Abstract
Background: Trauma is the third leading cause of death in the world and the
first cause of death among people younger than 44 years. In traumatic patients,
arterial blood gas (ABG) can provide physicians with important information
such as detecting the extent of internal damage, especially in the lung. The
equipment required to measure these gases is also expensive, which is why
most hospitals do not have this equipment. Thus, estimating these gases
without clinical trials can save the lives of traumatic patients and accelerate
their recovery. Methods: In this study, a method is presented based on
artificial neural networks to estimate and predict the arterial blood gas through
collecting information about 2280 traumatic patients. In the proposed method,
the neural network can only predict the amount of these gases from the
patient's initial information. Results: The results showed 87.92% accuracy in
predicting the arterial blood gas. The predicted arterial blood gases included
PH, PCO2, and HCO3, showing accuracy of 99.06%, 80.27%, and 84.43%,
respectively. Thus, the proposed method has relatively good accuracy in
predicting arterial blood gas. Conclusions: Given that this is the first study to
predict arterial blood gas using initial patient information, the proposed
method could be a useful tool in assisting hospital and laboratory specialists.
26 and 27 November 2020, Amirkabir University of Technology(Tehran Polytechnic) 2

Introduction
26 and 27 November 2020, Amirkabir University of Technology(Tehran Polytechnic)
• Trauma is the third leading cause of death in the world and the first
cause of death among people younger than 44 years.
• Arterial blood gas (ABG) investigation was proposed and made
accessible by Clark-Caverker almost 50 years prior.
• Examination and monitoring of these gases (ABGs) significantly
affect the administration of intensive care.
• ABG is a gold standard
• Measurement of blood pH and oxygen pressure and arterial carbon
dioxide determine the patient's respiratory and metabolic
condition.
3

Introduction
• The blood sample expected to test for ABGs can be acquired
through superficial arteries or a fixed blood vessel line to help
embed a catheter into the artery. The basic thing about blood vessel
biopsy is that medical nurses do the greater part of the cases.
Nurses ought to have adequate data over the meddling variables in
the blood test that can twist the consequences of the test. Among
the essential confounding factors are the time of blood storage and
the temperature of blood storage.
• Additionally, given the strategies accessible in blood vessel biopsy
sometimes, there is a requirement for a few ordinary endeavors
that may prompt painful difficulties, for the most part when
performed on the radial artery, including the hematoma, bleeding,
aneurysm, and distal ischemia
4

History and literature review
• Fabian et al. study (2005): comparing artificial neural networks
and logistic regression for mortality in patients with sepsis.
• Sadegh Kara et al.(2006): used the artificial neural network to
diagnose the optic nerve disease.
• Honarmand and co-workers (2008): connection between the
blood gases from the earlobe and the ABGs.
• Jutisoni et al. (2012): used a neural network to predict heart
disease in using a dataset prepared by Raj Kumar (2010).
• Wajs et al. (2016) : predict arterial blood gas using neural network
and arterial gas data obtained from repeated tests over specified
time intervals.
• Boulain et al., (2016): predict ABGs from venous blood gases, did
not yield favorable results.
• Zwisler et al. (2019): the arterial blood gases in order to determine
the amount of these gases.
5

In this paper, we present an artificial neural-network-based
way to deal with anticipate ABGs dependent on clinical
pointers and vital signs (VSs) that are estimated and gotten
at confirmation.
6

Profile of study

Data Collection
• Trauma Research Center of Shahid Rajaee (Emtiaz)
Hospital in Shiraz.
• 2016-2017 (1395-1396)
• Included 2280 patients
• 374 female
• 1906 male
 Clinical signs include diastolic blood pressure (DBP),
systolic blood pressure (SBP), pulse rate (PR),
respiratory rate (RR), and paraclinical lists include: PH,
PCO2, HCO3
8

Data Pre Processing
• data distribution is normalized to improve the training
process in the artificial neural network and then to use the
formula (1) approx. The data are normalized and are in the
interval [0,1]. The data normalization steps were performed
in Stata12 and Excel 2013 software.
Normali =
Xi − Mini
Maxi − Mini
9

Data Description
Real data Normalize data
Sd.1 Av.2 Min3 Max4 Sd. Av. Min Max
Input
Age 17.70 35.93 2 95 0.19 0.36 0 1
BP systolic 21.77 125.85 40 210 0.13 0.51 0 1
BP diastolic 14.69 79.11 20 146 0.12 0.47 0 1
Respiratory
Rate
3.62 19.62 8 45 0.09 0.32 0 1
Pulse Rate 22.37 100.48 17 190 0.13 0.48 0 1
Output
pH 0.12 7.36 6.69 9.94 0.04 0.21 0 1
Pressure of
CO2
10.40 40.99 11 99 0.12 0.34 0 1
Hco3 4.64 22.92 3.4 48.5 0.10 0.43 0 1
10

Data Description
PH PCO2 HCO3
Age
Pearson
Correlation
0.040 0.040 0.103
P value 0.295 0.294 0.007
BP systolic
Pearson
Correlation
0.172 0.031 0.225
P value <0.001 0.419 <0.001
BP diastolic
Pearson
Correlation
0.187 0.009 0.186
P value <0.001 0.813 <0.001
Respiratory
Rate
Pearson
Correlation
-0.09 0.034 -0.083
P value 0.019 0.368 0.029
Pulse Rate
Pearson
Correlation
-0.11 -0.028 -0.162
P value 0.004 0.459 <0.001
11

Methods (Neural Network)
Y x = f b +
i=1
n
𝑤jxi
• The general structure of a standard neural network
where, 𝑌 represents the neural network function with 𝑅 𝑧
→ 𝑅 𝐿
mapping. z
and L denote the number of input and output, respectively. 𝑥𝑖 is the 𝑖𝑡ℎ row
of neural network input, 𝑤𝑗 shows the 𝑗𝑡ℎ column of weight matrix, b is the
network bias vector, and n is the number of samples.
12

Methods
Y1 = f1 b1 +
j=1
s1
i=1
n
Wj
1
Xi (3)
Y2
= f2 b2 +
j=1
s2
i=1
n
Wj
2
Yi
1
(4)
where,
Wk =
w11 ⋯ w1sk
⋮ ⋱ ⋮
wsk+11 ⋯ wsk+1sk
(5)
X =
x11 ⋯ x1z
⋮ ⋱ ⋮
xn1 ⋯ xnz
(6)
sq = s1 = z, s2, … , sN, sN+1 = L (7)
bk = [b1, b2]T
8
13

Methods
• MATLAB version of 2015b
14

Methods
MSE =
1
N
i=1
N
yi − yi
2
In this formula, 𝑁 is equal to the number of samples tested, 𝑦𝑖 is
the value of arterial gases predicted for a sample of 𝑖 by the
proposed method, and y𝐢 denotes the value of actual arterial gases
sample of 𝑖.
Evaluate the error in the output:
15

Results
I. INFORMATION ON THE TYPES OF NEURAL NETWORKS USED IN THE STUDY
Name of
network
Training
function
Adaption
learning
function
Performance
function
Number
of
neurons
Transfer
function
network1 trainLM LearnGDM MSE1 50 logsig
network2 trainCGB LearnGD MSE 50 logsig
network3 trainCGB LearnGDM MSEREG2 50 logsig
network4 trainCGB LearnGD SSE3 40 tansig
network5 trainCGB LearnGDM MSEREG 40 tansig
network6 trainCGF LearnGD SSE 40 logsig
network7 trainCGF LearnGDM MSEREG 40 tansig
network8 trainBR LearnGDM MSEREG 30 tansig
network9 trainSCG LearnGDM MSEREG 40 logsig
network10 trainRP LearnGDM MSEREG 50 logsig
1Mean Square Error, 2Mean Square Error Regularization, 3Sum Square Error
16

Results (Accuracy of Learn)
Name of
Network
Type of Data
Accuracy of Learn (%)
ph pco2 hco3 Total
network1
Normalize
Data 89.77% 74.77% 83.13% 83.52%
Real Data 99.07% 81.54% 85.63% 88.75%
network2
Normalize
Data 89.90% 75.71% 83.72% 84.04%
Real Data 99.08% 82.23% 86.13% 89.15%
network3
Normalize
Data 89.92% 76.22% 83.69% 84.19%
Real Data 99.08% 82.60% 86.11% 89.26%
network4
Normalize
Data 89.63% 74.58% 83.13% 83.46%
Real Data 99.06% 81.40% 85.63% 88.70%
network5
Normalize
Data 88.73% 74.23% 82.84% 83.02%
Real Data 98.97% 81.14% 85.39% 88.50%
network6
Normalize
Data 89.12% 74.36% 83.01% 83.20%
Real Data 99.01% 81.24% 85.53% 88.59%
17

Results (Accuracy of Test)
Name of
Network
Type of Data
Accuracy of Test (%)
ph pco2 hco3 Total
network1
Normalize
Data 90.06% 72.65% 81.18% 83.00%
Real Data 99.10% 80.09% 84.06% 87.75%
network2
Normalize
Data 89.67% 68.01% 79.50% 80.89%
Real Data 99.07% 76.71% 82.64% 86.14%
network3
Normalize
Data 89.23% 70.15% 79.51% 81.41%
Real Data 99.03% 78.27% 82.65% 86.65%
network4
Normalize
Data 89.05% 72.57% 81.38% 82.90%
Real Data 99.01% 80.03% 84.23% 87.76%
network5
Normalize
Data 89.74% 72.48% 81.57% 83.19%
Real Data 99.07% 79.96% 84.39% 87.81%
network6
Normalize
Data 89.61% 72.90% 81.61% 83.26%
Real Data 99.06% 80.27% 84.43% 87.92%
18

Results

Discussion & Conclusion
Using data from 2280 patients
• diastolic blood pressure (BPD)
• systolic blood pressure (BPS)
• pulse rate (PR)
• respiratory rate (RR)
• PH
• PCO2
• HCO3
 predict arterial blood gas items in traumatic patients
using neural network
21

Discussion & Conclusion
 1596 patients (training data)
 684 patients (test data) with accuracy of
87.92%
 predict arterial blood gas items in traumatic patients
using neural network
22

Thank You
for your attention

Neural network predicts blood gases

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