公开(公告)号：US20210350933A1

公开(公告)日：2021-11-11

申请号：US17277619

申请日：2019-09-16

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： David Paul NOREN ,  Asif RAHMAN ,  Bryan CONROY ,  Minnan XU ,  Nikhil GALAGALI

IPC: G16H50/30 ,  G16H50/20 ,  G16H10/60 ,  G16H50/70 ,  G06N20/00 ,  G06F17/18 ,  A61B5/00

Abstract: Various embodiments of the present disclosure are directed to a general statistical classifier (40) and a personal statistical classifier (50) for executing a patient risk prediction method. In operation, the general statistical classifier (40) may render a singular general independent vital sign risk score for a singular vital sign and/or may render plural general independent vital sign risk scores for plural vital signs. The personal statistical classifier (50) may render a singular personal vital sign risk score from an integration of a singular patient feature into the singular general independent vital sign risk score, and/or may also render plural personal independent vital sign risk scores from individual integrations of plural patient features into the singular general independent vital sign risk score, individual integrations of a singular patient feature into the plural general independent vital sign risk scores, and/or individual integrations of plural patient features into the plural general independent vital sign risk scores.

公开(公告)号：US20200146619A1

公开(公告)日：2020-05-14

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：US20220028565A1

公开(公告)日：2022-01-27

申请号：US17276708

申请日：2018-09-17

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Nikhil GALAGALI ,  Minnan XU ,  Bryan CONROY ,  Asif RAHMAN ,  David Paul NOREN

IPC: G16H70/60 ,  G16H10/60

Abstract: A method of determining patient subtyping from disease progression trajectories, including: extracting patient data and related time stamps from patient record data related to a disease, wherein the extracted patient data is incomplete and irregular; building a continuous-time disease progression model based upon the extracted patient data; and building a mixture model for clustering of patient disease trajectory subtypes.

公开(公告)号：US20190139631A1

公开(公告)日：2019-05-09

申请号：US16097299

申请日：2017-05-04

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Larry James ESHELMAN ,  Eric Thomas CARLSON ,  Lin YANG ,  Minnan XU ,  Bryan CONROY

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

Abstract: The present disclosure relates to estimation and use of clinician assessment of patient acuity. In various embodiments, a plurality of patient feature vectors associated with a plurality of respective patients may be obtained (302, 304). Each patient feature vector may include one or more health indicator features indicative of observable health indicators of a patient, and one or more treatment features indicative of characteristics of treatment provided to the patient. A machine learning model (216) may be trained (306) based on the patient feature vectors to receive, as input, subsequent patient feature vectors, and to provide, as output, indications of levels of clinician acuity assessment. Later, a patient feature vector associated with a given patient may be provided (404) as input to the machine learning model. Based on output from the machine learning model, a level of clinician acuity assessment associated with the given patient may be estimated (406) and used (408-416) for various applications.

公开(公告)号：US20220165417A1

公开(公告)日：2022-05-26

申请号：US17441089

申请日：2020-03-18

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Yale CHANG ,  Bryan CONROY

IPC: G16H50/20 ,  G16H50/70 ,  G16H50/30 ,  G16H10/60

Abstract: A device, system and method for generating a prediction model for a test patient. To generate the prediction model, a multi-dimensional clinical time series for each of a plurality of training patients is collected to generate a training population. A machine learning algorithm is then trained using the training population. Measurement data corresponding to the test patient is also received, the measurement data includes a multi-dimensional clinical time series for the test patient. The test patient is not included in the plurality of training patients. The prediction model is generated for the test patient based on i) the measurement data corresponding to the test patient and ii) training the machine learning algorithm using the training population.

公开(公告)号：US20220028533A1

公开(公告)日：2022-01-27

申请号：US17267615

申请日：2020-04-10

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Chaitanya KULKARNI ,  Mohammad Shahed SOROWER ,  Bryan CONROY ,  Claire Yunzhu ZHAO ,  David Paul NOREN ,  Kailash SWAMINATHAN ,  Ting FENG ,  Kristen TGAVALEKOS ,  Daniel Craig MCFARLANE ,  Erina GHOSH ,  Vinod KUMAR ,  Vikram SHIVANNA ,  Shraddha BARODIA ,  Emma Holdrich SCHWAGER ,  Prasad RAGHOTHAM VENKAT

IPC: G16H40/20 ,  G16H50/30 ,  G16H50/80 ,  G06N7/00

Abstract: A method for processing medical information includes identifying a first patient in a first state, identifying a second patient in a second state, calculating a first risk score for the first patient, calculating a first risk score for the second patient, and determining a risk prone area in a medical facility based on the first risk score for the first patient and the first risk score for the second patient. The first state is an infected state and the second state is different from the first state. The first risk score of the first patient provides an indication of a severity of the infected state of the first patient, and the first risk score of the second patient provides an indication of the second patient being infected by the first patient.

公开(公告)号：US20210298686A1

公开(公告)日：2021-09-30

申请号：US17266684

申请日：2019-07-30

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Claire Yunzhu ZHAO ,  Minnan XU ,  Bryan CONROY

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

Abstract: Methods and systems for managing alerts. The methods and systems described herein receive a classification decision related to a patient. If the classification decision is a borderline classification decision, the systems and methods described herein apply one or more alert filters to patient data to determine an alert filter condition. Upon determining the alert filter condition contradicts the borderline classification, the systems and methods may issue a contextual data alert to a clinician to prompt the clinician to consider contextual data related to the patient.

公开(公告)号：US20190029533A1

公开(公告)日：2019-01-31

申请号：US16075812

申请日：2017-02-04

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Cristhian M. POTES ,  Minnan XU ,  Bryan CONROY

IPC: A61B5/02 ,  A61B5/00 ,  A61B5/083 ,  G16H50/30

Abstract: The present disclosure pertains to a system configured to determine a hemodynamic instability risk score for a pediatric subject. The system is configured to: obtain an age of the subject; obtain feature values for one or more features associated with physiological characteristics of the subject; determine one or more feature value thresholds for individual features that indicate risk of hemodynamic instability in the subject, the feature value thresholds determined based on the age of the subject; determine feature contribution prediction scores for the individual features based on whether the obtained feature values breach one or more of the determined feature value thresholds for the individual features; and aggregate the feature contribution prediction scores to determine the hemodynamic instability risk score for the subject.