公开(公告)号：US11651289B2

公开(公告)日：2023-05-16

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 ,  G06N3/08 ,  G06N20/00

CPC classification number: G06K9/6256 ,  G06K9/6262 ,  G06N3/08 ,  G06N20/00

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：US11406323B2

公开(公告)日：2022-08-09

申请号：US16628186

申请日：2018-07-10

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Gary Nelson Garcia Molina ,  Cristhian Mauricio Potes Blandon ,  Pedro Miguel Ferreira Dos Santos Da Fonseca ,  Bryan Conroy ,  Minnan Xu

IPC: A61B5/00 ,  A61B5/0205

Abstract: A system (400) for monitoring an individual's sleep includes: (i) a patient monitor (410) configured to obtain a patient waveform, the patient waveform comprising information representative of a vital statistic of the patient; a processor (420) in communication with the patient monitor and configured to: (i) process the patient waveform to generate a segmented waveform; (ii) extract at least one feature from a segment of the waveform in a time domain and/or at least one feature from the segment of the waveform in the frequency domain; (iii) classify, using the at least one extracted feature, a sleep stage of the patient for the segment of the waveform; and (iv) generate, from classified sleep stages for a plurality of segments of the waveform, a sleep quality measurement; and a user interface (480) configured to report the generated sleep quality measurement.

公开(公告)号：US11337616B2

公开(公告)日：2022-05-24

申请号：US16577425

申请日：2019-09-20

Applicant: KONINKLIJKE PHILIPS N.V.

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

IPC: A61B5/02 ,  A61B5/024 ,  A61B5/0255 ,  A61B5/00 ,  A61B5/316 ,  A61B5/344 ,  A61B5/352 ,  A61B5/366 ,  A61B5/327 ,  A61B5/364

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.

公开(公告)号：US11875277B2

公开(公告)日：2024-01-16

申请号：US17477615

申请日：2021-09-17

Applicant: KONINKLIJKE PHILIPS N.V.

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

IPC: G06N5/04 ,  G06N5/048 ,  G06N20/00 ,  G16H50/30 ,  G16H20/17 ,  G06N20/10 ,  G06N5/022 ,  G16H40/63 ,  G16H50/70 ,  G16H50/20

CPC classification number: G06N5/048 ,  G06N5/022 ,  G06N20/00 ,  G06N20/10 ,  G16H20/17 ,  G16H40/63 ,  G16H50/20 ,  G16H50/30 ,  G16H50/70

Abstract: Techniques disclosed herein relate to learning and applying contextual patient similarities. Multiple template similarity functions (118) may be provided (602). Each template similarity function may compare a respective subset of features of a query entity feature vector with a corresponding subset of features of a candidate entity feature vector. A composite similarity function (120) may be provided (604) as a weighted combination of respective outputs of the template similarity functions. A plurality of labeled entity vectors may be provided (606) as context training data. An approximation function may be applied (608) to approximate a first context label for each respective labeled entity vector. A first context specific composite similarity function may be trained (610) based on the composite similarity function by learning first context weights for the template similarity functions using a first loss function based on output of application of the approximation function to the first context training data.

公开(公告)号：US11126921B2

公开(公告)日：2021-09-21

申请号：US15957232

申请日：2018-04-19

Applicant: KONINKLIJKE PHILIPS N.V.

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

IPC: G06N5/04 ,  G06N20/00 ,  G16H50/30 ,  G16H20/17 ,  G06N20/10 ,  G06N5/02 ,  G16H40/63 ,  G16H50/70 ,  G16H50/20

Abstract: Techniques disclosed herein relate to learning and applying contextual patient similarities. Multiple template similarity functions may be provided. Each template similarity function may compare a respective subset of features of a query entity feature vector with a corresponding subset of features of a candidate entity feature vector. A composite similarity function may be provided as a weighted combination of respective outputs of the template similarity functions. A plurality of labeled entity vectors may be provided as context training data. An approximation function may be applied to approximate a first context label for each respective labeled entity vector. A first context specific composite similarity function may be trained based on the composite similarity function by learning first context weights for the template similarity functions using a first loss function based on output of application of the approximation function to the first context training data.

公开(公告)号：US20180307995A1

公开(公告)日：2018-10-25

申请号：US15957232

申请日：2018-04-19

Applicant: KONINKLIJKE PHILIPS N.V.

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

IPC: G06N5/04 ,  G16H50/20 ,  G16H50/70 ,  G06N99/00

CPC classification number: G06N5/048 ,  G06N99/005 ,  G16H50/20 ,  G16H50/70

Abstract: Techniques disclosed herein relate to learning and applying contextual patient similarities. Multiple template similarity functions may be provided. Each template similarity function may compare a respective subset of features of a query entity feature vector with a corresponding subset of features of a candidate entity feature vector. A composite similarity function may be provided as a weighted combination of respective outputs of the template similarity functions. A plurality of labeled entity vectors may be provided as context training data. An approximation function may be applied to approximate a first context label for each respective labeled entity vector. A first context specific composite similarity function may be trained based on the composite similarity function by learning first context weights for the template similarity functions using a first loss function based on output of application of the approximation function to the first context training data.

公开(公告)号：US12183105B2

公开(公告)日：2024-12-31

申请号：US17781074

申请日：2020-12-18

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Ashequl Qadir ,  Kathy Mi Young Lee ,  Claire Yunzhu Zhao ,  Aaditya Prakash ,  Minnan Xu

IPC: G06V30/413 ,  G06V10/774 ,  G16H15/00 ,  G16H30/40

Abstract: A system and method for unsupervised training of a text report identification machine learning model, including: labeling a first set of unlabeled text reports using a seed dictionary to identify concepts in the unlabeled text reports; inputting images associated with the first set of seed-labeled text reports into an auto-encoder to obtain an encoded first set of images; calculating a set of first correlation matrices as a dot product of the first encoded images with their associated text report features; determining a distance between the set of first correlation matrices and a filter bank value associated with the auto-encoder; identifying a first set of validated images as the images in the first set of images that have a distance less than a threshold value; and training the text report machine learning model using the labeled text reports associated with the set of first validated images.

公开(公告)号：US11676733B2

公开(公告)日：2023-06-13

申请号：US16955090

申请日：2018-12-18

Applicant: KONINKLIJKE PHILIPS N.V.

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

IPC: G16H50/70 ,  G16H50/20 ,  G06N20/00 ,  G06F18/22 ,  G06F18/214

CPC classification number: G16H50/70 ,  G06F18/214 ,  G06F18/22 ,  G06N20/00 ,  G16H50/20

Abstract: Techniques disclosed herein relate to learning and applying contextual patient similarities. In various embodiments, a first value for a query entity may be displayed (702) on an interface. The first value may be related to a first context. A first trained similarity function may be selected (704) from a plurality of trained similarity functions. The first trained similarity function may be associated with the first context. The first selected trained similarity function may be applied (706) to a set of features associated with the query entity and respective sets of features associated with a plurality of candidate entities. A set of one or more similar candidate entities may be selected (708) from the plurality of candidate entities based on application of the first trained similarity function. Information associated with the first set of one or more similar candidate entities may be displayed (710) on the interface.

公开(公告)号：US11620554B2

公开(公告)日：2023-04-04

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: G06N7/00 ,  G16H50/70 ,  G06N3/02 ,  G16H50/20 ,  G16H10/60 ,  G16H50/30 ,  G06N20/00

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：US10929774B2

公开(公告)日：2021-02-23

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: G06N20/00 ,  G16H50/20 ,  G06F16/28 ,  A61B5/00 ,  G06N5/04 ,  G06F17/18 ,  A61B5/02 ,  G06F7/08

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.