US 12,440,170 B1
Forecasting acute inflammatory condition and decision support tool
Douglas S. McNair, Seattle, WA (US)
Assigned to Cerner Innovation, Inc., Kansas City, MO (US)
Filed by CERNER INNOVATION, INC., North Kansas City, MO (US)
Filed on Jun. 27, 2022, as Appl. No. 17/850,632.
Application 17/850,632 is a continuation of application No. 16/237,201, filed on Dec. 31, 2018, granted, now 11,395,635.
Claims priority of provisional application 62/612,421, filed on Dec. 30, 2017.
This patent is subject to a terminal disclaimer.
Int. Cl. A61B 5/00 (2006.01); A61B 5/0205 (2006.01); G16H 50/50 (2018.01); A61B 5/021 (2006.01); A61B 5/024 (2006.01); A61B 5/08 (2006.01)
CPC A61B 5/7275 (2013.01) [A61B 5/0205 (2013.01); A61B 5/412 (2013.01); G16H 50/50 (2018.01); A61B 5/021 (2013.01); A61B 5/024 (2013.01); A61B 5/0816 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A non-transitory computer-readable media having computer-executable instructions embodied thereon that when executed by one or more processors of a system for forecasting an emergent acute inflammatory condition or event in a patient, provide a decision support system for predicting emergence of an acute inflammatory condition, the instructions comprising:
acquiring, by the one or more processors, using one or more patient monitoring devices comprising a sensor, a plurality of measurements of vital signs for the patient, the plurality of measurements of vital signs acquired over a timespan and not acquired on a periodic basis;
constructing, by the one or more processors, a vital signs time series using the plurality of measurements of vital signs;
determining, by the one or more processors, a likelihood of the patient experiencing an occurrence of the acute inflammatory condition over a future timeframe;
based on the determined likelihood, automatically initiating, by the one or more processors, a response action indicating a patient's condition, and automatically modifying a computer code executed in a healthcare software program for treating the patient by increasing a frequency of obtaining physiological measurements of the patient or by increasing a sensitivity of monitoring physiological changes in the patient, thereby transforming the healthcare software program; and
determining a linear model and a cusp catastrophe model based on the vital signs time series.