The Effect of Nursing Intervention Model Using Mobile Nursing System on Pregnancy Outcome of Pregnant Women

Hindawi
Journal of Healthcare Engineering
Volume 2022, Article ID 1011595, 9 pages
https://doi.org/10.1155/2022/1011595

Research Article
The Effect of Nursing Intervention Model Using Mobile Nursing
System on Pregnancy Outcome of Pregnant Women

 Yang Lu
 Third Ward of Obstetrics and Gynecology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China

 Correspondence should be addressed to Yang Lu; 4408@hrbmu.edu.cn

 Received 12 December 2021; Revised 5 January 2022; Accepted 17 January 2022; Published 23 February 2022

 Academic Editor: Rahim Khan

 Copyright © 2022 Yang Lu. This is an open access article distributed under the Creative Commons Attribution License, which
 permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

 Due to the recent advancement in technology specifically mobile phones, these devices can be used in the hospital to monitor or
 speed up various activities, which are related to doctors and nurses. In literature, various mechanisms have been presented to
 resolve this issue, but none of these approaches have considered effectiveness of this technology in the development of a proper
 mobile nursing system, which is specifically designed for pregnant women. Therefore, in this paper, we have explored the effect of
 the intervention model based on the mobile nursing system on the pregnancy outcome of pregnant women. In this study, an
 Android-based mobile nursing monitoring system was adopted to monitor and transmit the human physiological data through
 physiological parameter monitoring equipment and continuously monitor the physiological parameter data of pregnant women.
 If the physiological health data of the pregnant woman was abnormal, it had to implement timely nursing intervention. In this
 study, 266 pregnant women in the electronic records (E-records) were selected as the research objects and divided into two groups
 according to the intervention method. Pregnant women in group A received routine physical examination during pregnancy,
 while those in group B received nursing intervention based on mobile nursing system. The results showed that the incidence of
 each indicator of pregnancy outcome in group B was significantly lower than that in group A, and the difference was statistically
 significant (P < 0.05). The nursing intervention model based on the mobile nursing system can effectively improve the pregnancy
 outcome. The mobile nursing system can help nursing staff find the abnormalities of pregnant women during pregnancy and give
 effective nursing measures in time, which helped improve the pregnancy outcomes, reduce the probability of adverse pregnancy
 outcomes, ensure the safety of puerperae and newborns, and lessen the delivery risk factors.

1. Introduction standards have improved, and there are unreasonable diets
 such as high-calorie and high-protein food intake. In ad-
There are many factors that cause bad pregnancy outcome, dition, because pregnant women should not be too active
which are caused by the mutual influence of multiple factors during pregnancy, more and more pregnant women cannot
such as parents, heredity, personal living habits, environ- control their own weight gain well. Studies have confirmed
ment, and psychology [1]. With the gradual rise of social that there is a close relationship between the growth of
status, women have the possibility of receiving more edu- pregnant women’s own weight and pregnancy outcome.
cation or work and are no longer confined to the family. Obese people are more likely to develop gestational diabetes
Therefore, the marriage and childbearing ages of women or hypertension during pregnancy, and the probability of
have been postponed, and the elderly pregnant women have cesarean section is greatly increased [3, 4]. With the rapid
increased year by year. Many studies have proved that the development of modern industrialized society, environ-
rise of age leads to an increase in the incidence of pregnancy mental pollution has become more and more serious, which
complications and complications, which greatly increases has also led to the increasing probability of pregnant
the risk of birth and the probability of adverse pregnancy women’s bad pregnancy outcomes, such as miscarriage and
outcomes, such as large babies and low birth weight babies defects in newborns [5]. The occurrence of various bad
[2]. With the development of modern society, people’s living pregnancy outcomes is accompanied by the risk of maternal

2 Journal of Healthcare Engineering

and child death, which has had a huge impact on the public makes the whole process of nursing diagnosis and treatment
health and social problems of the quality of newborn a qualitative change, allowing nurses to dispense, check, and
populations in various countries, and it has brought huge execute more efficiently, and making the whole nursing
economic and mental pressures to the family and society. process “paperless.” In addition, it can store the basic in-
 Placenta previa, postpartum hemorrhage, placental formation of patients in time, realize the optimal medical
abruption, and premature rupture of membranes are all process, minimize the medical error rate, and lessen the
important factors that cause premature birth [6]. Studies work intensity maximally. The electronic medical record can
have shown that advanced age, smoking, drug abuse, be moved and written, the medical decision-making is
multiple pregnancy, curettage, and multiple abortions are all scientific and intelligent, and the quality assessment stan-
risk factors for placenta previa [7]. Puerperae, high blood dard of the medical staff is digital.
pressure, smoking, drug use, and advanced age are also Therefore, mobile nursing monitoring system based on
important reasons for placental abruption [8]. Lack of trace Android this time is a way of health monitoring in remote
elements such as zinc in the body, lack of vitamin C, and mode. Physiological parameter monitoring equipment
reproductive tract infections can all lead to premature monitors and transmits human physiological data, real-time
rupture of membranes [9]. In addition to the body’s own monitoring the physiological data such as heart rate, blood
factors such as soft birth canal injury and weak uterine pressure, and body temperature of the user. If the physio-
contractions, multiple curettage, excessive births, over- logical parameters of users are abnormal, an alarm can be
frequency, poor physical fitness, and other chronic systemic issued in time. It was compared with pregnant women who
diseases in pregnant women may all cause postpartum have not undergone nursing intervention and only have
hemorrhage [10]. In order to reduce the occurrence of the routine physical examinations during pregnancy to explore
abovementioned bad pregnancy outcomes, the Chinese the effect of this method on pregnancy outcome. In this
government and local medical and health care departments paper, we have explored the effect of the intervention model
have begun to pay attention to and strengthen the health care based on the mobile nursing system on the pregnancy
of pregnant women during pregnancy [11]. With the outcome of pregnant women. In this study, an Android-
strengthening of publicity and the continuous improvement based mobile nursing monitoring system was adopted to
of health care during pregnancy, people have begun to pay monitor and transmit the human physiological data through
more attention to health care during pregnancy, health physiological parameter monitoring equipment and con-
awareness has gradually increased, and compliance has also tinuously monitor the physiological parameter data of
improved. However, the incidence of adverse pregnancy pregnant women. If the physiological health data of the
outcomes such as newborns defects, low birth weight, pregnant woman was abnormal, it had to implement timely
pregnancy, or childbirth complications has not been found nursing intervention. In this study, 266 pregnant women in
to be significantly reduced. Therefore, pregnancy nursing the electronic records (E-records) were selected as the re-
intervention has gradually been paid attention to by medical search objects and divided into two groups according to the
staff in order to reduce adverse pregnancy outcomes and intervention method. Pregnant women in group A received
improve population quality [12]. However, pregnant women routine physical examination during pregnancy, while those
usually go to the hospital for pregnancy checkups on a in group B received nursing intervention based on mobile
regular basis due to conditions or other influences. However, nursing system.
for pregnant women, especially pregnant women in the third The remaining sections of the paper are managed and
trimester of pregnancy, it is necessary to detect vital signs in represented accordingly as given below.
real time, so that they can be helped when the bodies are In subsequent section, the android based mobile nursing
abnormal. monitoring system is described and presented in detailed
 The technology and modern telemedicine involve many along with its various portions or sections. Experimental
industries such as software technology, computer network, results and observations were reported in Section 3 of the
medical image processing, communication technology, and manuscript. These results have verified the exceptional
electronic information technology. The rapid development performance of the android based mobile nursing system. A
of hospital informatization has made the implementation of detailed discussion section is provided, which is followed by
the mobile nursing system possible. The mobile nursing comprehensive and brief concluding remarks sections. Fi-
system covers the entire process from the patient entering nally, reference materials are provided.
the hospital, through the preadmission, hospitalization, and
discharge. With the development of patient satisfaction as 2. Android Based Mobile Nursing
the core, it provides medical staff and managers with a Management System
process-based, informatized, paperless, automated, and in-
telligent one-stop nursing system management platform 2.1. Research Objects. In this study, the clinical data of 266
[13]. The mobile nursing system can record patient vital pregnant women were extracted from the electronic record
signs in real time and directly record and display the (E-record) system. When the data were extracted, it was
implementation of nursing-related diagnosis and nursing necessary to exclude I. pregnant women who were selected
measures after admission, effectively reducing the occur- to induce labor or abortion due to family planning re-
rence of medical accidents. The computer automatically strictions, personal or family factors after pregnancy; II.
generates the electronic version of the nursing record, which pregnant women who suffered from mental illness who did

Journal of Healthcare Engineering 3

not know their prepregnancy healthcare or had poor prediction method is based on the existing relevant theories
compliance and were unwilling to cooperate with the in- and knowledge and obtains the prediction results based on
vestigation. According to the treatment method of the pa- intuition. Therefore, the accuracy of its conclusions is mostly
tients, they were divided into two groups: pregnant women determined by the subjective thoughts of the judges, so the
in group A received routine physical examination during accuracy of the prediction results is poor. Quantitative
pregnancy, while those in group B received nursing inter- prediction is based on existing experimental data to establish
vention based on mobile nursing system. and calculate a certain mathematical model to obtain pre-
 diction data. Compared with qualitative prediction, the
 accuracy of prediction results has been effectively improved,
2.2. Mobile Nursing System. In this study, a remote nursing
 so it has also attracted more attention.
monitoring system based on the Android platform was
adopted, which was functioned with remote guidance
monitoring and physiological data monitoring to provide 2.3.1. Global Model Prediction. Statistical methods repre-
pregnant women with medical interaction, realize seamless sented by autoregressive and moving average model
connection and all-round medical monitoring services be- (ARIMA) achieved the time series fitting by determining
tween hospitals and families, and help nurses give timely model parameters and model coefficients. Then, the fitted
nursing interventions (Figure 1). model was adopted to predict the future trend, which
 In the beginning, the heart rate, blood pressure, and showed good prediction effect on linear systems [18]. The
body temperature of user were detected by the remote specific statistical method was given as follows:
medical monitoring system through various physiological
data collection terminals. After that, the collected physio- (1) Autoregressive (AR) Model. AR model is a dynamic
logical data were performed with D/B conversion, and the description of random process with good stability and is
wireless WiFi method was used to communicate with the suitable for the prediction of linear time series data. In this
Android mobile phone monitoring terminal (MPMT). The model, the current time value y1 of the time series was
Android MPMT processed the detected physiological data represented by the linear combination of the p historical
such as body temperature and blood pressure and then time values of the sequence plus the white noise perturbation
monitored and stored these data. In addition, the Android a1, as shown in the following equation:
MPMT transmitted the acquired data to the service system of
the remote monitoring center through interaction with the y1 � ∅1 yi− 1 + ∅2 yi− 2 + · · · + ∅p yi− p + a1 . (1)
wire beam electrode (WBE) service platform of the remote
monitoring center, so that the remote monitoring center can The above equation represented the p-order AR model
monitor these data in real time. Users, their relatives, and AR (p), a1 represented the white noise disturbance, showing
medical staff can log in at any time and view the general the sequence when the mean was equal to 0 and the variance
trend of current or past physiological data and various other was σ 2n , and ∅ (i � 1, 2, . . ., p) was the model coefficient. The
related service information. definition of AR model can explain that its function was to
 return the historical data of the sequence itself.

2.3. Related Algorithms for Physiological Parameter Collection. (2) Moving Average (MA) Model. In MA model, the linear
Because the monitoring system remotely detects the phys- combination of the white noise disturbance term in the past
iological data of the user, unlike a hospital that can help moment and the white noise disturbance term at the current
patients solve problems at any time, it is extremely necessary moment were used to represent the current moment values
to predict the physiological state of the patient in real time. of the time series, as shown in the following equation:
For example, if the heart abnormality cannot be found in
time for heart disease patients, it may lead to sudden death. If yi � ∅1 ai− 1 + ∅2 ai− 2 + · · · + ∅p ai− p + a1 yt . (2)
the system can predict the physical condition of future
patients in time, it can give nurses enough time to prepare The above equation represented the q-order MA model
solutions so as not to overwhelm nurses when problems MA (q), where the weighting factor ∅ (i � 1, 2, . . ., q) was the
arise. The prediction system not only reduces the risk of model coefficient. The MA model can run in reverse, and it
patients, but also reduces the pressure on nurses and ef- can also be used to predict linear system time series.
fectively improves the nursing effect. Therefore, a time series
prediction algorithm was applied in this study to predict the (3) Autoregressive Moving Average (ARMA) Model.
blood pressure, electrocardiograph (ECG), and other con- ARMA(p, q) can be expressed as the following equation:
ditions of patients. y1 � ∅1 yi− 1 + ∅2 yi− 2 + · · · + ∅p yi− p + ai θ1 ai− 1 + θ2 ai− 2
 At this stage, time series prediction methods roughly
include two major categories: statistical methods and arti- + · · · + θp ai− p + ai .
ficial intelligence methods [14, 15]. In addition, it can be (3)
divided into global model and local model according to the
difference of modeling principle and structure [16] (Fig- As shown in the above equation, the ARMA model can
ure 2). Statistical methods can be divided into two methods: be classified into two types: AR model and MA model. It is
qualitative and quantitative prediction [17]. The qualitative generally used to predict linear and stable time series

4 Journal of Healthcare Engineering

 Network
 Physiological data
 acquisition terminal

 Patient Nursing
 Patient
 staff
 Mobile terminal

 Figure 1: The remote nursing monitoring system based on the android platform.

 Simulation
 Training set training
 Time series Data Prediction
 data preproce results
 ssing
 Test set Model
 prediction

 (a)

 Simulation
 Historical training
 data set
 Time Data Similarity
 series data preprocessing measure
 Test set
 Model
 prediction

 Prediction
 results

 (b)

 Figure 2: Comparison of prediction principles between global model and local model. (a) Global model. (b) Local model.

modeling. Because modeling is easy to understand and easy The ARIMA model further improved the ARMA model,
to operate, it is widely used in many fields. However, in applying difference to deal with non-stationary data. Al-
reality, most of the environment is not stable, or the se- though there were applications within a certain range, it had
quence data is nonlinear, and ARMA is a linear model, so it a strong pertinence to application data and cannot adapt to
cannot meet the requirements of such data modeling some complex nonlinear data.
accuracy. With the continuous advancement of modern machine
 learning technology, some artificial intelligence methods
(4) Autoregressive and Moving Average (ARIMA) Model. The have gradually emerged and are widely used in various fields
structural model of ARIMA(p, d, q) can be expressed as the of predicting time series [19]. Compared with traditional
following equation: statistical methods, these artificial intelligence methods have
 obvious advantages in processing time series data with
 ∅(B)∇d yt � θ(B)ai . (4) complex nonlinearities, and some data can build the
 mapping relationship between input and output without
 In the above equation, ∇ � 1 − B represented the dif- human intervention. These methods of predicting sequences
ference operator, and the nonstationary time series y1 can be have been widely used at this stage. The least squares support
obtained by using the d-order difference processing to vector machine (LS-SVM) method was briefly described in
obtain the stationary time series ∇d y1 , ∅(B) � 1 − ∅1 B− this study.
· · · − ∅p Bp , and ∅(B) � 1 − ∅1 B − · · · − ∅q Bq . It can be LS-SVM is mostly used to deal with function estimation
explained from the above equation that when d � 0, the or nonlinear regression. The initial weight space of the re-
ARIMA (p, d, q) model was the ARIMA (p, q) model. The gression model was shown in the following equation:
process of the ARIMA time series prediction was shown in
Figure 3. y(x) � ωT ∅(x) + b. (5)

Journal of Healthcare Engineering 5

 Stationary Y White Y Prediction
 Time
 series test noise test results

 N
 N

 Differential Fitting ARIMA
 operation model

 Figure 3: The process of the ARIMA time series prediction.

 In the equation above, ω represented the weight vector, b Therefore, the LS-SVM prediction model can be ob-
was the bias term, and ∅ (x) was the mapping function from tained as follows:
the input space to the high-dimensional feature space. The N
optimization steps of LS-SVM in the initial space can be as (x) � ak K x, xk + b.
 y (11)
shown in the following equation: k�1

 1 T 1 N 2 In equation (11), y (x) represented the output value of
 minJ(ω,e) � ↑ω ω + c e . (6)
 ω,b,e
 2 2 k− t k the prediction model, (x, xk � ∅(x)T ∅(x)), and the fol-
 lowing radial 2 basis kernel function was used:
 2
 The constraints were defined as follows: K(x, xk ) � e|x− xk | /σ .
 y k � ω T ∅ xk + b + e k , k � 1, . . . , N. (7)

 In equation (7), yk is the output value, c is the regula- 2.4. Local Model Prediction. The main purpose of the local
rization parameter, which is the penalty for the error term, model is to find the nearest neighbor set of the query sample.
and ek is the regression error of the k-th training sample. Its focus is to select the similarity measurement algorithm.
 Applying Lagrangian function to solve the above opti- The appropriate measurement algorithm can ensure that the
mization, the following equation can be obtained: local model has a better prediction effect. In this study, the
 multitask LA-SVM algorithm and the K-means algorithm
 n
 method were adopted and combined with semisupervised
 L ω, ak , b, ek � J + ak yk − ωT ∅ xk − b − ek . (8) and supervised learning algorithms. The input values were
 k�1
 clustered through the K-mean algorithm, adjacent samples
 It could solve all the variables in equation (8), where ak is a were classified into the same cluster, and each of them was
Lagrangian multiplier, and the inequality constraint term is trained and simulated by the MTLS-SVM algorithm to
transformed into an equality constraint term according to KKT: obtain the predicted output value. This method could not
 N
 only make up for the shortcomings of mining sequence
 zL information in traditional algorithms, but also did not
 � 0⇒ω � ak ∅ xk ,
 zω k�1 consider the poor prediction accuracy caused by compre-
 hensive intersequence correlation and other reasons. At the
 zL N same time, it reduces the dimensionality in the support
 � 0⇒ ak � 0, vector machine and effectively improves the operating
 zb k�1
 (9) efficiency.
 The algorithm mainly included three parts: data pre-
 zL
 � 0⇒cek − ai � 0, processing, task construction, and model training (Figure 4):
 zek The data preprocessing was described as follows.
 It was supposed that the time series y1 , y2 , . . . , yn ,
 zL
 � 0⇒yk − ωT ∅ xk − b − ek � 0. yn+1 , . . . , yt } was sampled at the same time interval, t was the
 zak current time, and the predicted time series was to estimate
 After training to obtain the values of ω and ek , the the time t + h based on the historical sampling value. The
following linear system can be obtained: yt+h was shown in the following equation:
 yt+h � f yt , yt− 1 , . . . , yt− n+1 . (12)
 0 1T
 ⎜
 ⎛
 ⎜
 ⎜ ⎞ b
 ⎟
 ⎟
 ⎟ 0
 ⎜
 ⎜ ⎟
 ⎟⎢
 ⎡
 ⎢ ⎢
 ⎤⎥⎥⎦ � ⎡
 ⎢ ⎤⎥⎥⎦. In equation (12), h represented the prediction step
 ⎜
 ⎜ ⎟
 ⎟⎣ ⎣ (10)
 ⎜
 ⎝ 1⎠⎟ length; when h � 1, it meant single-step prediction, and when
 1 K+ a y
 c h > 1, it meant multistep prediction. f represented the pre-
 diction model, and n represented the delay time. The col-
 In equation (10), (1 � 1, 1, . . . , 1)T was a matrix in lected observation value y1 , y2 , . . . , yn , yn+1 , . . . , yt was
N × 1, (a � a1 , a2 , . . . , ak )T , and K referred to the kernel clustered to construct a data set sample through the K-means
function. algorithm.

6 Journal of Healthcare Engineering

 Cluster Test set
 1

 Input

 Time series Data Cluster
 MTLS-SVM
 preprocessing 2

 Output

 Cluster Estimate
 k

 Figure 4: The time series prediction models based on K-means and MTLS-SVM.

 For the task construction (shown in Figures 4 and 5), the
single task and multiple tasks constructed in the time series, t-1
the combination of multitask learning methods and time
series prediction focuses mainly on the close relationship
between adjacent time points. In the past, single task was t-n+1 … t-1 t t-1
used. The learning method was to predict the value at time
t + h (h ≥ 1) from the observation value at n times, and the …
model output value was single. The multitask learning K tasks
method can jointly represent the prediction output of t + 1, t-1
t + 2,. . ., t + k, which had the effect of inductive bias and
improves the accuracy of prediction. Figure 5: Multitask construction in time series.
 Applying the multitask learning method only needed to
build a multioutput prediction model, and the output values
 3.1. Algorithm Model Experiment Results. In order to verify
of k tasks could be obtained at a time. According to the time
 the practicability of the research algorithm and obtain ac-
series prediction results, the appropriate k tasks were se-
 curate experimental results, the case data of 95 patients
lected, and a corresponding training set was constructed for diagnosed with heart disease and hypertension were
each task.
 extracted. After data collection and processing, K-means
 The model training was described as follows.
 algorithm was used to implement the clustering algorithm.
 It could train the corresponding training sets of k
 In this study, the appropriate value of k was determined
learning tasks and the MTLS-SVM model synchronously.
 firstly so that the results that can be easily obtained showed
Because these tasks were to predict similar time values
 higher accuracy. The results when the k value was 2, 3 and 4,
synchronously, there was a parallel relationship and a close
 5 were simulated through different subsets, as shown in
relationship among them. Throughout the training process,
 Figures 6–9.
tasks can be restricted to each other to achieve the effect of
 The number of sample values contained in data sets 1, 2,
inductive bias, thereby improving the model’s ability to
 and 3 was 198, 427, and 562, respectively. It can be explained
predict unknown data (Algorithm 1).
 that when the data set sample was small, the preaccuracy was
 After the required estimated value was selected, it can be best when k � 2; and when the data set sample was large, the
compared with the measured data to illustrate the accuracy
 prediction accuracy was the best when the k value was 3.
of the algorithm prediction in this study.

2.5. Statistical Methods. The SPSS22.0 was adopted to per- 3.2. Comparison on Pregnancy Outcome Results. The com-
form statistical analysis on the data, and descriptive statistics parison results of pregnancy outcome indicators of the two
were performed on the pregnant women pregnancy out- groups are shown in Figure 10. The incidence of related
come of each group. The data between the two groups were pregnancy outcome indicators of group A was significantly
compared using analysis of variance, and P < 0.05 was higher than that of group B, and the difference was statis-
considered statistically significant. tically significant (P < 0.05).

3. Results and Evaluation 4. Discussion
In this section, a detailed discussion on various evaluation Poor pregnancy outcome is the result of a variety of factors
metrics and performance of the proposed system is before and during pregnancy, which seriously threatens the
presented. safety of mothers and babies. Some pregnant women have

Journal of Healthcare Engineering 7

 Input: time series y1 , y2 , . . . , yn , yn+1 , . . . , yt 
 Output: estimated value yt+h at a future moment
 (1) Data preprocessing: after the original time series y was clustered through the K-means algorithm, the corresponding h should be
 determined according to the prediction, and the data set sample should be constructed.
 (2) k learning tasks were determined according to the target estimated value: t + 1, t + 2,. . ., t + k, k < h, each task outputted the
 corresponding yt+h , and the data set was clustered by the K-means algorithm one by one, and k classes formed a training set.
 (3) The set of k tasks and the MTLS-SVM model were trained at the same time, and the final prediction model can be obtained.
 (4) Inputting the corresponding test samples of k tasks into the trained MTLS-SVM model can obtain k estimated values at the same
 time, and the required estimated value yt+h could be selected from the obtained values.

 ALGORITHM 1: The process of predicting the time series based on MTLS-SVM was clarified as follows.

 Accuracy test results Accuracy test results
 100 100
 90 90
 80 80
 70 70
 60 60
 50 50
 40 40
 30 30
 20 20
 10 10
 0 0
 1 2 3 1 2 3
 ECG ECG
 BP BP
 Figure 6: Accuracy test results when K � 2. Figure 9: Accuracy test results when K � 5.

 poor prognostic effects, which bring huge pressure to the
 Accuracy test results
 100 family and society. Poor pregnancy outcome includes var-
 90 ious complications during childbirth, various complications
 80 during pregnancy, induction of labor, and premature birth
 70
 60 caused by various pathological factors during pregnancy
 50 [20]. Some studies all over the world have proved that
 40
 30
 nursing intervention can effectively reduce the probability of
 20 adverse pregnancy outcome, and there is an effective method
 10 to ensure the health of pregnant women during pregnancy
 0
 1 2 3 and after delivery [12, 21]. However, traditional nursing
 interventions cannot guarantee that pregnant women are
 ECG
 BP
 always concerned about their physical conditions. Therefore,
 this study was made to explore the impacts of the nursing
 Figure 7: Accuracy test results when K � 3. intervention model based on the mobile nursing system on
 the pregnancy outcome, provide strong data support for
 further improving the health care effect during pregnancy,
 and reduce the occurrence of undesirable pregnancy
 Accuracy test results outcomes.
 100
 90 In this article, the K-means algorithm and the multitask
 80 LS-SVM algorithm were combined, and the human physi-
 70
 60
 ological parameter monitoring system was combined; the
 50 K-means algorithm was adopted to cluster the data, and then
 40 the model was trained through the multitask LS-SVM al-
 30
 20 gorithm; and more accurate results were obtained in the
 10 prediction of physiological parameters of patients such as
 0 ECG and blood pressure. In addition, the pregnancy out-
 1 2 3
 comes were compared between pregnant women who had
 ECG not undergone special nursing interventions and pregnant
 BP
 women who had been given nursing interventions based on
 Figure 8: Accuracy test results when K � 4. the mobile nursing system. It can be found that, regarding

8 Journal of Healthcare Engineering

 Comparison of pregnancy outcomes
 Urinary tract infection
 *
 Postpartum hemorrhage
 *
 Premature rupture of membranes
 *
 Fetal distress
 *
 Polyhydramnios
 *
 Cesarean section
 *
 Premature delivery
 *
 Placenta previa
 *
 0 10 20 30 40 50 60 70 80 90
 Control group
 Experience group
 Figure 10: Comparison on pregnant women pregnancy outcome (∗ suggested P < 0.05 compared with the control group).

the pregnancy outcome indicators of placenta previa, pre- shortcomings of inability to monitor in real time and ef-
mature delivery, fetal distress, postpartum hemorrhage, fectively respond to the emergencies of pregnant women.
premature rupture of membranes, urinary tract infection, During pregnancy, there would be various factors that will
polyhydramnios, and cesarean section, the probability of affect the pregnancy outcome. Only by doing a good job in
occurrence of various adverse pregnancy outcomes in group pregnancy health care, timely discovering pregnancy risk
B was significantly lower in contrast to that in group A, and factors and giving corresponding nursing interventions, can
the difference was obvious with statistical significance it effectively reduce the appearance of bad pregnancy out-
(P < 0.05). This shows that the nursing intervention model comes, ensure the health of pregnant women and newborns,
based on the mobile nursing system can effectively improve and improve the birth quality of the population.
the pregnancy outcome. Preterm birth refers to babies whose
gestation period is 28 weeks but less than 37 weeks at the Data Availability
time of delivery. The research results show that preterm birth
accounts for 8.4% of the total number of bad pregnancy The datasets used and/or analyzed during the current study
outcomes in puerperae, which is roughly the same as the are available from the corresponding author upon reason-
average incidence of 5%∼15% in China previously reported. able request.
Postpartum hemorrhage means that pregnant women lose
more than 500 mL of blood within 24 hours after delivery, Conflicts of Interest
and the prevalence rate accounts for 28%–3% of the total
number of deliveries. The results of the study showed that The authors declare that they have no conflicts of interest.
postpartum hemorrhage accounted for 9.4%. This situation
may be related to the increase in the cesarean section rate. In References
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