Joy H Wu

20230083916, Mar 16, 2023

Sep 13, 2021

17/472787

- Armonk NY, US
Joy Tzung-yu Wu - San Jose CA, US
Ashutosh Jadhav - San Jose CA, US

G06F 16/2458
G06F 16/248
G16H 10/60
G06F 16/901

Mechanisms are provided to implement a visual analytics pipeline. The mechanisms generate, from an input database of records, a chronology-aware graph data structure of a plurality of records based features specified in an ontology data structure. The chronology-aware graph data structure has vertices representing one or more of events or records based features corresponding to events, and edges representing chronological relationships between events. The mechanisms execute a chronology-aware graph query on the chronology-aware graph data structure to generate a filtered set of vertices and corresponding features corresponding to criteria of the chronology-aware graph query. The mechanisms execute a pattern discovery operation on the filtered set of vertices and corresponding features to identify a subset of vertices and corresponding features that correspond to a relatively higher frequency set of patterns of event paths, and generate a visual analytics graphical representation for the subset of vertices and corresponding features.

20210166150, Jun 3, 2021

Dec 2, 2019

16/700305

- Armonk NY, US
Joy Tzung-yu Wu - San Jose CA, US

G06N 20/00
G06N 3/08
G06K 9/62
G06K 9/72
G06T 7/10

Embodiments of the present disclosure include a computer-implemented method, a system, and a computer program product for integrating bottom-up segmentation techniques into a semi-supervised image segmentation machine learning model. The computer implemented method includes training a machine learning model with a labeled dataset. The labeled dataset includes ground truth segmentation labels for each sample in the labeled dataset. The computer implemented method also includes generating a pseudo labeled dataset by applying an unlabeled dataset to the machine learning model using a top-down segmentation grouping rule. The computer implemented method further includes evaluating the pseudo labeled dataset using a bottom-up segmentation grouping rule to produce evaluation results, combining the pseudo labeled dataset with the second pseudo labeled dataset into a training dataset, and then retraining the machine learning model with the training dataset.

20210166822, Jun 3, 2021

Dec 2, 2019

16/700137

- Armonk NY, US
Joy Tzung-yu Wu - San Jose CA, US

G16H 50/70
G06N 20/00
G06N 5/04
G16H 30/40
G16H 15/00
G16H 70/00
G06K 9/62

Systems and methods for developing a classification model for classifying medical reports, such as radiology reports. One method includes selecting, from a corpus of reports, a training set and a testing set, assigning labels of a modality and an anatomical focus to the reports in both sets, and extracting a sparse representation matrix for each set based on features in the training set. The method also includes learning, with one or more electronic processors, a correlation between the features of the training set and the corresponding labels using a machine learning classifier, thereby building a classification model and testing the classification model on the reports in the testing set for accuracy using the sparse representation matrix of the testing set. The method further includes predicting, with the classification model, labels of an anatomical focus and a modality for remaining reports in the corpus not included in the sets.

20210106286, Apr 15, 2021

Oct 11, 2019

16/599824

- Armonk NY, US
Hongzhi Wang - San Jose CA, US
Joy Tzung-yu Wu - San Jose CA, US
Chun Lok Wong - San Jose CA, US

A61B 5/00
G06T 7/10
G06K 9/68
G06N 3/08

A system for automated detection and type classification of central venous catheters. The system includes an electronic processor that is configured to, based on an image, generate a segmentation of a potential central venous catheter using a segmentation method and extract, from the segmentation, one or more image features associated with the potential central venous catheter. The electronic processor is also configured to, based on the one or more image features, determine, using a first classifier, whether the image includes a central venous catheters and determine, using a second classifier, a type of central venous catheter included in the image.

20200321101, Oct 8, 2020

Apr 8, 2019

16/377944

- Armonk NY, US
Chun Lok Wong - San Jose CA, US
Joy Wu - San Jose CA, US
Mehdi Moradi - San Jose CA, US

G16H 30/40
G06N 20/00
G06K 9/62
G06T 7/00
G06F 17/24
G16H 15/00

Methods and systems are directed to training an artificial intelligence engine. One system includes an electronic processor configured obtain a set of reports corresponding to a set of medical images, determine a label for a finding of interest, and identify one or more ambiguous reports in the set of repots. Ambiguous reports do not include a positive label or a negative label for the finding of interest. The electronic processor is also configured to generate an annotation for each of the one or more ambiguous reports in the set of reports, and train the artificial intelligence engine using a training set including the annotation for each of the one or more ambiguous reports and non-ambiguous reports in the set of reports. A result of the training is generation of a classification model for the label for the finding of interest.

20200258215, Aug 13, 2020

Feb 11, 2019

16/272652

- Armonk NY, US
Joy Wu - Mountain View CA, US
Mehdi Moradi - San Jose CA, US

G06T 7/00
G06K 9/62
G06N 3/08
G06N 20/00
G16H 30/00

Methods and systems for determining a diagnostically unacceptable medical image. One system includes at least one electronic processor configured to receive a new medical image captured via a medical imaging device. The at least one electronic processor is also configured to determine a classification of the new medical image using a model developed with machine learning using training information that includes a plurality of medical images and an associated classification for each medical image, each associated classification identifying whether the associated medical image is diagnostically unacceptable, wherein the classification of the new medical image indicates whether the new medical image is diagnostically unacceptable. The at least one electronic processor is also configured to, when the classification indicates that the new medical image is diagnostically unacceptable, prompt a user of the medical imaging device to adjust a parameter associated with the new medical image and recapture the new medical image.

20190198137, Jun 27, 2019

Dec 26, 2017

15/854136

- Armonk NY, US
Marina Bendersky - San Jose CA, US
Ashutosh Jadhav - Santa Clara CA, US
Karina Kanjaria - San Jose CA, US
Chaitanya Shivade - San Jose CA, US
Joy Wu - Mountain View CA, US

G16H 10/60
G06Q 50/22
G16H 15/00
G06N 5/02

Mechanisms are provided to implement a medical information summarization engine (MISE). The MISE receives input specifying a summarization template, wherein the summarization template specifies terms or concepts of interest to a medical professional when making a medical decision regarding a patient. The MISE maps the terms or concepts of interest to medical concepts in a medical knowledge base. The MISE processes electronic medical records (EMR) of the patient based on the mapping of the medical concepts in the medical knowledge base to the terms or concepts of interest in the summarization template to extract patient information from the patient EMR that matches at least one of the medical concepts from the mapping. The MIE generates and outputs a holistic summary of the patient's EMRs that summarizes the most salient portions of the patient EMR for use by the medical professional in making the medical decision regarding the patient.

20190198138, Jun 27, 2019

Dec 26, 2017

15/854179

- Armonk NY, US
Ashutosh Jadhav - Santa Clara CA, US
Chaitanya Shivade - San Jose CA, US
Joy Wu - Mountain View CA, US

G16H 10/60
G06F 17/30

Mechanisms are provided to implement a medical information summarization engine (MISE). The MISE receives input specifying a summarization template, wherein the summarization template specifies terms or concepts of interest to a medical professional when making a medical decision regarding a patient. The MISE expands the summarization template based on related concepts or related terms related to the terms or concepts of interest specified in the summarization template. The MISE processes an EMR of the patient based on the expanded summarization template to extract patient information corresponding to the terms or concepts of interest and the related concepts or related terms. The MISE generates and outputs a holistic summary of the EMR of the patient that summarizes the most salient portions of the patient EMR for use by the medical professional in making the medical decision regarding the patient, based on extracted patient information obtained from processing the patient EMR.