Etiometry, Inc.

The Charles Stark Draper Laboratory, Inc.

Massachusetts Institute of Technology

Michael F McManus

Evan J Butler

Dimitar V Baronov

Jesse M Lock

Rami S Mangoubi

Yaron Rachlin

Thomas F Marinis

Christopher C Yu

Richard D Elliott

Peter G Sherman

Jeffery W Warren

Matthew S Bottkol

Michael Y Feng

Shan Mohiuddin

John E Pritchett

Charles H Lange

A risk-based patient monitoring system for critical care patients combines data from multiple sources to assess the current and the future risks to the patient, thereby enabling providers to review a current patient risk profile and to continuously track a clinical trajectory. A physiology observer module in the system utilizes multiple measurements to estimate Probability Density Functions (PDF) of a number of Internal State Variables (ISVs) that describe a components of the physiology relevant to the patient treatment and condition. A clinical trajectory interpreter module in the system utilizes the estimated PDFs of ISVs to identify under which probable patient states the patient can be currently categorized and assign a probability value that the patient will be in each of the identified states. The combination of patient states and their probabilities is defined as the clinical risk to the patient.

System and methods for transitioning patient care from signal based monitoring to risk based monitoring

A risk-based patient monitoring system for critical care patients combines data from multiple sources to assess the current and the future risks to the patient, thereby enabling providers to review a current patient risk profile and to continuously track a clinical trajectory. A physiology observer module in the system utilizes multiple measurements to estimate Probability Density Functions (PDF) of a number of Internal State Variables (ISVs) that describe components of the physiology relevant to the patient treatment and condition. A clinical trajectory interpreter module in the system utilizes the estimated PDFs of ISVs to identify under which probable patient states the patient can be currently categorized and assign a probability value that the patient will be in each of the identified states. The combination of patient states and their probabilities is defined as the clinical risk to the patient.

Systems and methods for transitioning patient care from signal based monitoring to risk based monitoring

Systems and methods for transitioning patient care from signal-based monitoring to risk-based monitoring

A method and apparatus for estimating and compensating for a broad class of non-Gaussian sensor and process noise. In one example, a coded filter combines a dynamic state estimator (for example, a Kalman filter) and a non-linear estimator to provide approximations of the non-Gaussian process and sensor noise associated with a dynamic system. These approximations are used by the dynamic state estimator to correct sensor measurements or to alter the dynamic model governing evolution of the system state. Examples of coded filters leverage compressive sensing techniques in combination with error models based on concepts of compressibility and the application of efficient convex optimization processes.

Coded filter

A micro-electro-mechanical systems (MEMS) inertial measurement system facilitates accurate location and/or attitude measurements via passive thermal management of MEMS inertial sensors. Accuracy of the system is also improved by subjecting the inertial sensors to programmed single-axis gimbal motion, and by performing coarse and fine adjustments to the attitude estimates obtained by the system based on the programmed motion and on the passive thermal management of the sensors.

Inertial measurement systems, and methods of use and manufacture thereof

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Halifax, MA, United States of America

Michael F McManus