公开(公告)号：US20170007139A9

公开(公告)日：2017-01-12

申请号：US13889513

申请日：2013-05-08

Applicant: The Regents of the University of Colorado, a body corporate

Inventor： Gregory Zlatko Grudic ,  Steven Lee Moulton ,  Isobel Jane Mulligan

IPC: A61B5/03 ,  A61B5/1455 ,  A61B8/08 ,  A61B5/0205

CPC classification number: A61B5/031 ,  A61B5/0205 ,  A61B5/021 ,  A61B5/02108 ,  A61B5/02405 ,  A61B5/02416 ,  A61B5/0402 ,  A61B5/0836 ,  A61B5/14542 ,  A61B5/14551 ,  A61B5/4261 ,  A61B5/4839 ,  A61B5/742 ,  A61B8/488 ,  G06F19/00 ,  G06F19/3481 ,  G16H40/63 ,  G16H50/50

Abstract: Tools and techniques for the rapid, continuous, invasive and/or noninvasive measurement, estimation, and/or prediction of a patient's intracranial pressure. In an aspect, some tools and techniques can predict the onset of conditions such as herniation and/or can recommend (and, in some cases, administer) a therapeutic treatment for the patient's condition. In another aspect, some techniques employ high speed software technology that enables active, long term learning from extremely large, continually changing datasets. In some cases, this technology utilizes feature extraction, state-of-the-art machine learning and/or statistical methods to autonomously build and apply relevant models in real-time.

Abstract translation: 用于快速，连续，有创和/或无创测量，估计和/或预测患者颅内压的工具和技术。 在一方面，一些工具和技术可以预测诸如疝的病症的发病和/或可以推荐（并且在某些情况下，施用）用于患者状况的治疗性治疗。 另一方面，一些技术采用高速软件技术，可以从极大的，不断变化的数据集中进行主动，长期的学习。 在某些情况下，该技术利用特征提取，最先进的机器学习和/或统计方法来实时自主构建和应用相关模型。

公开(公告)号：US20200146944A1

公开(公告)日：2020-05-14

申请号：US16608158

申请日：2018-04-25

Applicant: THE REGENTS OF THE UNIVERSITY OF COLORADO, A BODY CORPORATE

Inventor： Steven Lee Moulton ,  Fergus Moynihan ,  Jeremy Parsons ,  Aleksander Stefanov ,  Young Mee Choi ,  Benjamin Fried ,  Brian Heckman ,  Tyler Mironuck

IPC: A61J15/00

Abstract: The present disclosure comprises a multi-component dressing for securing an enteral access device, for example, a gastrosnootomy, jejunostomy, cecostomy or vesicostomy button or a gastrostomy tube, or a parenteral access device, and can comprise one or more of an adhesive layer, an absorptive layer and a securement layer to engage the device.

公开(公告)号：US10226194B2

公开(公告)日：2019-03-12

申请号：US14867938

申请日：2015-09-28

Applicant: The Regents of the University of Colorado, a body corporate

Inventor： Gregory Zlatko Grudic ,  Steven Lee Moulton ,  Isobel Jane Mulligan

IPC: A61B5/03 ,  G06F19/00 ,  G16H50/50 ,  G16H40/63 ,  A61B5/1455 ,  A61B5/021 ,  A61B5/024 ,  A61B5/0402 ,  A61B5/083 ,  A61B5/145 ,  A61B5/0205 ,  A61B8/08 ,  A61B5/00

Abstract: Tools and techniques for the rapid, continuous, invasive and/or noninvasive measurement, estimation, and/or prediction of a patient's physiological state. In an aspect, some tools and techniques can estimate predict the onset of conditions intracranial pressure, an amount of blood volume loss, cardiovascular collapse, and/or dehydration. Some tools can recommend (and, in some cases, administer) a therapeutic treatment for the patient's condition. In another aspect, some techniques employ high speed software technology that enables active, long term learning from extremely large, continually changing datasets. In some cases, this technology utilizes feature extraction, state-of-the-art machine learning and/or statistical methods to autonomously build and apply relevant models in real-time.

公开(公告)号：US20130245397A1

公开(公告)日：2013-09-19

申请号：US13889513

申请日：2013-05-08

Applicant: The Regents of the University of Colorado, a body corporate

Inventor： Gregory Zlatko Grudic ,  Steven Lee Moulton ,  Isobel Jane Mulligan

IPC: A61B5/03 ,  A61B5/1455 ,  A61B8/08 ,  A61B5/0205

CPC classification number: A61B5/031 ,  A61B5/0205 ,  A61B5/021 ,  A61B5/02108 ,  A61B5/02405 ,  A61B5/02416 ,  A61B5/0402 ,  A61B5/0836 ,  A61B5/14542 ,  A61B5/14551 ,  A61B5/4261 ,  A61B5/4839 ,  A61B5/742 ,  A61B8/488 ,  G06F19/00 ,  G06F19/3481 ,  G16H20/40 ,  G16H40/63 ,  G16H50/50

Abstract: Tools and techniques for the rapid, continuous, invasive and/or noninvasive measurement, estimation, and/or prediction of a patient's intracranial pressure. In an aspect, some tools and techniques can predict the onset of conditions such as herniation and/or can recommend (and, in some cases, administer) a therapeutic treatment for the patient's condition. In another aspect, some techniques employ high speed software technology that enables active, long term learning from extremely large, continually changing datasets. In some cases, this technology utilizes feature extraction, state-of-the-art machine learning and/or statistical methods to autonomously build and apply relevant models in real-time.

Abstract translation: 用于快速，连续，有创和/或无创测量，估计和/或预测患者颅内压的工具和技术。 在一方面，一些工具和技术可以预测诸如疝的病症的发病和/或可以推荐（并且在某些情况下，施用）用于患者状况的治疗性治疗。 另一方面，一些技术采用高速软件技术，可以从极大的，不断变化的数据集中进行主动，长期的学习。 在某些情况下，该技术利用特征提取，最先进的机器学习和/或统计方法来实时自主构建和应用相关模型。

公开(公告)号：US20160015284A1

公开(公告)日：2016-01-21

申请号：US14867938

申请日：2015-09-28

Applicant: The Regents of the University of Colorado, a body Corporate

Inventor： Gregory Zlatko Grudic ,  Steven Lee Moulton ,  Isobel Jane Mulligan

IPC: A61B5/03 ,  A61B5/024 ,  A61B5/145 ,  A61B5/083 ,  A61B5/00 ,  A61B5/021 ,  A61B5/0402

CPC classification number: A61B5/031 ,  A61B5/0205 ,  A61B5/021 ,  A61B5/02108 ,  A61B5/02405 ,  A61B5/02416 ,  A61B5/0402 ,  A61B5/0836 ,  A61B5/14542 ,  A61B5/14551 ,  A61B5/4261 ,  A61B5/4839 ,  A61B5/742 ,  A61B8/488 ,  G06F19/00 ,  G06F19/3481 ,  G16H40/63 ,  G16H50/50

Abstract: Tools and techniques for the rapid, continuous, invasive and/or noninvasive measurement, estimation, and/or prediction of a patient's physiological state. In an aspect, some tools and techniques can estimate predict the onset of conditions intracranial pressure, an amount of blood volume loss, cardiovascular collapse, and/or dehydration. Some tools can recommend (and, in some cases, administer) a therapeutic treatment for the patient's condition. In another aspect, some techniques employ high speed software technology that enables active, long term learning from extremely large, continually changing datasets. In some cases, this technology utilizes feature extraction, state-of-the-art machine learning and/or statistical methods to autonomously build and apply relevant models in real-time.

Abstract translation: 用于快速，连续，有创和/或无创测量，估计和/或预测患者生理状态的工具和技术。 在一个方面，一些工具和技术可以估计预测病症颅内压的发生，血容量损失量，心血管塌陷和/或脱水。 一些工具可以推荐（并且在某些情况下，管理）用于患者状况的治疗性治疗。 另一方面，一些技术采用高速软件技术，可以从极大的，不断变化的数据集中进行主动，长期的学习。 在某些情况下，该技术利用特征提取，最先进的机器学习和/或统计方法来实时自主构建和应用相关模型。

公开(公告)号：US20160162786A1

公开(公告)日：2016-06-09

申请号：US15007489

申请日：2016-01-27

Applicant: The Regents of the University of Colorado, a body corporate

Inventor： Gregory Zlatko Grudic ,  Steven Lee Moulton ,  Isobel Jane Mulligan

IPC: G06N5/02

CPC classification number: G06N5/022 ,  G06N7/005 ,  G06N20/00 ,  G16H50/20 ,  G16H50/50

Abstract: Methods and systems are disclosed for autonomously building a predictive model of outcomes. A most-predictive set of signals Sk is identified out of a set of signals s1, s2, . . . , sD for each of one or more outcomes ok. A set of probabilistic predictive models ôk=Mk(Sk) is autonomously learned, where ôk is a prediction of outcome ok derived from the model Mk that uses as inputs values obtained from the set of signals Sk. The step of autonomously learning is repeated incrementally from data that contains examples of values of signals s1, s2, . . . , sD and corresponding outcomes o1, o2, . . . , oK. Various embodiments are also disclosed that apply predictive models to various physiological events and to autonomous robotic navigation.

Abstract translation: 公开了用于自主建立结果预测模型的方法和系统。 从信号集合s1，s2，...中识别最可预测的信号集合Sk。 。 。 ，每个一个或多个结果的sD都可以。 一组概率预测模型ôk= Mk（Sk）是自主学习的，其中ôk是从使用从信号集合Sk获得的输入值的模型Mk导出的结果确定的预测。 自动学习的步骤从包含信号s1，s2，...的值的例子的数据逐渐重复。 。 。 ，sD和相应的结果o1，o2，。 。 。 ， 好。 还公开了各种实施例，其将预测模型应用于各种生理事件和自主机器人导航。