公开(公告)号：US20200022597A1

公开(公告)日：2020-01-23

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: A61B5/024 ,  A61B5/0255 ,  A61B5/0444 ,  A61B5/00 ,  A61B5/0456 ,  A61B5/04 ,  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.

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：US20190287675A1

公开(公告)日：2019-09-19

申请号：US16301618

申请日：2017-05-30

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Erina GHOSH ,  Oladimeji Feyisetan FARRI ,  Lin YANG ,  Eric Thomas CARLSON

IPC: G16H40/20 ,  G16H10/20 ,  G06F16/2455

Abstract: The present disclosure pertains to obtaining information that facilitates determining healthcare quality measures by evaluating subject healthcare data in real-time. Information is obtained that facilitates determination of compliance with healthcare quality measures. This is accomplished by running queries on a clinical database comprising subject healthcare data. Natural language processing is utilized to extract subject healthcare data at various times from the clinical database based on individual queries, thus determining any changes in subject healthcare data over time. A rule-based component is used to implement healthcare quality measures and evaluate updated subject healthcare data based upon rules.

公开(公告)号：US20170277853A1

公开(公告)日：2017-09-28

申请号：US15528564

申请日：2015-12-14

Applicant: Koninklijke Philips N.V.

Inventor： Eric Thomas CARLSON ,  Larry James ESHELMAN

IPC: G06F19/00

CPC classification number: G06F19/3418 ,  G16H50/30

Abstract: An early warning system for patient monitoring includes one or more patient monitors (620) configured to generate patient physiological data, a patient database (602) storing patient physiological measurements and outcomes, and one or more computer processors (604) programmed to: machine learn an Aggregate Weighted Track and Trigger System (AWTTS) algorithm for quantifying patient condition by an AWTTS score based on a training set of the patient physiological measurements and outcomes; apply an Early Warning Score or Modified Early Warning Score (EWS) algorithm to patient physiological measurements to generate EWS scores; apply the machine-learned AWTTS algorithm to the patient physiological measurements to generate AWTTS scores; and create a mapping between the AWTTS scores and the EWS scores.

公开(公告)号：US20210407632A1

公开(公告)日：2021-12-30

申请号：US16647163

申请日：2018-09-06

Applicant: KONINKLIJKE PHILIPS N.V.

Inventor： Eric Thomas CARLSON ,  Oladimeji Feyisetan FARRI

IPC: G16H10/60

Abstract: A system (300) configured to analyze electronic medical records comprises: a user interface (310) configured to receive input from a user and to receive a request for patient information; and a processor (320) comprising: a patient cohort generator (350) configured to: (i) track user input; (ii) identify patient information accessed through the user interface as well as patient parameters associated with the patient; (iii) associate patients into a patient cohort based on the patient parameters; (iv) identify, for the patient cohort, types of information most commonly accessed by the users; and (v) associate the identified types of information with the patient cohort; and a record identifier (370) configured to: (i) associate the patient for whom patient information is requested with a patient cohort; and (ii) identify, based on the patient cohort with whom the patient is associated, the types of information associated with that cohort.

公开(公告)号：US20190355479A1

公开(公告)日：2019-11-21

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 ,  G16H50/20 ,  G16H10/60 ,  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：US20160198969A1

公开(公告)日：2016-07-14

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: A61B5/0444 ,  A61B5/0456 ,  A61B5/0402 ,  A61B5/0472 ,  A61B5/00 ,  A61B5/024 ,  A61B5/04

CPC classification number: A61B5/02411 ,  A61B5/02405 ,  A61B5/0255 ,  A61B5/04017 ,  A61B5/04028 ,  A61B5/0444 ,  A61B5/0456 ,  A61B5/0468 ,  A61B5/0472 ,  A61B5/4362 ,  A61B5/7203 ,  A61B5/725 ,  A61B5/7253

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.

Abstract translation: 用于使用计算机处理器（26）从至少一个母体信号提取胎儿心率的系统（10）。 该系统包括附接到患者以接收腹部ECG信号的传感器（12-18），以及记录器和数字化仪（20），用于在母体信号缓冲器（22A-22D）中记录和数字化每个至少一个母体信号。 该系统还包括峰值检测器（40），以识别母体信号缓冲器中的候选峰值。 系统的信号堆叠器（42）将至少一个母体信号缓冲器分成多个片段，每个片段包括一个候选峰和空间滤波器（44），以识别并衰减多个片段中的母体QRS信号 的母体信号缓冲器，空间滤波器包括主成分分析和正交投影中的至少一个，以产生存储在原始胎儿ECG缓冲器中的原始胎儿ECG信号。 该系统还包括用于通过主成分分析和峰值检测器中的至少一个识别原始胎儿ECG缓冲器中的峰值的胎儿QRS标识符（46），随后是基于规则的fQRS提取和合并（48）以计算并合并 胎心率从确定的峰值。