Prediction of Arterial Blood Gases(ABG) by Using Neural Network In Trauma Patients Milad Shayan Mohammad Sabouri Dr. Shahram Paydar Leila Shayan ACRRL Applied Control & Robotics Research Laboratory of Shiraz University Department of Power and Control Engineering, Shiraz University, Fars, Iran. https://sites.google.com/view/acrrl/

1. http://www.free-powerpoint-templates-design.com
Prediction of Arterial Blood
Gases(ABG) by Using Neural
Network In Trauma Patients
Milad Shayan
Mohammad Sabouri
Dr. Shahram Paydar
Leila Shayan

2. Dr. Shahram Paydar
Leila Shayan
Milad Shayan
Mohammad Sabouri
Use of artificial intelligence and neural network algorithms to predict arterial
blood gas items in trauma victims
Milad Shayan, Mohammad Sabouri, Leila Shayan, Shahram Paydar
doi: https://doi.org/10.1101/2020.03.14.20035584
Applied Control & Robotics Research Laboratory of Shiraz University
https://sites.google.com/view/acrrl/home

3. 1
2
3
4
Table of
Contents
Introduction
Medical description and our goal and phase alignment
Data Preprocessing
Choose I/O and What we do
Neural Network
Initial design and prediction
Result
Analysis and result of 1st phase
3

4. 1
INTRODUCTION

5. Trauma and ABG
5
Traumatic injury
Serious injury to the body, as from physical violence or an accident
Psychological trauma
Severe emotional or mental distress caused by an experience
Arterial Blood Gases
An ABG is a blood test that measures the acidity, or pH, and the levels of oxygen (O2)
and carbon dioxide (CO2) and HCO3 from an artery

6. 01
02
03
3 Phase of Project
6
ABG
Arterial Blood Gases include
PH & PO2 & PCO2 & HCO3
BE
Base Excess or Base Deficit
Fibrinogen
Level of Fibrinogen
One of the Coagulating factor of blood

7. Reason of Prediction of ABG
On arrival
Age, Gender, Vital signs
After 1 Hour
First ABG and the other
parameter
After 2 Hour
Second ABG and Required
parameter
Transfer to
Surgery room, CCU, ICU & …
7

8. 2
DATA
PREPROCESSING

9. Our Step
Del/Rep Missing
Low Missing
Use Average
Correct Data
Scatter chart
Normalize Data
[0,1]
Export to NN
Ready to use
9

10. Normalize Data
𝑁𝑜𝑟𝑚𝑎𝑙𝑖 =
𝑋𝑖 − 𝑀𝑖𝑛𝑖
𝑀𝑎𝑥𝑖 − 𝑀𝑖𝑛𝑖
Formula 1
12

11. Input/Output Information
13
Row data Normalize from normal distribution Normalize from row data
variance average min max variance average min max variance average min max
input
Age 17.70459 35.925439 2 95 0.166769 0.5299989 0 1 0.190372 0.36479 0 1
Blood pressure systol 21.7673 125.84837 40 210 0.128043 0.5049904 0 1 0.128043 0.50499 0 1
Blood pressure dystol 14.68896 79.112782 20 146 0.116579 0.4691491 0 1 0.116579 0.469149 0 1
Respiratory Rate 3.62063 19.620301 8 45 0.097855 0.3140622 0 1 0.097855 0.314062 0 1
Pulse Rate 22.36556 100.48183 17 190 0.129281 0.4825539 0 1 0.129281 0.482554 0 1
output
pH 0.122997 7.3606266 6.69 9.94 0.037845 0.2063466 0 1 0.037845 0.206347 0 1
Pressure of CO2 10.39696 40.989286 11 99 0.118147 0.3407873 0 1 0.118147 0.340787 0 1
Pressure of O2 41.10154 44.673058 0.4 333.3 0.122121 0.6550968 0 1 0.123465 0.132992 0 1
Hco3 4.636405 22.924311 3.4 48.5 0.102803 0.4329115 0 1 0.102803 0.432912 0 1

12. 3
NEURAL NETWORK

13. Table of Neural Network
15
name of
network
network type
training
function
adaption learning
function
performance
function
number of
layers
number of
neurons
transfer
function
network1 feed-forward backprop trainLM LearnGDM MSE 3 50 logsig
network2 feed-forward backprop trainCGB LearnGD MSE 3 50 logsig
network3 feed-forward backprop trainCGB LearnGDM MSEREG 3 50 logsig
network4 feed-forward backprop trainCGB LearnGD SSE 3 40 tansig
network5 feed-forward backprop trainCGB LearnGDM MSEREG 3 40 tansig
network6 feed-forward backprop trainCGF LearnGD SSE 3 40 logsig
network7 feed-forward backprop trainCGF LearnGDM MSEREG 3 40 tansig
network8 feed-forward backprop trainBR LearnGDM MSEREG 3 30 tansig
network9 feed-forward backprop trainSCG LearnGDM MSEREG 3 40 logsig
network10 feed-forward backprop trainRP LearnGDM MSEREG 3 50 logsig

14. 4
RESULT

15. Table of Result
17
name of
network
Type of Data
Accuracy of Learn Accuracy of Test Average of Absolute Errors
ph pco2 po2 hco3 Total ph pco2 po2 hco3 Total ph pco2 po2 hco3
network1
Normalize Data 89.77% 74.77% 86.43% 83.13% 83.52% 90.06% 72.65% 88.09% 81.18% 83.00%Learn 0.069 7.5676 23.981 3.2935
Real Data 99.07% 81.54% 46.32% 85.63% 78.14% 99.10% 80.09% 48.73% 84.06% 78.00%Test 0.066 8.0582 25.998 3.5374
network2
Normalize Data 89.90% 75.71% 86.83% 83.72% 84.04% 89.67% 68.01% 86.39% 79.50% 80.89%Learn 0.077 7.7695 23.287 3.378
Real Data 99.08% 82.23% 47.87% 86.13% 78.83% 99.07% 76.71% 44.42% 82.64% 75.71%Test 0.068 9.4247 28.187 3.8533
network3
Normalize Data 89.92% 76.22% 86.94% 83.69% 84.19% 89.23% 70.15% 86.77% 79.51% 81.41%Learn 0.068 7.1319 23.173 3.1843
Real Data 99.08% 82.60% 48.13% 86.11% 78.98% 99.03% 78.27% 46.78% 82.65% 76.68%Test 0.071 8.7944 26.99 3.8522
network4
Normalize Data 89.63% 74.58% 86.48% 83.13% 83.46% 89.05% 72.57% 88.59% 81.38% 82.90%Learn 0.07 7.6233 23.911 3.294
Real Data 99.06% 81.40% 46.48% 85.63% 78.14% 99.01% 80.03% 50.43% 84.23% 78.42%Test 0.073 8.0824 25.137 3.5006
network5
Normalize Data 88.73% 74.23% 86.29% 82.84% 83.02% 89.74% 72.48% 88.97% 81.57% 83.19%Learn 0.076 7.7287 24.164 3.3496
Real Data 98.97% 81.14% 45.91% 85.39% 77.85% 99.07% 79.96% 51.03% 84.39% 78.62%Test 0.068 8.1074 24.836 3.4644
network6
Normalize Data 89.12% 74.36% 86.29% 83.01% 83.20% 89.61% 72.90% 88.93% 81.61% 83.26%Learn 0.073 7.69 24.122 3.3174
Real Data 99.01% 81.24% 46.00% 85.53% 77.94% 99.06% 80.27% 50.94% 84.43% 78.68%Test 0.069 7.9844 24.878 3.4561
network7
Normalize Data 89.59% 74.75% 86.37% 83.17% 83.47% 90.13% 71.72% 88.70% 81.03% 82.90%Learn 0.07 7.5738 24.112 3.2861
Real Data 99.05% 81.52% 46.03% 85.67% 78.07% 99.11% 79.41% 50.31% 83.94% 78.19%Test 0.065 8.3327 25.199 3.5651
network8
Normalize Data 89.83% 74.75% 86.47% 83.06% 83.53% 90.51% 72.53% 88.63% 81.70% 83.34%Learn 0.068 7.572 23.96 3.3083
Real Data 99.07% 81.53% 46.37% 85.57% 78.13% 99.14% 80.00% 50.35% 84.51% 78.50%Test 0.063 8.0926 25.179 3.4393
network9
Normalize Data 89.65% 74.40% 86.49% 83.01% 83.39% 90.10% 72.42% 88.42% 81.59% 83.13%Learn 0.069 7.6787 23.883 3.317
Real Data 99.06% 81.27% 46.54% 85.53% 78.10% 99.11% 79.91% 49.61% 84.41% 78.26%Test 0.066 8.1274 25.554 3.4602
network10
Normalize Data 88.55% 74.36% 86.32% 82.82% 83.01% 88.42% 71.48% 88.12% 80.72% 82.18%Learn 0.077 7.6893 24.101 3.3541
Real Data 98.96% 81.24% 46.05% 85.37% 77.90% 98.96% 79.24% 41.01% 83.67% 75.72%Test 0.077 8.4018 29.914 3.6241

16. Real Data
𝑁𝑜𝑟𝑚𝑎𝑙𝑖 =
𝑋𝑖 − 𝑀𝑖𝑛𝑖
𝑀𝑎𝑥𝑖 − 𝑀𝑖𝑛𝑖
Formula 2
18
⇒ 𝑋𝑖 = 𝑁𝑜𝑟𝑚𝑎𝑙𝑖 × 𝑀𝑎𝑥𝑖 − 𝑀𝑖𝑛𝑖 + 𝑀𝑖𝑛𝑖
Formula 3 %𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 1 −
𝐴𝑣𝑔 |𝑋𝑖 − 𝑋
∧
𝑖| 𝑛
𝐴𝑣𝑔 𝑋𝑖
× 100

17. Thank You for Watching!