Bio-medical Ontologies Maintenance and Change Management

306 M. Fernandez, M. Villasana, and D. Streja
RA (mg/min)
y 2 (μU/ml)
y 1 (mg/dl)
1000
500
OMI
0
−100 0 100 200 300 0
30
20
10
0
−100 0 100 200 300 0
400
300
200
Data
LMM
MM
100
−100 0 100
Time (mins)
200 300
10
5
1.5
1
0.5
Glucose load (g/5 min)
IR (U/5 min)
RA (mg/min)
y 1 (mg/dl)
y 2 (μU/ml)
400
300
200
100
NMI
0
−100 0 100 200 300 0
8
6
4
2
−100 0 100 200 300 0
100
−100 0 100
Time (mins)
200 300
Fig. 7. LMM and MM fit to data obtained experimentally from patient 1, for the old (left) and
new (right) model inputs)
LMM show residuals that are correlated (p-value < 0.01) and the Anderson-Darling
test shows evidence that 9 out of 13 (69%) windows have random residuals (p-value
< 0.05), whilst residuals for 4 windows (31%) are not random (p-value > 0.05).
For the MM also correlation is found for all windows (p-value < 0.01), and the
Table 1. The R 2 values for the LMM and MM after they have been fitted to the experimental
data using the old and new input models (OIM and NIM respectively)
400
300
200
LMM MM
OIM NIM OIM NIM
0.9895 0.9822 0.9927 0.9876
0.9562 0.9649 0.9537 0.9685
0.8405 0.9110 0.8463 0.8894
0.7845 0.7318 0.8611 0.7191
0.8312 0.8515 0.9870 0.8462
0.7607 0.8428 0.8100 0.8900
0.8002 0.6647 0.7907 0.6647
0.8727 0.8811 0.8687 0.8789
0.9692 0.9631 0.9719 0.9678
0.9392 0.9614 0.8635 0.8467
0.8753 0.5754 0.8710 0.5826
0.8750 0.5342 0.9044 0.5623
0.8128 0.8287 0.7868 0.8322
Data
LMM
MM
8
6
4
2
1
Glucose load (g/5 min)
1.5
0.5
IR (U/5 min)