Patents by Inventor Bryan Conroy

Bryan Conroy has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20210052217
    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.
    Type: Application
    Filed: July 2, 2020
    Publication date: February 25, 2021
    Inventors: 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
  • Patent number: 10929774
    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.
    Type: Grant
    Filed: February 5, 2016
    Date of Patent: February 23, 2021
    Assignee: Koninklijke Philips N.V.
    Inventors: Bryan Conroy, Larry James Eshelman, Cristhian Potes, Minnan Xu
  • Publication number: 20210012897
    Abstract: A method of explaining a machine learning model, including: receiving a plurality of disease states for a patient over time from a database, wherein the disease states have a plurality of features; generating a plurality of locally faithful explanation models for the patient for each disease state based upon the machine learning model; calculating an explanation with respect to one feature of the plurality of features over time using the locally faithful explanation models; and calculating the importance of the one feature of the plurality of features over time based upon the plurality of locally faithful explanation models .
    Type: Application
    Filed: May 21, 2020
    Publication date: January 14, 2021
    Inventors: Gajendra Jung Katuwal, Bryan Conroy, Jonathan Rubin
  • Patent number: 10869631
    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).
    Type: Grant
    Filed: December 9, 2015
    Date of Patent: December 22, 2020
    Assignee: KONINKLIJKE PHILIPS N.V.
    Inventors: Cristhian Potes, Bryan Conroy, Adam Jacob Seiver, Minnan Xu, Larry James Eshelman
  • Publication number: 20200242470
    Abstract: A method, system and computer-program product for identifying neural network inputs for a neural network that may have been incorrectly processed by the neural network. A set of activation values (of a subset of neurons of a single layer) associated with a neural network input is obtained. A neural network output associated with the neural network input is also obtained. A determination is made as to whether a first and second neural network input share similar sets of activation values, but dissimilar neural network outputs or vice versa. In this way a prediction can be made as to whether one of the first and second neural network inputs has been incorrectly processed by the neural network.
    Type: Application
    Filed: October 16, 2018
    Publication date: July 30, 2020
    Applicant: KONINKLIJKE PHILIPS N.V.
    Inventors: Vlado Menkovski, Asif Rahman, Caroline Denise Francoise Raynaud, Bryan Conroy, Dimitrios Mavroeidis, Erik Bresch, Teun van den Heuvel
  • Publication number: 20200160201
    Abstract: A method for training a probabilistic encoder-decoder having a latent space, the method including: extracting different types of medical data for a group of individuals; creating a data matrix X including the extracted medical data, wherein each row of the data matrix X includes data for one of the group of individuals; creating condition matrix C including features to define a clinical condition, wherein each row of the condition matrix C includes the condition data for one of the group of individuals; and training the encoder and the decoder to learn the latent space by minimizing the reconstruction loss and using a regularization effect to force clinically similar inputs to be close together in the latent space.
    Type: Application
    Filed: November 11, 2019
    Publication date: May 21, 2020
    Inventors: Gajendra Jung Katuwal, Bryan Conroy, Jack He, Jonathan Rubin
  • Publication number: 20200051696
    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.
    Type: Application
    Filed: August 7, 2019
    Publication date: February 13, 2020
    Inventors: Claire Zhao, Jonathan Rubin, Bryan Conroy, Asif Rahman, Minnan Xu
  • Publication number: 20200050892
    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.
    Type: Application
    Filed: August 5, 2019
    Publication date: February 13, 2020
    Inventors: Bryan Conroy, Junzi Dong, Minnan Xu
  • Publication number: 20190192110
    Abstract: Various embodiments of the inventions of the present disclosure provide a combination of feature-based approach and deep learning approach for distinguishing between normal heart sounds and abnormal heart sounds. A feature-based classifier (60) is applied to a phonocardiogram (PCG) signal to obtain a feature-based abnormality classification of the heart sounds represented by the PCG signal and a deep learning classifier (70) is also applied to the PCG signal to obtain a deep learning abnormality classification of the heart sounds represented by the PCG signal. A final decision analyzer (80) is applied to the feature-based abnormality classification and the deep learning abnormality classification of the heart sounds represented by the PCG signal to determine a final abnormality classification decision of the PCG signal.
    Type: Application
    Filed: September 7, 2017
    Publication date: June 27, 2019
    Inventors: Saman Parvaneh, Cristhian Mauricio Potes Blandon, Asif Rahman, Bryan Conroy
  • Publication number: 20190164648
    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.
    Type: Application
    Filed: August 1, 2017
    Publication date: May 30, 2019
    Inventors: Bryan Conroy, Cristhian Mauricio Potes Blandon, Minnan Xu
  • Publication number: 20190133480
    Abstract: Techniques described herein relate to training and applying predictive models using discretized physiological sensor data. In various embodiments, a continuous stream of samples measured by a physiological sensor may be discretized into a training sequence of quantized beats. A training sequence of vectors determined based on the training sequence of quantized beats and an embedding matrix may be associated with labels indicative of medical conditions, and applied as input across a neural network to generate corresponding instances of training output. Based on a comparison of each instance of training output with a respective label, the neural network and the embedding matrix may be trained and used to predict medical conditions from unlabeled continuous streams of physiological sensor samples. In some embodiments, the trained embedding matrix may be visualized to identify correlations between medical conditions and physiological signs.
    Type: Application
    Filed: November 2, 2018
    Publication date: May 9, 2019
    Inventors: Asif Rahman, Bryan Conroy
  • Publication number: 20180307995
    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.
    Type: Application
    Filed: April 19, 2018
    Publication date: October 25, 2018
    Inventors: Bryan Conroy, Minnan Xu, Asif Rahman, Cristhian Mauricio Potes Blandon
  • Publication number: 20180046942
    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.
    Type: Application
    Filed: February 5, 2016
    Publication date: February 15, 2018
    Inventors: Bryan Conroy, Larry James Eshelman, Cristhian Potes, Minnan Xu
  • Publication number: 20170360366
    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).
    Type: Application
    Filed: December 9, 2015
    Publication date: December 21, 2017
    Applicant: KONINKLIJKE PHILIPS N.V.
    Inventors: Cristhian Potes, Bryan Conroy, Adam Jacob Seiver, Minnan Xu, Larry James Eshelman