2

公开(公告)日：2021-09-14

申请号：US16199023

申请日：2018-11-23

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Yi Qin Yu ,  En Liang Xu ,  Shi Lei Zhang ,  Bibo Hao ,  Eryu Xia

IPC: G06F16/242 ,  G06F16/28 ,  G06F16/901 ,  G06F16/2457

Abstract: A computer-implemented method, system, and computer program product are provided for enhanced search strategies. The method includes selecting, by a processor device, known candidate sources related to a search topic. The method also includes ranking, by the processor device, keyphrase candidates from the known candidate sources according to inter-topic weighting. The method additionally includes assembling, by the processor device, a search string of a predetermined number of top ranked keyphrase candidates. The method further includes generating, by the processor device, new candidate sources from a candidate source repository responsive to the search string. The method also includes defining, by the processor device, a candidate source pool by the known candidate sources and the new candidate sources to reduce user search times on computer interface devices.

公开(公告)号：US20200210621A1

公开(公告)日：2020-07-02

申请号：US16238216

申请日：2019-01-02

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Sui Jun Tong ,  Wen Sun ,  Yi Qin Yu ,  Eryu Xia ,  Yong Qin

IPC: G06F21/62 ,  G06F21/60 ,  G06F16/28 ,  G06N20/00

Abstract: A system for decentralized privacy-preserving clinical data evaluation includes a plurality of sites of a decentralized private network, a memory device for storing program code, and at least one processor device operatively coupled to the memory device and configured to execute program code stored on the memory device to, for each of the local datasets, evaluate the local dataset using each of the local models to obtain one or more features related to a degree of outlierness, determine at least one outlier dataset based on the one or more features, and implement one or more actions based on the determination.

公开(公告)号：US11281801B2

公开(公告)日：2022-03-22

申请号：US16238216

申请日：2019-01-02

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Sui Jun Tong ,  Wen Sun ,  Yi Qin Yu ,  Eryu Xia ,  Yong Qin

IPC: H04L29/06 ,  G06F21/62 ,  G06F21/60 ,  G06N20/00 ,  G06F16/28

Abstract: A system for decentralized privacy-preserving clinical data evaluation includes a plurality of sites of a decentralized private network, a memory device for storing program code, and at least one processor device operatively coupled to the memory device and configured to execute program code stored on the memory device to, for each of the local datasets, evaluate the local dataset using each of the local models to obtain one or more features related to a degree of outlierness, determine at least one outlier dataset based on the one or more features, and implement one or more actions based on the determination.