Diabetes management therapy advisor

Patent metadata

Field	Value
Publication number	US-9886556-B2
Application number	US-201615241703-A
Country	US
Kind code	B2
Filing date	Aug 19, 2016
Priority date	Aug 20, 2015
Publication date	Feb 6, 2018
Grant date	Feb 6, 2018

Abstract

A method includes obtaining training data for a plurality of patients of a patient population. The training data includes training blood glucose history data including treatment doses of insulin administered by the patients of the patient population and one or more outcome attributes associated with each treatment dose. The method also includes identifying, for each patient of the patient population, one or more optimum treatment doses of insulin from the treatment doses yielding favorable outcome attributes. The method also includes receiving patient-state information for the treated patient, determining a next recommended treatment dose of insulin for the treated patient based on one or more of the identified optimum treatment doses associated with the patients of the patient population having training patient-state information similar to the patient-state information for the treated patient, and transmitting the next recommended treatment dose to a portable device associated with the treated patient.

First claim

What is claimed is:

1. A method for determining a treatment dose for a treated patient, the method comprising:
obtaining, at data processing hardware, training data for a plurality of patients of a patient population from memory hardware in communication with the data processing hardware, the training data inputted to the memory hardware by a medical professional computing device associated with an authorized medical professional, the training data comprising training blood glucose history data and training patient-state information for each patient of the patient population, the training blood glucose history data comprising treatment doses of insulin administered by the patients of the patient population and one or more outcome attributes associated with each treatment dose of insulin administered by the patients of the patient population, the treatment doses comprising:
meal boluses of insulin administered by each patient of the patient population during each of multiple scheduled time intervals throughout a day; and
basal doses of insulin administered by each patient of the patient population;

for each patient of the patient population, identifying, using the data processing hardware, optimum treatment doses of insulin from the treatment doses of insulin yielding favorable outcome attributes, the identified optimum treatment doses comprising:
an optimum meal bolus of insulin associated with each scheduled time interval; and
an optimum basal dose of insulin;

receiving, at the data processing hardware, patient-state information for the treated patient;
determining, using the data processing hardware, a next recommended treatment dose of insulin for the treated patient based on one or more of the identified optimum treatment doses associated with patients of the patient population having training patient-state information similar to the patient-state information for the treated patient; and
transmitting the next recommended treatment dose of insulin to an administration device in communication with the data processing hardware, the administration device comprising:
a doser; and
an administration computing device in communication with the doser, the administration computing device configured to automatically dial in a number of units of insulin for the next recommended treatment dose of insulin and cause the doser to administer the number of units of insulin for the next recommended treatment dose of insulin to the treated patient,

wherein determining the next recommended treatment dose of insulin for the treated patient comprises:
determining the treated patient requires insulin based on the patient-state information for the treated patient;
receiving meal boluses of insulin previously administered by the treated patient during a scheduled time-interval; and
determining a next recommended meal bolus during the scheduled time interval for the treated patient based on the identified optimum meal bolus of insulin associated with the scheduled time interval and the received meal boluses of insulin previously administered by the treated patient during the scheduled time interval.

2. The method of claim 1 , wherein obtaining the training data comprises obtaining the training data periodically at an end of a re-occurring configurable time interval.

3. The method of claim 1 , wherein obtaining the training data comprises obtaining the training data immediately in response to a user input selecting an immediate start button displayed upon a display in communication with the data processing hardware.

4. The method of claim 1 , wherein obtaining the training data comprises obtaining the training data on a selected date.

5. The method of claim 1 , wherein the scheduled time interval comprises a pre-breakfast time interval, a pre-lunch time interval, a pre-dinner time interval, a bedtime time interval, or a midsleep time interval.

6. The method of claim 1 , wherein the one or more outcome attributes of the training blood glucose history data comprises a blood glucose percent error based on a function of a next scheduled blood glucose measurement and a blood glucose target range, the next scheduled blood glucose measurement corresponding to a blood glucose measurement occurring after administration of a corresponding treatment dose of insulin.

7. The method of claim 1 , wherein determining the next recommended treatment dose of insulin for the treated patient comprises:
determining the treated patient requires insulin based on the patient-state information for the treated patient;
receiving basal doses of insulin previously administered by the treated patient; and
determining a next recommended basal dose for the treated patient based on at least one of the identified optimum basal dose and the received basal doses of insulin previously administered by the treated patient.

8. The method of claim 1 , further comprising transmitting the next recommended treatment dose to a portable device associated with the treated patient, the portable device configured to display the next recommended insulin dose.

9. The method of claim 1 , further comprising:
receiving, at the data processing hardware, patient-state information for a new treated patient;
determining, by the data processing hardware, anti-diabetic medications are usable for treating the new treated patient based on the patient-state information for the new treated patient;
receiving, at the data processing hardware, a glycated hemoglobin measurement of the new treated patient; and
determining, by the data processing hardware, an anti-diabetes medication regimen for the new treated patient based on the glycated hemoglobin measurement and the training data.

10. The method of claim 9 , wherein the training data further comprises anti-diabetes medication dose-combinations administered by patients of the patient population and a glycated hemoglobin measurement associated with each anti-diabetes medication regimen.

11. The method of claim 1 , wherein the patient-state information comprises a plurality of patient-state attributes associated with the patient, the patient-state attributes including one or more of an age, a gender, a medical history, a body mass index, a medical history, risk factors, and/or financial attributes.

12. A system comprising:
a dosing controller including data processing hardware and memory hardware in communication with the data processing hardware, the dosing controller:
obtaining training data for a plurality of patients of a patient population from the memory hardware, the training data inputted to the memory hardware by a medical professional computing device associated with an authorized medical professional, the training data comprising training blood glucose history data and training patient-state information for each patient of the patient population, the training blood glucose history data comprising treatment doses of insulin administered by the patients of the patient population and one or more outcome attributes associated with each treatment dose of insulin administered by the patients of the patient population, the treatment doses comprising:
meal boluses of insulin administered by each patient of the patient population during each of multiple scheduled time intervals throughout a day; and
basal doses of insulin administered by each patient of the patient population;

for each patient of the patient population, identifying one or more optimum treatment doses of insulin from the treatment doses of insulin yielding favorable outcome attributes, the identified optimum treatment doses comprising:
an optimum meal bolus of insulin associated with each scheduled time interval; and
an

Assignees

• Aseko Inc

Inventors

• Booth Robert C
• Hebblewhite Harry

Classifications

• G16H20/10

  relating to drugs or medications, e.g. for ensuring correct administration to patients · CPC title

• G16H50/20

  for computer-aided diagnosis, e.g. based on medical expert systems · CPC title

• G16H10/60

  for patient-specific data, e.g. for electronic patient records · CPC title

• G16H50/00

  ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics · CPC title

• G16H20/60

  relating to nutrition control, e.g. diets · CPC title