Microsoft Technology Licensing, LLC

Jeremiah Wander

Daniel Morris

Sumit Basu

Gregory Ray Smith

Sidhant Gupta

T Scott Saponas

Jonathan Bernard Lester

Jessica De Souza

A method for determining a risk of decompensated heart failure in a user includes receiving a first set of data that is fixed with respect to time. A machine-learning model generates one or more initial risk factors based on the first set of data. A second set of data for the user that dynamically updates over time is received from a wearable cardiovascular physiology monitor. The machine-learning model is used to generate dynamic data classifiers based on the one or more initial risk factors. Aggregate risk scores for the user are then indicated based on an evaluation of the second set of data against the dynamic data classifiers. In this way, static electronic medical records may be combined with dynamic, real-time data from wearable cardiovascular physiology monitors to provide an accurate and continuously updating risk of decompensated heart failure for a user.

Machine-learning models for predicting decompensation risk

A photoplethysmogram device is provided comprising a light source configured to emit light to illuminate skin, a photo-detector configured to receive the light illuminating the skin and generate an electrical output as a function of an intensity of the received light, a skin temperature regulator configured to heat and/or cool a temperature of the skin adjacent to the photo-detector and light source to increase the signal-to-noise ratio (SNR) of the electrical output from the photo-detector, and a processor configured to generate, based on the electrical output, an output signal indicative of blood properties, including physiological parameters such as blood pressure, heart rate, stroke volume, cardiac output, total peripheral resistance, blood vessel elasticity, and arterial oxygen saturation.

Photoplethysmogram device with skin temperature regulator

A first data window of a pulse waveform signal comprising a first number of samples is analyzed to determine a level of confidence that a pulse sensing device is placed correctly. If an initial level of confidence is met, the user is given positive feedback, and a second data window of a pulse waveform signal comprising a second, larger number of samples is analyzed. If an increased level of confidence is met, the user is given increased positive feedback. If a level of confidence is not met, the user is given negative feedback. If a final level of confidence is met, the user is given feedback that the pulse sensing device is placed correctly.

Wearable pulse sensing device signal quality estimation

According to one aspect of the present disclosure, a method for estimating blood pressure is provided, comprising training a machine learning model on a cohort data set. The cohort data set may include subject-specific contextual data, time-varying features, and blood pressure measurements for a plurality of subjects. The method may include receiving contextual data for a specific subject, wherein the contextual data includes medical history data of the subject. The method may further include personalizing the machine learning model to the subject based on the contextual data. The method may include calibrating the machine learning model to the subject based on a set of time-varying features and blood pressure measurements of the subject. In addition, the method may include using the machine learning model and the time-varying features for the subject to generate a blood pressure estimate.

Growing community of inventors

Seattle, WA, United States of America

Jeremiah Wander