公开(公告)号：US20250095642A1

公开(公告)日：2025-03-20

申请号：US18468593

申请日：2023-09-15

Applicant: GE Precision Healthcare LLC

Inventor： Soumya Ghose ,  Sanand Sasidharan ,  Sanghee Cho ,  Akshit Achara ,  Rakesh Mullick ,  Anuradha Kanamarlapudi ,  Fiona Ginty ,  Annamraju Ravi Bhardwaj ,  Sundararajan Mani ,  Brion Sarachan

IPC: G10L15/197 ,  G06N3/0455 ,  G06N3/09 ,  G10L15/06 ,  G10L15/16 ,  G10L15/22 ,  G16H10/60 ,  G16H50/20 ,  G16H70/20

Abstract: The current disclosure provides methods for an automated clinical recommendation system that generates clinical recommendations for patients of a health care system based on natural language queries submitted by care providers of the health care system. The natural language queries may be typed into a user interface (UI) of the clinical recommendation system, or submitted by voice via a microphone. The clinical recommendation system provides a clinically explainable disease state of a patient based on patient data included in a query, and recommends a next course of action (e.g., a treatment) based on clinical guidelines and population statistics, in a manner that reduces a current burden of clinicians in consulting digital clinical manuals via a series of time-consuming and cumbersome interactions with a graphical user interface (GUI) of the digital clinical manuals.

公开(公告)号：US20230309836A1

公开(公告)日：2023-10-05

申请号：US17981913

申请日：2022-11-07

Applicant: GE Precision Healthcare LLC

Inventor： Souyma Ghose ,  Zhanpan Zhang ,  Sanghee Cho ,  Fiona Ginty ,  Cynthia Elizabeth Landberg Davis ,  Jhimli Mitra ,  Sunil S. Badve ,  Yesim Gokmen-Polar ,  Elizabeth Mary McDonough

IPC: A61B5/00 ,  G06K9/62 ,  G06T7/00 ,  G16H50/20

CPC classification number: A61B5/0091 ,  A61B5/004 ,  A61B5/4312 ,  A61B5/7275 ,  A61B5/742 ,  G06K9/6223 ,  G06V2201/032 ,  G16H50/20 ,  A61B2576/02 ,  G06T2207/10116 ,  G06T2207/30068 ,  G06T2207/30096 ,  G06T7/0012

Abstract: A method for determining a recurrence of a disease in a patient is presented. The method includes generating a plurality of medical images of an organ of the patient and determining a plurality of recurrence probabilities from the plurality of medical images. A recurrence of the disease is determined based on the plurality of recurrence probabilities and clinicopathological data of the patient using a Bayesian network.

公开(公告)号：US20230307137A1

公开(公告)日：2023-09-28

申请号：US17704531

申请日：2022-03-25

Applicant: GE Precision Healthcare LLC ,  The Trustees of Indiana University

Inventor： Soumya Ghose ,  Fiona Ginty ,  Cynthia Elizabeth Landberg Davis ,  Sanghee Cho ,  Sunil S. Badve ,  Yesim Gokmen-Polar

IPC: G16H50/30 ,  G16H30/40 ,  G06T7/00 ,  G06V10/26

CPC classification number: G16H50/30 ,  G16H30/40 ,  G06T7/0012 ,  G06V10/26 ,  G06T2207/30096

Abstract: A method for determining a recurrence of a disease in a patient includes generating a medical image of an organ of the patient and then extracting an invasive edge around an area of interest in the medical image. A plurality of radiomics features is obtained from the invasive edge and the recurrence of the disease is determined based on the plurality of radiomics features.

公开(公告)号：US20250166827A1

公开(公告)日：2025-05-22

申请号：US18949548

申请日：2024-11-15

Applicant: GE Precision Healthcare LLC

Inventor： Soumya Ghose ,  Sanghee Cho ,  Fiona Mary Ginty ,  Sriram Boppana ,  SM Shajedul Hasan ,  Erhan Bas ,  Thiruvarul Selvan Senthivel

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

Abstract: The described technology is generally directed towards automated and dynamic creation of a recommended treatment for a patient's medical condition. A potential treatment can be generated by application of patient data to a multimodal model representing radiomic data and pathomic data integrated with transcriptomic data. The potential treatment can be applied to a probability model to determine a risk score for the potential treatment. The probability model comprises a collection of treatments and medical conditions, in conjunction with respective measures of success of application of a respective treatment to a respective condition. The multimodal model can comprise a network of nodes and edges, wherein the nodes represent knowledge of a patient condition, medical knowledge/medical research regarding the respective patient conditions, and the like. An output of the multimodal model is one or more recommended treatments for the medical condition.

公开(公告)号：US20230317293A1

公开(公告)日：2023-10-05

申请号：US17710326

申请日：2022-03-31

Applicant: GE Precision Healthcare LLC ,  The Trustees of Indiana University

Inventor： Sanghee Cho ,  Zhanpan Zhang ,  Soumya Ghose ,  Fiona Ginty ,  Cynthia Elizabeth Landberg Davis ,  Jhimli Mitra ,  Sunil S. Badve ,  Yesim Gokmen-Polar

IPC: G16H50/30 ,  G16H30/00 ,  G06T7/00 ,  G06N3/04

CPC classification number: G16H50/30 ,  G16H30/00 ,  G06T7/0014 ,  G06N3/0454 ,  G06T2207/30068

Abstract: A method for determining a recurrence of a disease in a patient is presented. The method includes generating a plurality of medical images of an organ of the patient and determining a plurality of recurrence probabilities from the plurality of medical images. A recurrence of the disease is determined based on the plurality of recurrence probabilities and clinicopathological data of the patient using a Bayesian network.