Figure 1.
Depicted above are the two different dynamical phenotyping strategies, directive dynamical phenotyping where the population is stratified and then characterized by differences in dynamics, and undirected dynamical phenotyping where a complex population is stratified by differences in dynamics.
Table 1.
Full list of parameters for the glucose/insulin model [4] used in this paper; note that these are the model parameters we us in this paper.
Figure 2.
Depicted above are: (a) the histogram of the raw TDMI of glucose time series for hrs for the population of
patients; (b) the
mode FBC model of the TDMI distribution; (c) the
mode FBC model of the TDMI distribution; (d) the
mode FBC model of the TDMI distribution; (e) variation in the distribution (as quantified by the mean and variance) of the log-likelihood for models with
–
modes.
Figure 3.
Depicted above are: (a) the glucose time series of an individual with high TDMI, , in the
hrs bin — this individual falls into cluster two; (b) the glucose time series of an individual with low TDMI,
, in the
hrs bin — this individual falls into cluster one.
Figure 4.
Depicted above are: (a) KDE of the length of individual records; (b) KDE of the number of measurements per individual; (c) KDE of the mean glucose per record.
Figure 5.
Parameter variation plot versus predictability (TDMI) for selected parameters.
Figure 6.
Depicted above are: (a) glucose time series for three different values of a linear constant affecting IIGU, ; (b) glucose time series density for three different values of a linear constant affecting IIGU
.
Figure 7.
Depicted above are: (a) glucose time series for three different values of an exponential constant affecting insulin secretion, ; (b) glucose time series density for three different values of an exponential constant affecting insulin secretion,
.
Figure 8.
Depicted above are: (a) glucose time series for three different values of a time constant for plasma insulin degradation (via kidney and liver filtering), ; (b) glucose time series density for three different values of a time constant for plasma insulin degradation (via kidney and liver filtering),
.
Figure 9.
Depicted above are: (a) glucose time series for three different values of the delay rate between plasma insulin and glucose production, ; (b) glucose time series density for three different values of the delay rate between plasma insulin and glucose production,
.
Figure 10.
Depicted above are: (a) glucose time series for three different values of a linear constant affecting IDGU, ; (b) glucose time series density for three different values of a linear constant affecting IDGU,
.
Table 2.
Summary of the effects of various key parameters on the glucose dynamics, and TDMI that are observed when varying a parameter from below the nominal value to
above the nominal value.
Figure 11.
Depicted above are: the variations in TDMI for insulin secretion, , and kidney/liver function,
, when varied by up to
of their nominal values.
Note that both undergo at least one bifurcation (qualitative state change) over this variation in parameters.
Figure 12.
Depicted above are: (a) glucose time series for different values of the constant affecting insulin secretion, ; (b) glucose time series density for different values of the constant affecting insulin secretion,
.
Figure 13.
Depicted above are: (a) glucose time series for different values of the constant affecting kidney/liver function, ; (b) glucose time series density for different values of the constant affecting kidney/liver function,
.