	US 12,220,263 B2
	Physiological property forecasting
Amir Hossein Hayeri, Vancouver (CA); Ricardo Cacho, Vancouver (CA); Suvan Ashok Ramchandani, Vancouver (CA); Nicolas Brandt, Vancouver (CA); and Aleksey Sher, Vancouver (CA)
Assigned to BIO-CONSCIOUS TECHNOLOGIES INC., Vancouver (CA)
Appl. No. 16/650,581
Filed by BIO-CONSCIOUS TECHNOLOGIES INC., Vancouver (CA)
PCT Filed Sep. 26, 2018, PCT No. PCT/CA2018/051213 § 371(c)(1), (2) Date Mar. 25, 2020, PCT Pub. No. WO2019/060991, PCT Pub. Date Apr. 4, 2019.
Claims priority of provisional application 62/614,047, filed on Jan. 5, 2018.
Claims priority of provisional application 62/563,078, filed on Sep. 26, 2017.
Prior Publication US 2020/0229772 A1, Jul. 23, 2020
Int. Cl. A61B 5/00 (2006.01); A61B 5/024 (2006.01); A61B 5/145 (2006.01); G16H 40/63 (2018.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01)

CPC A61B 5/7275 (2013.01) [A61B 5/024 (2013.01); A61B 5/14532 (2013.01); G16H 40/63 (2018.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01)]

10 Claims

1. A computer-implemented method of facilitating physiological property forecasting for detecting disease complications, the method comprising:

receiving signals representing sensed physiological property indicators, each of the sensed physiological property indicators representing a sensed physiological property of a patient at a respective time;

receiving signals representing one or more contextual indicators associated with the sensed physiological property indicators;

applying at least one classification criterion to the one or more contextual indicators to determine a patient state of a plurality of possible patient states, each patient state associated with a respective set of forecasting parameters; and

applying the set of forecasting parameters associated with the determined patient state to the sensed physiological property indicators to determine at least one forecast physiological property indicator representing a forecast physiological property of the patient at a future time;

wherein each of the sets of forecasting parameters includes a plurality of sets of historical physiological property indicators, each of the sets of historical physiological property indicators representing physiological properties of the patient during a respective historical time period, and wherein applying the set of forecasting parameters associated with the determined patient state comprises:

comparing the sensed patient physiological property indicators to each of the sets of historical physiological property indicators included in the set of forecasting parameters associated with the determined patient state;

selecting at least one of the sets of historical physiological property indicators included in the set of forecasting parameters associated with the determined patient state based on said comparing;

generating a time-dependent function representing the selected at least one of the sets of historical physiological property indicators;

determining the at least one forecast patient physiological property indicator using the generated time-dependent function;

wherein comparing the sensed patient physiological property indicators to each of the sets of historical physiological property indicators comprises, for each of the sets of historical physiological property indicators included in the set of forecasting parameters associated with the determined patient state, determining a sum of differences between the sensed patient physiological property indicators and respective ones of the historical physiological property indicators included in the set of historical physiological indicators;

wherein comparing the sensed patient physiological property indicators to each of the sets of historical physiological property indicators comprises, for each of the sets of historical patient physiological property indicators included in the set of forecasting parameters associated with the determined patient state, determining a weight associated with the set of historical physiological property indicators and applying the weight to the determined sum of differences;

wherein determining the weight associated with the set of historical physiological property indicators comprises:

determining a change over time of at least some of the historical physiological property indicators included in the set;

applying a probability density function to the change to determine a probability density for the change occurring within a time period associated with the sensed physiological property indicators; and

determining the weight based on the determined probability density;

wherein each of the at least one forecast physiological property indicators includes a blood glucose value representing a forecast future blood glucose level of the patient; and

wherein the method further comprises:

adjusting, by a computer for managing insulin injection, an insulin injection to correct blood glucose levels in response to the forecast future blood glucose levels indicated by the at least one forecast physiological property indicator.