Table 1.
The overall performance of the three methods for detecting abnormal observations.
Fig 1.
Performance of the static and dynamic reference ranges according to r1 and r2.
The distribution of AUC for the three approaches (Static, Bayesian, and Approximate EM) in detection of outliers based on different combinations of r1 and r2.
Fig 2.
Performance of the static and dynamic reference ranges according to I and ni.
The distribution of AUC for the three approaches (Static, Bayesian, and Approximate EM) in detection of outliers based on different combinations of I and ni.
Table 2.
The average time (in minutes) taken by each approach to generate reference ranges baed on different combinations of I and ni.
Fig 3.
Static and dynamic reference ranges of glucose levels for a type 1 diabetic patient.
A series of 2029 longitudinal glucose levels (longdashed line) for a type 1 diabetic patient with static reference ranges (solid lines), dynamic reference ranges using the Bayesian (dotted lines) and Approximate EM (dashed-dotted lines) methods.
Table 3.
The level of agreement between the clinical and approximate EM reference ranges in classifying a sample patient’s glucose level as normal or abnormal.
Fig 4.
Static and dynamic reference ranges of free oxygen radical test for a female runner.
A series of 18 longitudinal hydroperoxides values (longdashed line) for a female athlete with static reference ranges (solid lines), dynamic reference ranges using the Bayesian (dotted lines) and Approximate EM (dashed-dotted lines) methods.
Fig 5.
Static and dynamic reference ranges of free oxygen radical test for a male runner.
A series of 14 longitudinal hydroperoxides values (longdashed line) for a male athlete with static reference ranges (solid lines), dynamic reference ranges using the Bayesian (dotted lines) and Approximate EM (dashed-dotted lines) methods.