View source for 20240033419. METHOD AND MEANS FOR POSTPRANDIAL BLOOD GLUCOSE LEVEL PREDICTION simplified abstract (Roche Diabetes Care, Inc.)

=METHOD AND MEANS FOR POSTPRANDIAL BLOOD GLUCOSE LEVEL PREDICTION=


==Organization Name==

[[:Category:Roche Diabetes Care, Inc.|Roche Diabetes Care, Inc.]]


[[Category:Roche Diabetes Care, Inc.]]

==Inventor(s)==

[[:Category:Daniel Adelberger of Linz (AT)|Daniel Adelberger of Linz (AT)]][[Category:Daniel Adelberger of Linz (AT)]]

[[:Category:Luigi Del Re of Linz (AT)|Luigi Del Re of Linz (AT)]][[Category:Luigi Del Re of Linz (AT)]]

[[:Category:Florian Reiterer of Vandans (AT)|Florian Reiterer of Vandans (AT)]][[Category:Florian Reiterer of Vandans (AT)]]

[[:Category:Christian Ringemann of Mannheim (DE)|Christian Ringemann of Mannheim (DE)]][[Category:Christian Ringemann of Mannheim (DE)]]

[[:Category:Patrick Schrangl of Linz (AT)|Patrick Schrangl of Linz (AT)]][[Category:Patrick Schrangl of Linz (AT)]]

==METHOD AND MEANS FOR POSTPRANDIAL BLOOD GLUCOSE LEVEL PREDICTION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240033419 titled 'METHOD AND MEANS FOR POSTPRANDIAL BLOOD GLUCOSE LEVEL PREDICTION

==Simplified Explanation==

The abstract describes a computer-implemented method for predicting blood glucose levels, specifically postprandial blood glucose levels. The method involves receiving a first medical data set of a patient, which includes glucose data and other medical data. From this data set, a second medical data set is extracted, which is a subset of the first data set. The extraction process includes identifying and removing duplicates, data values above or below predefined thresholds, data values that differ significantly from expected values, incomplete data, and time-dependent data patterns. The extracted second data set is then used as input for a blood glucose level prediction model, which predicts future blood glucose levels based on the second data set.

* The method involves receiving a first medical data set of a patient, which includes glucose data and other medical data.
* A second medical data set is extracted from the first data set, which is a subset of the first data set.
* The extraction process includes identifying and removing duplicates, data values above or below predefined thresholds, data values that differ significantly from expected values, incomplete data, and time-dependent data patterns.
* The extracted second data set is used as input for a blood glucose level prediction model.
* The blood glucose level prediction model predicts future blood glucose levels based on the second data set.

Potential applications of this technology:
* Personalized diabetes management: The method can be used to predict postprandial blood glucose levels for individuals with diabetes, allowing for personalized management and treatment plans.
* Continuous glucose monitoring: The method can be integrated with continuous glucose monitoring devices to provide real-time predictions and alerts for blood glucose levels.
* Clinical research: The method can be used in clinical research studies to analyze and predict blood glucose levels in patient populations.

Problems solved by this technology:
* Inaccurate blood glucose predictions: The method addresses issues such as duplicate data, outliers, incomplete data, and time-dependent data patterns, which can affect the accuracy of blood glucose level predictions.
* Time-consuming data preprocessing: The method automates the process of extracting and preprocessing medical data, saving time and effort for healthcare professionals.
* Individualized prediction: The method allows for personalized blood glucose level predictions based on an individual's medical data, taking into account their unique characteristics and patterns.

Benefits of this technology:
* Improved diabetes management: Accurate blood glucose level predictions can help individuals with diabetes make informed decisions about their diet, medication, and lifestyle choices.
* Early detection of abnormal glucose levels: The method can provide early warnings for high or low blood glucose levels, allowing for timely interventions and preventing complications.
* Enhanced research capabilities: The method enables researchers to analyze and predict blood glucose levels in large patient populations, contributing to advancements in diabetes management and treatment.


==Original Abstract Submitted==

a method for predicting blood glucose levels, in particular, for postprandial blood glucose level prediction, the method being computer-implemented and comprising: receiving a first medical data set of a patient covering a time range, the first medical data set comprising glucose data and further other medical data of the patient, extracting a second medical data set from the first medical data set, wherein the second medical data set is a subset of the first medical data set and wherein the extracting comprises at least one of: identifying duplicates in the first medical data set and removing identified duplicates, identifying data values that lie above a predefined maximum threshold data value or identifying data values that lie below a predefined minimum threshold data value and removing data associated with the identified data values, identifying data values that differ from predetermined expected data values by more than a predetermined amount and removing data associated with the identified data values, identifying incomplete data for which data values are missing and removing identified incomplete data, identifying at least one predetermined time-dependent data pattern and removing data associated with the identified time-dependent data pattern, providing the extracted second medical data set as input to a blood glucose level prediction model, and predicting future blood glucose levels of the patient using the output of the blood glucose level prediction model based on the second medical data set.



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