公开(公告)号：US20180366211A1

公开(公告)日：2018-12-20

申请号：US16061372

申请日：2016-12-20

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Lin Yang ,  Minnan Xu ,  Stijn De Waele ,  Rony Calo

IPC: G16H10/40 ,  G16H50/20 ,  G16H50/70 ,  G06N7/00 ,  G06K9/62 ,  G06F17/18

Abstract: Systems for user behavior-trained clinical support. Features of the present invention monitor users to track their behavior while using medical devices/software. This information is combined with physiological parameters of patients to train new clinical decision support (CDS) algorithms to provide improvement in deterioration detection, alarm management, and decision and navigation support.

公开(公告)号：US20170360366A1

公开(公告)日：2017-12-21

申请号：US15536163

申请日：2015-12-09

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Cristhian Potes ,  Bryan Conroy ,  Adam Jacob Seiver ,  Minnan Xu ,  Larry James Eshelman

IPC: A61B5/00 ,  A61M5/172 ,  A61B5/0205 ,  A61B5/02 ,  A61B5/091 ,  A61B5/08 ,  A61B5/0245 ,  A61B5/021 ,  A61B5/087

CPC classification number: A61B5/4848 ,  A61B5/02028 ,  A61B5/0205 ,  A61B5/02055 ,  A61B5/021 ,  A61B5/02405 ,  A61B5/02416 ,  A61B5/0245 ,  A61B5/04325 ,  A61B5/0456 ,  A61B5/082 ,  A61B5/0836 ,  A61B5/087 ,  A61B5/091 ,  A61B5/4833 ,  A61B5/4839 ,  A61B5/7267 ,  A61B5/7289 ,  A61B5/742 ,  A61B5/746 ,  A61M5/1723 ,  A61M2005/14208 ,  A61M2205/50 ,  A61M2205/52 ,  G06F19/3456 ,  G06F19/3468

Abstract: A system (100) for assessing fluid responsiveness includes an infusion pump (24) in communication with at least one processor (32), and a plurality of physiological monitors (40,42,44,46) operable to receive physiological signals from an associated patient. Physiological signals (48,50) acquired from the associated patient (10) during a fluid challenge are synchronized with a timing signal (54) of the infusion pump (24) administering the fluid challenge. One or more dynamic indices and/or features (58) is calculated from the synchronized physiological signals (50), and one or more dynamic indices and/or features (50) is calculated from baseline physiological signals (48) acquired from the associated patient (10) prior to the fluid challenge. A fluid responsiveness probability value (64) of the patient (10) is determined based on dynamic indices and/or features (58) from the synchronized physiological signals (50) and dynamic indices and/or features (50) from the baseline physiological signals (48).

公开(公告)号：US20240091623A1

公开(公告)日：2024-03-21

申请号：US18509945

申请日：2023-11-15

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Louis Nicolas Atallah ,  Josheph James Frassica ,  Minnan Xu

IPC: A63B71/06 ,  A61B3/11 ,  A61B5/00 ,  A61B5/0205 ,  A61B5/103 ,  A61B5/11 ,  A61B5/1455 ,  A61B5/16 ,  G06F18/21 ,  G06N3/082 ,  G06T11/00 ,  G06V20/40 ,  G10L25/30 ,  G10L25/63 ,  G10L25/66 ,  G16H40/67 ,  G16H50/30 ,  H04N7/14

CPC classification number: A63B71/06 ,  A61B3/112 ,  A61B5/0077 ,  A61B5/0205 ,  A61B5/1032 ,  A61B5/1101 ,  A61B5/14551 ,  A61B5/165 ,  A61B5/4803 ,  A61B5/7267 ,  G06F18/21 ,  G06N3/082 ,  G06T11/00 ,  G06V20/41 ,  G06V20/46 ,  G10L25/30 ,  G10L25/63 ,  G10L25/66 ,  G16H40/67 ,  G16H50/30 ,  H04N7/141 ,  A61B5/02444

Abstract: The present disclosure pertains to a system for providing client-side physiological condition estimations during a live video session. In some embodiments, the system includes a first client computer system that is caused to: (i) store a neural network on one or more computer-readable storage media of the first client computer system, (ii) obtain a live video stream of an individual via a camera of the first client computer system during a video streaming session between the first client computer system and a second client computer system, (iii) provide, during the video streaming session, video data of the live video stream as input to the neural network to obtain physiological condition information from the neural network, and (iv) provide, during the video streaming session, the physiological condition information for presentation at the second client computer system.

公开(公告)号：US11020092B2

公开(公告)日：2021-06-01

申请号：US16061455

申请日：2016-12-22

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Minnan Xu ,  Balasundar Iyyavu Raju ,  Ajay Anand

IPC: A61B8/08 ,  A61B8/06 ,  A61B8/00

Abstract: A Doppler ultrasound instrument (10) includes ultrasound pulse control and data acquisition electronics (12, 24, 26) for acquiring Doppler ultrasound data, an N-channel connector port (14) for simultaneously operatively connecting up to N ultrasound transducer patches (16) where N is an integer equal to or greater than two, and an electronic processor (30) programmed to concurrently determine up to N blood flow velocities corresponding to up to N patches operatively connected to the N channel connector port. The blood flow velocity for each patch may be determined by: determining transducer blood flow velocities for ultrasound transducers (60) of a transducer array of the patch; and determining the blood flow velocity for the patch as a highest determined transducer blood flow velocity or as an aggregation of highest determined transducer blood flow velocities.

公开(公告)号：US20190164648A1

公开(公告)日：2019-05-30

申请号：US16323802

申请日：2017-08-01

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Bryan Conroy ,  Cristhian Mauricio Potes Blandon ,  Minnan Xu

IPC: G16H50/20 ,  G16H10/60

Abstract: An electronic clinical decision support (CDS) device (10) employs a trained CDS algorithm (30) that operates on values of a set of covariates to output a prediction of a medical condition. The CDS algorithm was trained on a training data set (22). The CDS device includes a computer (12) that is programmed to provide a user interface (62) for completing clinical survey questions using the display and the one or more user input devices. Marginal probability distributions (42) for the covariates of the set of covariates are generated from the completed clinical survey questions. The trained CDS algorithm is adjusted for covariate shift using the marginal probability distributions. A prediction of the medical condition is generated for a medical subject using the trained CDS algorithm adjusted for covariate shift (50) operating on values for the medical subject of the covariates of the set of covariates.

公开(公告)号：US20180046942A1

公开(公告)日：2018-02-15

申请号：US15549714

申请日：2016-02-05

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Bryan Conroy ,  Larry James Eshelman ,  Cristhian Potes ,  Minnan Xu

IPC: G06N99/00 ,  G06F19/00 ,  A61B5/02 ,  G06F7/08 ,  A61B5/00 ,  G06N5/04 ,  G06F17/30

CPC classification number: G06N20/00 ,  A61B5/02028 ,  A61B5/7267 ,  A61B5/7275 ,  A61B2505/03 ,  G06F7/08 ,  G06F16/285 ,  G06F17/18 ,  G06N5/04 ,  G16H50/20

Abstract: Various embodiments described herein relate to methods and apparatus for robust classification. Many real-world datasets suffer from missing or incomplete data. By assigning weights to certain features of a dataset based on which feature(s) are missing or incomplete, embodiments of the prevention can provide robustness and resilience to missing data.

公开(公告)号：US11904224B2

公开(公告)日：2024-02-20

申请号：US16969114

申请日：2019-01-31

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Louis Nicolas Atallah ,  Joseph James Frassica ,  Minnan Xu

IPC: G16H40/67 ,  A63B71/06 ,  G16H50/30 ,  A61B3/11 ,  A61B5/00 ,  A61B5/0205 ,  A61B5/103 ,  A61B5/11 ,  A61B5/1455 ,  A61B5/16 ,  G06N3/082 ,  G06T11/00 ,  G10L25/30 ,  G10L25/63 ,  G10L25/66 ,  H04N7/14 ,  G06V20/40 ,  G06F18/21 ,  A61B5/024 ,  A61B5/08

CPC classification number: A63B71/06 ,  A61B3/112 ,  A61B5/0077 ,  A61B5/0205 ,  A61B5/1032 ,  A61B5/1101 ,  A61B5/14551 ,  A61B5/165 ,  A61B5/4803 ,  A61B5/7267 ,  G06F18/21 ,  G06N3/082 ,  G06T11/00 ,  G06V20/41 ,  G06V20/46 ,  G10L25/30 ,  G10L25/63 ,  G10L25/66 ,  G16H40/67 ,  G16H50/30 ,  H04N7/141 ,  A61B5/02444 ,  A61B5/0816

Abstract: The present disclosure pertains to a system for providing client-side physiological condition estimations during a live video session. In some embodiments, the system includes a first client computer system that is caused to: (i) store a neural network on one or more computer-readable storage media of the first client computer system, (ii) obtain a live video stream of an individual via a camera of the first client computer system during a video streaming session between the first client computer system and a second client computer system, (iii) provide, during the video streaming session, video data of the live video stream as input to the neural network to obtain physiological condition information from the neural network, and (iv) provide, during the video streaming session, the physiological condition information for presentation at the second client computer system.

公开(公告)号：US20200051696A1

公开(公告)日：2020-02-13

申请号：US16533912

申请日：2019-08-07

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Claire Zhao ,  Jonathan Rubin ,  Bryan Conroy ,  Asif Rahman ,  Minnan Xu

IPC: G16H50/30 ,  G16H10/60 ,  G16H50/70

Abstract: A method of determining the infection risk probability for a patient, including: encoding physiological data of the patient into a first synthetic image; encoding environmental data of the patient into a second synthetic image; determining an intrinsic probability of infection for the patient based upon the first synthetic image and the second synthetic image using a machine learning model; generating a graphical model based upon the patient and other patients based upon similarity scores between the patient and the other patients; and determining the infection risk probability for the patient based upon the graphical model and the intrinsic probability of infection for the patient and the other patients.

公开(公告)号：US20200050892A1

公开(公告)日：2020-02-13

申请号：US16531189

申请日：2019-08-05

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Bryan Conroy ,  Junzi Dong ,  Minnan Xu

IPC: G06K9/62 ,  G06N20/00 ,  G06N3/08

Abstract: A method of implementing a task complexity learning system, including: learning a model for predicting the value of a continuous task variable y based upon an input variable x; learning an encoder that encodes a continuous task variable y into an encoded task value; calculating a loss function based upon the predicted value of y output by the model and the encoded task value output by the encoder; calculating a distortion function based upon the input continuous task variable y and the encoded task value, wherein learning the model and learning the encoder includes minimizing an objective function based upon the loss function and the distortion function for a set of input training data including x, y pairs.

公开(公告)号：US10531801B2

公开(公告)日：2020-01-14

申请号：US14913700

申请日：2014-08-20

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Limei Cheng ,  Eric Thomas Carlson ,  Srinivasan Vairavan ,  Minnan Xu

IPC: A61B5/04 ,  A61B5/024 ,  A61B5/0456 ,  A61B5/0472

Abstract: System (10) for extracting a fetal heart rate from at least one maternal signal using a computer processor (26). The system includes sensors (12-18) attached to a patient to receive abdominal ECG signals and a recorder and digitizer (20) to record and digitize each at least one maternal signal in a maternal signal buffer (22A-22D). The system further includes a peak detector (40) to identify candidate peaks in the maternal signal buffer. The signal stacker (42) of the system stacks the divides at least one maternal signal buffer into a plurality of snippets, each snippet including one candidate peak and a spatial filter (44) to identify and attenuate a maternal QRS signal in the plurality of snippets of the maternal signal buffer, the spatial filter including at least one of principal component analysis and orthogonal projection, to produce a raw fetal ECG signal which is stored in a raw fetal ECG buffer. The system further includes a fetal QRS identifier (46) for identifying peaks in the raw fetal ECG buffer by at least one of principal component analysis and a peak-detector followed by rule based fQRS extraction and a merger (48) to calculate and merge the fetal heart rate from the identified peaks.