Vincent Derozier1,2, Sylvie Arnavielhe2,3, Eric Renard4, Gérard Dray1,2, Sophie Martin2,5. 1. 1 LGI2P, IMT Mines Ales, Univ Montpellier, Ales, France. 2. 2 Plateforme COGITHON MSH sud, Montpellier, France. 3. 3 Kyomed INNOV, Montpellier, France. 4. 4 Diabetology Department, CHU Lapeyronie, Montpellier, France. 5. 5 Université Paul Valéry, Montpellier, France.
Abstract
BACKGROUND: Self-monitoring blood glucose (SMBG) is facilitated by application available to analyze these data. They are mainly based on descriptive statistical analyses. In this study, we are proposing a method inspired by artificial intelligence algorithm for displaying glycemic data in an intelligible way with high-level information that is compatible with the short duration allocated to medical visits. METHOD: We propose a display method based on a numerical glycemic data conversion using a qualitative color scale that exhibits the patient's overall glycemic state. Moreover, a machine learning algorithm inputs these displays to exhibit recurrent glycemic pattern over configurable extended time period. RESULTS: A demonstrator of our method, output as a glycemic map, could be used by the physician during quarterly patient consultations. We have tested this methodology retrospectively on a database in order to observe the behavior of our algorithm. In some data files we were able to highlight some of the glycemic patterns characteristics that remain invisible on the tabular representations or through the use of descriptive statistic. In a next step the interpretation will have to be done by physicians to confirm they underlie knowledge. CONCLUSIONS: Our approach with artificial intelligence algorithm paired up with graphical color display allow a large database fast analysis to provide insights on diabetes knowledge. The next steps are first to set up a clinical trial to validate this methodology with dedicated patients and physicians then we will adapt our methodology for the huge data sets generated by continuous glycemic measurement (CGM) devices.
BACKGROUND: Self-monitoring blood glucose (SMBG) is facilitated by application available to analyze these data. They are mainly based on descriptive statistical analyses. In this study, we are proposing a method inspired by artificial intelligence algorithm for displaying glycemic data in an intelligible way with high-level information that is compatible with the short duration allocated to medical visits. METHOD: We propose a display method based on a numerical glycemic data conversion using a qualitative color scale that exhibits the patient's overall glycemic state. Moreover, a machine learning algorithm inputs these displays to exhibit recurrent glycemic pattern over configurable extended time period. RESULTS: A demonstrator of our method, output as a glycemic map, could be used by the physician during quarterly patient consultations. We have tested this methodology retrospectively on a database in order to observe the behavior of our algorithm. In some data files we were able to highlight some of the glycemic patterns characteristics that remain invisible on the tabular representations or through the use of descriptive statistic. In a next step the interpretation will have to be done by physicians to confirm they underlie knowledge. CONCLUSIONS: Our approach with artificial intelligence algorithm paired up with graphical color display allow a large database fast analysis to provide insights on diabetes knowledge. The next steps are first to set up a clinical trial to validate this methodology with dedicated patients and physicians then we will adapt our methodology for the huge data sets generated by continuous glycemic measurement (CGM) devices.
Authors: William H Polonsky; Lawrence Fisher; Charles H Schikman; Deborah A Hinnen; Christopher G Parkin; Zhihong Jelsovsky; Bettina Petersen; Matthias Schweitzer; Robin S Wagner Journal: Diabetes Care Date: 2011-02 Impact factor: 19.112