公开(公告)号：US11475302B2

公开(公告)日：2022-10-18

申请号：US16840856

申请日：2020-04-06

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Stephanie Lanius ,  Erina Ghosh ,  Emma Holdrich Schwager ,  Larry James Eshelman

IPC: G06N3/08 ,  G06N3/04 ,  G06N20/00 ,  A61B5/20 ,  G06K9/62 ,  G16H50/30

Abstract: A method for training a baseline risk model, including: pre-processing input data by normalizing continuous variable inputs and producing one-hot input features for categorical variables; providing definitions for clean input data and dirty input data based upon various input data related to a patient condition; segmenting the input data into clean input data and dirty input data, wherein the clean input data includes a first subset and a second subset, where the first subset and the second subset include all of the clean input data and are disjoint; training a machine learning model using the first subset of the clean data; and evaluating the performance of the trained machine learning model using the second subset of the clean input data and the dirty input data.

公开(公告)号：US20190147993A1

公开(公告)日：2019-05-16

申请号：US16300271

申请日：2017-05-03

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Erina Ghosh ,  Oladimeji Feyisetan Farri

IPC: G16H15/00 ,  G16H10/60 ,  G06N20/00 ,  G16H50/70

Abstract: Instructions (108) cause a processor (104) to: classify a clinical report for a subject under evaluation by one of anatomical organ or disease; identify and retrieve clinical reports for the same subject from the healthcare data source(s); group the retrieved clinical report by one of anatomical organ or disease; select a group of the clinical report, wherein the group includes reports for a same or related one of the anatomical organ or the disease; build a model that predicts semantic relationships between nodes in the reports in the selected group of reports based on one or more of extracted parameters or keywords; compare one of the parameter values or the keywords across the reports using the model; construct a graphical timeline of the reports; highlight differences in the parameter values or the keywords based on a result of the compare; and visually present the graphical timeline with the highlighted differences.

公开(公告)号：USD840434S1

公开(公告)日：2019-02-12

申请号：US29616384

申请日：2017-09-06

Applicant: KONINKLIJKE PHILIPS N.V.

Designer： Christopher Edward Haverstock ,  Erina Ghosh

公开(公告)号：US20240127951A1

公开(公告)日：2024-04-18

申请号：US18275637

申请日：2022-01-29

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Erina Ghosh ,  Larry Eshelman ,  Stephanie Lanius ,  Emma Holdrich Schwager ,  Kianoush Kashani

IPC: G16H50/20

CPC classification number: G16H50/20

Abstract: A method (100) for determining a baseline creatinine value for a subject, comprising: obtaining (130) a set of features about the subject; analyzing (140), using a trained baseline creatinine determination model, the obtained set of features to generate a baseline creatinine value for the subject; reporting (150), via a user interface, the generated baseline creatinine value for the subject.

公开(公告)号：US11836447B2

公开(公告)日：2023-12-05

申请号：US16846605

申请日：2020-04-13

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Erina Ghosh ,  Stephanie Lanius ,  Emma Holdrich Schwager ,  Larry James Eshelman

IPC: G06K9/62 ,  G06N20/00 ,  G06F40/216 ,  G06F18/214 ,  G06F18/24

CPC classification number: G06F40/216 ,  G06F18/214 ,  G06F18/24765 ,  G06N20/00

Abstract: A machine learning model, including: a categorical input feature, having a defined set of values; a plurality of non-categorical input features; a word embedding layer configured to convert the categorical input feature into an output in a word space having two dimensions; and a machine learning network configured to receive the output of the word embedding layer and the plurality of non-categorical input features and to produce a machine learning model output.

公开(公告)号：US11605467B2

公开(公告)日：2023-03-14

申请号：US16475794

申请日：2018-01-03

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Eric Thomas Carlson ,  Erina Ghosh ,  Mohammad Shahed Sorower ,  David Paul Noren ,  Bo Liu

IPC: G16H50/70 ,  G16H10/60 ,  G16H50/20 ,  G06K9/62

Abstract: A method (100) for training a scoring system (600) comprising the steps of: (i) providing (110) a scoring system comprising a scoring module (606); (ii) receiving (120) a training dataset comprising a plurality of patient data and treatment outcomes; (iii) analyzing (130), using a clinical decision support algorithm, the training dataset to generate a plurality of clinical decision support recommendations; (iv) clustering (140), using the scoring module, the plurality of clinical decision support recommendations into a plurality of clusters; and (v) identifying (160), using the scoring module, one or more features of at least one of the plurality of clusters, and generating, based on the identified one or more features, one or more inclusion criteria for the at least one of the plurality of clusters.

公开(公告)号：US11527312B2

公开(公告)日：2022-12-13

申请号：US16300271

申请日：2017-05-03

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Erina Ghosh ,  Oladimeji Feyisetan Farri

IPC: G16H15/00 ,  G16H10/60 ,  G16H50/70 ,  G06N20/00

Abstract: Instructions (108) cause a processor (104) to: classify a clinical report for a subject under evaluation by one of anatomical organ or disease; identify and retrieve clinical reports for the same subject from the healthcare data source(s); group the retrieved clinical report by one of anatomical organ or disease; select a group of the clinical report, wherein the group includes reports for a same or related one of the anatomical organ or the disease; build a model that predicts semantic relationships between nodes in the reports in the selected group of reports based on one or more of extracted parameters or keywords; compare one of the parameter values or the keywords across the reports using the model; construct a graphical timeline of the reports; highlight differences in the parameter values or the keywords based on a result of the compare; and visually present the graphical timeline with the highlighted differences.

公开(公告)号：US20210398677A1

公开(公告)日：2021-12-23

申请号：US17320324

申请日：2021-05-14

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Stephanie Lanius ,  Erina Ghosh ,  Larry James Eshelman

IPC: G16H50/20 ,  G06N20/00 ,  G16H10/60 ,  G16H50/30 ,  A61B5/00

Abstract: Techniques are described herein for using time series data such as vital signs data and laboratory data or other time series data as input across machine learning models to predict a change in stage of a medical condition of a patient. In various embodiments, patient data comprising vital signs data of a patient and laboratory data or other time series data of the patient corresponding to an observation window may be received. A time series model may be used to predict a change in stage of a medical condition in the patient in a prediction window based on the patient data. The predicted change in stage of the medical condition may be output.

公开(公告)号：US20210052217A1

公开(公告)日：2021-02-25

申请号：US16919154

申请日：2020-07-02

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Claire Yunzhu Zhao ,  Bryan Conroy ,  Mohammad Shahed Sorower ,  David Paul Noren ,  Kailash Swaminathan ,  Chaitanya Kulkarni ,  Ting Feng ,  Kristen Tgavalekos ,  Emma Holdrich Schwager ,  Erina Ghosh ,  Vinod Kumar ,  Vikram Shivanna ,  Srinivas Hariharan ,  Daniel Craig McFarlane

IPC: A61B5/00 ,  G16H10/60 ,  G16H50/70 ,  G16H50/20 ,  G16H50/30 ,  G16H50/50

Abstract: The present disclosure is directed to systems and methods for developing an individual-specific patient baseline for a target patient. An exemplary method involves: determining one or more acuity scores for the target patient; identifying patient health data corresponding to one or more low acuity time periods; storing retrospective clinical data from a group of patients in a second database; comparing the patient health data corresponding to the one or more low acuity time periods with retrospective clinical data from a group of patients by identifying one or more patient subgroups; determining the individual-specific patient baseline using an adaptive baseline selection algorithm, wherein the adaptive baseline selection algorithm is used to determine whether to determine the individual-specific patient baseline using patient health data or using retrospective clinical data from one or more patient subgroups; and displaying, using a user interface, the individual-specific patient baseline.

公开(公告)号：US20190231274A1

公开(公告)日：2019-08-01

申请号：US16311453

申请日：2017-06-12

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Erina Ghosh ,  Cristhian Potes ,  Richard Earl Gregg

IPC: A61B5/00 ,  A61B5/04 ,  A61B5/0452 ,  A61B5/021

CPC classification number: A61B5/7221 ,  A61B5/021 ,  A61B5/0215 ,  A61B5/02416 ,  A61B5/0295 ,  A61B5/04012 ,  A61B5/04525 ,  A61B5/0456 ,  A61B5/0468 ,  A61B5/0472 ,  A61B5/7246 ,  A61B5/7264

Abstract: In various embodiments, a first classification assigned to a periodic component of an electrical waveform that represents electrical activity in a patient's heart may be identified (302). A corresponding periodic component of a hemodynamic waveform that represents hemodynamic activity in the patient's cardiovascular system may be analyzed (306, 318, 328). The corresponding periodic component may be causally related to the periodic component of the electrical waveform. Based on the analysis, the previously-assigned classification may be assigned (312, 324) to the corresponding periodic component of the hemodynamic waveform in response to a determination, based on the analyzing, that the previously-assigned classification also applies to the corresponding periodic component. In a database (130) of hemodynamic templates, a hemodynamic template associated with the previously-assigned classification may be updated (314) to include one or more features of the corresponding periodic component of the hemodynamic waveform.