Koninklijke Philips Corporation N.v.

Emma Holdrich Schwager

Erina Ghosh

Larry James Eshelman

Stephanie Lanius

Bryan Conroy

Srinivas Hariharan

David Paul Noren

Kailash Swaminathan

Claire Yunzhu Zhao

Mohammad Shahed Sorower

Vinod Kumar

Daniel Craig McFarlane

Chaitanya Kulkarni

Kristen Tgavalekos

Vikram Shivanna

Ting Feng

The present disclosure is directed to systems and methods for developing an individual-specific patient baseline for a target patient. An exemplary method involves: determining one or more acuity scores for the target patient; identifying patient health data corresponding to one or more low acuity time periods; storing retrospective clinical data from a group of patients in a second database; comparing the patient health data corresponding to the one or more low acuity time periods with retrospective clinical data from a group of patients by identifying one or more patient subgroups; determining the individual-specific patient baseline using an adaptive baseline selection algorithm, wherein the adaptive baseline selection algorithm is used to determine whether to determine the individual-specific patient baseline using patient health data or using retrospective clinical data from one or more patient subgroups; and displaying, using a user interface, the individual-specific patient baseline.

Methods and systems for patient baseline estimation

A machine learning model, including: a categorical input feature, having a defined set of values; a plurality of non-categorical input features; a word embedding layer configured to convert the categorical input feature into an output in a word space having two dimensions; and a machine learning network configured to receive the output of the word embedding layer and the plurality of non-categorical input features and to produce a machine learning model output.

Word embedding for non-mutually exclusive categorical data

A method for training a baseline risk model, including: pre-processing input data by normalizing continuous variable inputs and producing one-hot input features for categorical variables; providing definitions for clean input data and dirty input data based upon various input data related to a patient condition; segmenting the input data into clean input data and dirty input data, wherein the clean input data includes a first subset and a second subset, where the first subset and the second subset include all of the clean input data and are disjoint; training a machine learning model using the first subset of the clean data; and evaluating the performance of the trained machine learning model using the second subset of the clean input data and the dirty input data.

Multilayer perceptron based network to identify baseline illness risk

Growing community of inventors

Englewood, CO, United States of America

Emma Holdrich Schwager