scholarly journals Prediction of Obstetric Patient Flow and Horizontal Allocation of Medical Resources Based on Time Series Analysis

2021 ◽  
Vol 9 ◽  
Author(s):  
Hua Li ◽  
Dongmei Mu ◽  
Ping Wang ◽  
Yin Li ◽  
Dongxuan Wang
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Seasonal Changes ◽  
Prediction Models ◽  
Patient Flow ◽  
Prediction Method ◽  
Hospital Management ◽  
Medical Resources ◽  
Obstetric Patient ◽  
The Government

Objective: Given the ever-changing flow of obstetric patients in the hospital, how the government and hospital management plan and allocate medical resources has become an important problem that needs to be urgently solved. In this study a prediction method for calculating the monthly and daily flow of patients based on time series is proposed to provide decision support for government and hospital management.Methods: The historical patient flow data from the Department of Obstetrics and Gynecology of the First Hospital of Jilin University, China, from January 1, 2018, to February 29, 2020, were used as the training set. Seven models such as XGBoost, SVM, RF, and NNAR were used to predict the daily patient flow in the next 14 days. The HoltWinters model is then used to predict the monthly flow of patients over the next year.Results: The results of this analysis and prediction model showed that the obstetric inpatient flow was not a purely random process, and that patient flow was not only accompanied by the random patient flow but also showed a trend change and seasonal change rule. ACF,PACF,Ljung_box, and residual histogram were then used to verify the accuracy of the prediction model, and the results show that the Holtwiners model was optimal. R2, MAPE, and other indicators were used to measure the accuracy of the 14 day prediction model, and the results showed that HoltWinters and STL prediction models achieved high accuracy.Conclusion: In this paper, the time series model was used to analyze the trend and seasonal changes of obstetric patient flow and predict the patient flow in the next 14 days and 12 months. On this basis, combined with the trend and seasonal changes of obstetric patient flow, a more reasonable and fair horizontal allocation scheme of medical resources is proposed, combined with the prediction of patient flow.

scholarly journals A single-station prediction model as a contribution to instantaneous mapping

1994 ◽  
Vol 37 (2) ◽  
Author(s):  
I. Stanislawska
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Prediction Method ◽  
Physical Processes ◽  
The Past ◽  
Ionospheric Station ◽  
Single Station ◽  
Daily Forecast

The paper presents two opposite approaches for single-station prediction and forecast. Both methods are based on different assumptions of physical processes in the ionosphere and need the different set of incoming data. Different heliogeophysical data, mainly f0F2 parameters from the past were analyzed for f0F2 obtaining for the requested period ahead. In the first method - the autocovariance prediction method - the time series of f0F2 from one station are used for daily forecast at that point. The second method may be used for obtaining f0F2 not only at the particular ionospheric station, but also at any point within the considered area.

A Traffic Prediction Method Based on the Hybrid Model of CLWT

2015 ◽  
Vol 713-715 ◽  
pp. 1564-1569
Author(s):  
Jin Long Fei ◽  
Wei Lin ◽  
Tao Han ◽  
Yue Fei Zhu
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Network Traffic ◽  
Prediction Models ◽  
Prediction Method ◽  
Support Vector ◽  
Prediction Methods ◽  
Recurrent Nerve ◽  
Nerve Network ◽  
Traffic Time Series

Current prediction models for network traffic cannot accurately depict the multi-properties of the Internet traffic. This paper proposes a wavelet-based hybrid model prediction method for network traffic called CLWT model and proposes a prediction method for traffic based on this model. The traffic time series can be rapidly decomposed respectively into approximate time series and detail time series with LF and HF response. The approximate time series predicts by making use of Least Squares Support Vector Machine and proceeds error calibration by using Generalized Recurrent Nerve Network. The detail time series predict it by making use of self-adaption chaotic prediction methods after the medium-soft threshold noise reduction. Finally the prediction value of time series is got by making use of promoting wavelet reconstitution. The effectiveness for the prediction methods mentioned in the paper has been validated by simulation experiment. High prediction accuracy is obtained compared with the existing methods.

scholarly journals Prognosis of Chronic Renal Syndrome by Classification and Progression Using Temporal Abstraction

2019 ◽  
Vol 8 (11) ◽  
pp. 53-58
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Renal Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Series Data ◽  
Support Vector ◽  
Temporal Abstraction ◽  
Graphic Data ◽  
Lab Test

Chronic renal syndrome is defined as a progressive loss of renal function over period. Analysers have make effort in attempting to diagnosis the risk factors that may affect the retrogression of chronic renal syndrome. The motivation of this project helps to develop a prediction model for level 4 CKD patients to detect on condition that, their estimated Glomerular Filtration Rate (eGFR) stage downscale to lower than 15 ml/min/1.73 m². End phase renal disease, after six months accumulating their concluding lab test observation by assessing time affiliated aspects. Data mining algorithm along with Temporal Abstraction (TA) are confederated to reinforce CKD evolvement of prognostication models. In this work a inclusive of 112 chronic renal disease patients are composed from April 1952 to September 2011 which were extracted from the patient’s Electronic Medical Records (EMR). The information of chronic renal patients are collected in a big spatial info-graphic data. In order to analyse these info-graphic data, it is significant to detect the issues affecting CKD deterioration and hence it becomes a challenging task. To overcome this challenge, time series graph has been generated in this project work based on creatinine and albumin lab test values and reports of the time period. The presence of CKD diagnostic codes are transformed into default seven digit default format of International Classification of Disease 10 Clinical Modification (ICD 10 CM). Feature selection is performed in this work based on wrapper method using genetic algorithm. It is helpful for finding the most relevant variables for a predictive model. High Utility Sequential Rule Miner (HUSRM) is used here to address the discovery of CKD sequential rules based on sequence patterns. Temporal Abstraction (TA) techniques namely basic TA and complex TA are used in this work to analyse the status of chronic renal syndrome patients. Classification and Regression Technique (CART) along with Adaptive Boosting (AdaBoost) and Support Vector Machine Boosting (SVMBoost) are applied to develop the CKD in which the progression prediction models exhibit most accurate prediction. The results obtained from this work divulged that comprehending temporal observation forward the prognostic instances has escalated the efficacy of the instances. Finally, an evaluation metrics namely accuracy, sensitivity, specificity, positive likelihood, negative likelihood and Area Under the Curve (AUC) are helps to evaluate the performance of the prediction models which are designed and implemented in this project. Key Words: CKD, progression, time series data, genetic algorithm, sequential rules, TA classification and prediction model.

scholarly journals The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Tongfei Lao ◽  
Xiaoting Chen ◽  
Jianian Zhu
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Small Sample ◽  
Grey Prediction ◽  
Dynamic Background ◽  
Background Values ◽  
Grey Prediction Model ◽  
Model Based

As a tool for analyzing time series, grey prediction models have been widely used in various fields of society due to their higher prediction accuracy and the advantages of small sample modeling. The basic GM (1, N) model is the most popular and important grey model, in which the first “1” stands for the “first order” and the second “N” represents the “multivariate.” The construction of the background values is not only an important step in grey modeling but also the key factor that affects the prediction accuracy of the grey prediction models. In order to further improve the prediction accuracy of the multivariate grey prediction models, this paper establishes a novel multivariate grey prediction model based on dynamic background values (abbreviated as DBGM (1, N) model) and uses the whale optimization algorithm to solve the optimal parameters of the model. The DBGM (1, N) model can adapt to different time series by changing parameters to achieve the purpose of improving prediction accuracy. It is a grey prediction model with extremely strong adaptability. Finally, four cases are used to verify the feasibility and effectiveness of the model. The results show that the proposed model significantly outperforms the other 2 multivariate grey prediction models.

scholarly journals On the Prediction of Upwelling Events at the Colombian Caribbean Coasts from Modis-SST Imagery

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2861 ◽  
Author(s):  
José J. Alonso del Rosario ◽  
Juan M. Vidal Pérez ◽  
Elizabeth Blázquez Gómez
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Prediction Models ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Maximum Entropy Spectral Analysis ◽  
Nodal Lines ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Karhunen Loeve Transform

The upwelling cores on the Caribbean Colombian coasts are mainly located at the Peninsula de la Guajira and Cabo de la Aguja. We used monthly averaged Moderate Resolution Imaging Spectroradiometer (MODIS) sea surface temperature as the only information to build up a prediction model for the upwelling events. This comprised two steps: (i) the reduction of the complexity by means of the Karhunen–Loève transform and (ii) a prediction model of time series. Two prediction models were considered: (a) a parametric autoregressive-moving average (ARMA) time series from the Box–Jenkins methodology and (b) a harmonic synthesis model. The harmonic synthesis also comprised of two steps: the maximum entropy spectral analysis and a least-squares harmonic analysis on the set of frequencies. The parametric ARMA time series model failed at the time of prediction with a very narrow range, and it was quite difficult to apply. The harmonic synthesis allowed prediction with a horizon of six months with a correlation of about 0.80. The results can be summarized using the time series of the weights of the different oscillation modes, their spatial structures with the nodal lines, and a high confidence model with a horizon of prediction of about four months.

scholarly journals A Study of Objective Prediction for Summer Precipitation Patterns Over Eastern China Based on a Multinomial Logistic Regression Model

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 213 ◽  
Author(s):  
Lihao Gao ◽  
Fengying Wei ◽  
Zhongwei Yan ◽  
Jin Ma ◽  
Jiangjiang Xia
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Prediction Models ◽  
Multinomial Logistic Regression ◽  
Eastern China ◽  
Prediction Method ◽  
Summer Precipitation ◽  
Intrinsic Structure ◽  
Climate Conditions ◽  
Precipitation Patterns

The prediction of summer precipitation patterns (PPs) over eastern China is an important and topical issue in China. Predictors that are selected based on historical information may not be suitable for the future due to non-stationary relationships between summer precipitations and corresponding predictors, and might induce the instability of prediction models, especially in cases with few predictors. This study aims to investigate how to learn as much information as possible from various and numerous predictors reflecting different climate conditions. An objective prediction method based on the multinomial logistic regression (MLR) model is proposed to facilitate the study. The predictors are objectively selected from a machine learning perspective. The effectiveness of the objective prediction model is assessed by considering the influence of collinearity and number of predictors. The prediction accuracy is found to be comparable to traditionally estimated predictability, ranging between 0.6 and 0.7. The objective prediction model is capable of learning the intrinsic structure of the predictors, and is significantly superior to the prediction model with randomly-selected predictors and the single best predictor. A robust prediction can be generally obtained by learning information from plenty of predictors, although the most effective model may be constructed with fewer predictors through proper methods of predictor selection. In addition, the effectiveness of objective prediction is found to generally improve as observation increases, highlighting its potential for improvement during application as time passes.

A numerical external pitting damage prediction method of buried pipelines

2020 ◽  
Vol 38 (5) ◽  
pp. 433-444
Author(s):  
Eliceo Sosa ◽  
Adrian Verdín Martinez ◽  
Jorge L. Alamilla ◽  
Antonio Contreras ◽  
Luis M. Quej ◽  
...  
Keyword(s):  
Neural Network ◽  
Corrosion Rate ◽  
Prediction Model ◽  
Influencing Factors ◽  
Prediction Models ◽  
Prediction Method ◽  
Corrosion Damage ◽  
Buried Pipelines ◽  
Damage Prediction ◽  
External Corrosion

AbstractThe work introduces a numerical external damage prediction method for buried pipelines. The external pitting initiation and corrosion rate of oil or gas pipelines are affected by pipeline age, physicochemical properties of soils and cathodic protection performance as well as coating conditions. Before developing the damage prediction model, the influencing factors were weighed by grey relational analysis, and then the relationship among the pitting depth and the influencing factors of external corrosion was established for corrosion damage prediction through artificial neural network (ANN). Subsequently, the established ANN was applied to predict corrosion damage and corrosion rate for some selected cases, and the neural network prediction model was analyzed and compared to another corrosion rate prediction models. Through the analysis and comparison, a few opinions were proposed on the external corrosion damage prediction and pipeline integrity management.

scholarly journals Power Customer Complaint Prediction Model Based on Time Series Analysis

2020 ◽  
Vol 34 (4) ◽  
pp. 471-477
Author(s):  
Shuangshuang Guo ◽  
Linlin Tang ◽  
Xiaoyan Guo ◽  
Zheng Huang
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Customer Service ◽  
Prediction Models ◽  
Moving Average ◽  
Customer Complaints ◽  
Customer Complaint ◽  
Optimized Model ◽  
Predicted Values ◽  
Bpnn Model

To improve customer service of power enterprises, this paper constructs an intelligent prediction model for customer complaints in the near future based on the big data on power service. Firstly, three customer complaint prediction models were established, separately based on autoregressive integrated moving average (ARIMA) time series algorithm, multiple linear regression (MLR) algorithm, and backpropagation neural network (BPNN) algorithm. The predicted values of the three models were compared with the real values. Through the comparison, the BPNN model was found to achieve the best predictive effect. To help the BPNN avoid local minimum, the genetic algorithm (GA) was introduced to optimize the BPNN model. Finally, several experiments were conducted to verify the effect of the optimized model. The results show that the relative error of the optimized model was less than 40% in most cases. The proposed model can greatly improve the customer service of power enterprises.

scholarly journals Ultra-Short-Term Photovoltaic Power Prediction Model Based on the Localized Emotion Reconstruction Emotional Neural Network

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2857 ◽  
Author(s):  
Yufei Wang ◽  
Li Zhu ◽  
Hua Xue
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Prediction Models ◽  
Short Term Memory ◽  
Single Step ◽  
Power Prediction ◽  
Short Term ◽  
Model Based ◽  
Emotional Neural Network

Due to the intermittency and randomness of photovoltaic (PV) power, the PV power prediction accuracy of the traditional data-driven prediction models is difficult to improve. A prediction model based on the localized emotion reconstruction emotional neural network (LERENN) is proposed, which is motivated by chaos theory and the neuropsychological theory of emotion. Firstly, the chaotic nonlinear dynamics approach is used to draw the hidden characteristics of PV power time series, and the single-step cyclic rolling localized prediction mechanism is derived. Secondly, in order to establish the correlation between the prediction model and the specific characteristics of PV power time series, the extended signal and emotional parameters are reconstructed with a relatively certain local basis. Finally, the proposed prediction model is trained and tested for single-step and three-step prediction using the actual measured data. Compared with the prediction model based on the long short-term memory (LSTM) neural network, limbic-based artificial emotional neural network (LiAENN), the back propagation neural network (BPNN), and the persistence model (PM), numerical results show that the proposed prediction model achieves better accuracy and better detection of ramp events for different weather conditions when only using PV power data.

IMPROVED ITERATIVE PREDICTION FOR MULTIPLE STOP ARRIVAL TIME USING A SUPPORT VECTOR MACHINE

Transport ◽  
2012 ◽  
Vol 27 (2) ◽  
pp. 158-164 ◽  
Author(s):  
Chang-Jiang Zheng ◽  
Yi-Hua Zhang ◽  
Xue-Jun Feng
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Arrival Time ◽  
Prediction Models ◽  
Prediction Method ◽  
Support Vector ◽  
Dynamic Algorithm ◽  
Proposed Model ◽  
Speed Prediction ◽  
Two Stages

The paper presents an improved iterative prediction method for bus arrival time at multiple downstream stops. A multiple-stop prediction model includes two stages. At the first stage, an iterative prediction model is developed, which includes a single stop prediction model for arrival time at the immediate downstream stop and an average bus speed prediction model on further segments. The two prediction models are constructed with a support vector machine (SVM). At the second stage, a dynamic algorithm based on the Kalman filter is developed to enhance prediction accuracy. The proposed model is assessed with reference to data collected on transit route No 23 in Dalian city, China. The obtained results show that the improved iterative prediction model seems to be a powerful tool for predicting multiple stop arrival time.

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