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Data Anonymization for Pervasive Health Care: Systematic Literature Mapping Study
In addition, inspired by the survey study by Zigomitros et al [62], we also included image data because an EHR is essentially a 2 D data matrix and thus could be viewed as a 2 D image and anonymized using statistical and computer vision techniques.
Relational data [62] are the most common type of data. This category usually contains a fixed number of variables (ie, columns) and data records (ie, rows).
JMIR Med Inform 2021;9(10):e29871
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A study by Rose et al [18] discussed the correlation between RBS and Hb A1c levels. Stanley et al [19] used a linear regression model for imputation of missing Hb A1c data. Their model calculates Hb A1c levels for patient records with missing Hb A1c values as continuous and categorical values and uses 4 predictors extracted from an EHR system—RBS, FBS, age, and gender—as predictors to calculate the level of Hb A1c for a diabetic population.
JMIR Med Inform 2021;9(5):e25237
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A study by Huang et al [12] showed that patients with Hb A1c levels of 5.7% to 6.5% are likely to develop diabetes in 2.49 years. Not only that, but the trend of the Hb A1c test has been shown to be an important factor for predicting mortality for patients with T2 DM [13]. Furthermore, nondiabetic people with an elevated Hb A1c level have an increased risk of cardiovascular disease [9,14].
JMIR Med Inform 2020;8(7):e18963
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