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    Constituent centric architecture for reading comprehension
    1.
    发明授权
    Constituent centric architecture for reading comprehension 有权

    公开(公告)号：US10706234B2

    公开(公告)日：2020-07-07

    申请号：US15948241

    申请日：2018-04-09

    申请人： Petuum Inc.

    发明人： Pengtao Xie ,  Eric Xing

    IPC分类号： G06F40/30 ,  G06N3/04 ,  G06N3/08 ,  G06F16/332 ,  G06N5/04 ,  G06N5/02 ,  G06F40/205 ,  G06F40/216

    摘要： A constituent-centric neural architecture for reading comprehension is disclosed. One embodiment provides a method that performs reading comprehension comprising encoding individual constituents from a text passage using a chain of trees long short-term encoding, encodes question related to the text passage using a tree long short-term memory encoding, generates a question-aware representation for each constituent in the passage using a tree-guided attention mechanism, generates a plurality of candidate answers from the question-aware representation using hierarchical relations among constituents, and predicts an answer to the question in relation to the text passage using a feed-forward network. Other embodiments are disclosed herein.

    Constituent Centric Architecture for Reading Comprehension
    3.
    发明申请
    Constituent Centric Architecture for Reading Comprehension 审中-公开

    公开(公告)号：US20200293721A1

    公开(公告)日：2020-09-17

    申请号：US16886478

    申请日：2020-05-28

    申请人： PETUUM INC.

    发明人： Pengtao Xie ,  Eric Xing

    IPC分类号： G06F40/30 ,  G06N3/04 ,  G06N3/08 ,  G06F16/332 ,  G06N5/04 ,  G06N5/02 ,  G06F40/205 ,  G06F40/216

    摘要： A constituent-centric neural architecture for reading comprehension is disclosed. One embodiment provides a method that performs reading comprehension comprising encoding individual constituents from a text passage using a chain of trees long short-term encoding, encodes question related to the text passage using a tree long short-term memory encoding, generates a question-aware representation for each constituent in the passage using a tree-guided attention mechanism, generates a plurality of candidate answers from the question-aware representation using hierarchical relations among constituents, and predicts an answer to the question in relation to the text passage using a feed-forward network. Other embodiments are disclosed herein.

    Constituent Centric Architecture for Reading Comprehension
    4.
    发明申请
    Constituent Centric Architecture for Reading Comprehension 审中-公开

    公开(公告)号：US20180300314A1

    公开(公告)日：2018-10-18

    申请号：US15948241

    申请日：2018-04-09

    申请人： Petuum Inc.

    发明人： Pengtao Xie ,  Eric Xing

    IPC分类号： G06F17/27 ,  G06F17/30 ,  G06N3/04 ,  G06N3/08

    摘要： A constituent-centric neural architecture for reading comprehension is disclosed. One embodiment provides a method that performs reading comprehension comprising encoding individual constituents from a text passage using a chain of trees long short-term encoding, encodes question related to the text passage using a tree long short-term memory encoding, generates a question-aware representation for each constituent in the passage using a tree-guided attention mechanism, generates a plurality of candidate answers from the question-aware representation using hierarchical relations among constituents, and predicts an answer to the question in relation to the text passage using a feed-forward network. Other embodiments are disclosed herein.

    Method to the automatic International Classification of Diseases (ICD) coding for clinical records
    5.
    发明申请
    Method to the automatic International Classification of Diseases (ICD) coding for clinical records 有权

    公开(公告)号：US20210343410A1

    公开(公告)日：2021-11-04

    申请号：US16865335

    申请日：2020-05-02

    申请人： Petuum Inc.

    发明人： Shanghang Zhang ,  Najmeh Sadoughi ,  Pengtao Xie ,  Eric Xing

    IPC分类号： G16H50/20 ,  G06F40/30 ,  G16H50/70 ,  G16H10/60 ,  G16H70/60 ,  G16H40/20 ,  G06N3/04 ,  G06N3/08 ,  G06F16/22

    摘要： The present invention is a system and a method to classify clinical records into International Classification of Diseases (ICD) codes. The system includes a processor, and a memory communicatively coupled to the processor. The memory includes a generator (G), a feature extractor, a discriminator (D), a label encoder, and a keywords reconstructor. The generator (G) generates synthesized features corresponding to ICD code descriptions. The feature extractor extracts real latent features from clinical documents and generates real features by training a GANs. The generator (G) generates synthesized features after the GANs are trained and calibrate a binary code classifier with the real latent features generated by the feature extractor for a low-shot ICD code l. The feature extractor generates code-specific latent features conditioned on a textual description of each ICD code description by using a WGAN-GP. The discriminator (D) distinguishes between the synthesized features and the real features and determines whether the features are the real features or synthetic features. The label encoder encodes a sequence of keywords in the ICD code description into a sequence of hidden states.

    Systems and Methods for Predicting Medications to Prescribe to a Patient Based on Machine Learning
    7.
    发明申请
    Systems and Methods for Predicting Medications to Prescribe to a Patient Based on Machine Learning 审中-公开

    公开(公告)号：US20200027539A1

    公开(公告)日：2020-01-23

    申请号：US16207114

    申请日：2018-12-01

    申请人： Petuum Inc.

    发明人： Pengtao Xie ,  Eric Xing

    IPC分类号： G16H20/10 ,  G16H10/60 ,  G06N20/00

    摘要： A system for predicting medications to prescribe to a patient includes a text encoding module and a medication prediction module. The text encoding module is configured to obtain a clinical-information vector from clinical information of the patient. The medication prediction module configured to apply a machine-learned medication-prediction algorithm to the clinical-information vector to select a subset of medications to prescribe to the patient. The machine-learned medication-prediction algorithm is designed with a diversity-promoting regularization model, and is configured to simultaneously consider correlations among different medications and dependencies between patient information and medications when selecting a subset of medications to prescribe to the patient.

    SYSTEMS AND METHODS FOR IDENTIFYING ATHEROMATOUS PLAQUES IN MEDICAL IMAGES
    10.
    发明申请
    SYSTEMS AND METHODS FOR IDENTIFYING ATHEROMATOUS PLAQUES IN MEDICAL IMAGES 有权

    公开(公告)号：US20210192717A1

    公开(公告)日：2021-06-24

    申请号：US16719695

    申请日：2019-12-18

    申请人： Petuum Inc.

    发明人： Najmeh Sadoughi ,  Suhaila Mumtaj Shakiah ,  Pengtao Xie ,  Eric Xing

    IPC分类号： G06T7/00 ,  A61B3/10 ,  G06T7/11

    摘要： The current disclosure is directed towards providing systems and methods for identifying atheromatous plaques in optical coherence tomography (OCT) images. In one example, a method for a trained neural network may include acquiring an OCT image slice of an artery, identifying one or more image features of the OCT image slice with the trained neural network, and responsive to the one or more image features indicating a thin-cap fibroatheroma (TCFA), segmenting the OCT image slice into a plurality of regions with the trained neural network, the plurality of regions including a first region depicting the TCFA, and determining start and end coordinates for the TCFA based on the first region.