Time Prediction Model Research Articles

Development of prediction models to estimate extubation time and midterm recovery time of ophthalmic patients undergoing general anesthesia: a cross-sectional study

BackgroundTo develop prediction models for extubation time and midterm recovery time estimation in ophthalmic patients who underwent general anesthesia.MethodsTotally 1824 ophthalmic patients who received general anesthesia at Joint Shantou International Eye Center were included. They were divided into a training dataset of 1276 samples, a validation dataset of 274 samples and a check dataset of 274 samples. Up to 85 to 87 related factors were collected for extubation time and midterm recovery time analysis, respectively, including patient factors, anesthetic factors, surgery factors and laboratory examination results. First, multiple linear regression was used for predictor selection. Second, different methods were used to develop predictive models for extubation time and midterm recovery time respectively. Finally, the models’ generalization abilities were evaluated using a same check dataset with MSE, RMSE, MAE, MAPE, R-Squared and CCC.ResultsThe fuzzy neural network achieved the highest R-Squared of 0.956 for extubation time prediction and 0.885 for midterm recovery time, and the RMSE value was 6.637 and 9.285, respectively.ConclusionThe fuzzy neural network developed in this study had good generalization performance in predicting both extubation time and midterm recovery time of ophthalmic patients undergoing general anesthesia.Trial registrationThis study is prospectively registered in the Chinese Clinical Trial Registry, registration number: CHiCRT2000036416, registration date: August 23, 2020.

Exploring potential barriers in equitable access to pediatric diagnostic imaging using machine learning.

In this work, we examine magnetic resonance imaging (MRI) and ultrasound (US) appointments at the Diagnostic Imaging (DI) department of a pediatric hospital to discover possible relationships between selected patient features and no-show or long waiting room time endpoints. The chosen features include age, sex, income, distance from the hospital, percentage of non-English speakers in a postal code, percentage of single caregivers in a postal code, appointment time slot (morning, afternoon, evening), and day of the week (Monday to Sunday). We trained univariate Logistic Regression (LR) models using the training sets and identified predictive (significant) features that remained significant in the test sets. We also implemented multivariate Random Forest (RF) models to predict the endpoints. We achieved Area Under the Receiver Operating Characteristic Curve (AUC) of 0.82 and 0.73 for predicting no-show and long waiting room time endpoints, respectively. The univariate LR analysis on DI appointments uncovered the effect of the time of appointment during the day/week, and patients' demographics such as income and the number of caregivers on the no-shows and long waiting room time endpoints. For predicting no-show, we found age, time slot, and percentage of single caregiver to be the most critical contributors. Age, distance, and percentage of non-English speakers were the most important features for our long waiting room time prediction models. We found no sex discrimination among the scheduled pediatric DI appointments. Nonetheless, inequities based on patient features such as low income and language barrier did exist.

Prediction of Winter Wheat Harvest Based on Back Propagation Neural Network Algorithm and Multiple Remote Sensing Indices

Predicting the harvest time of wheat in large areas is important for guiding the scheduling of wheat combine harvesters and reducing losses during harvest. In this study, Zhumadian, Zhengzhou and Anyang, the main winter-wheat-producing areas in Henan province, were selected as the observation points, and the main producing areas were from south to north. Based on Landsat 8 satellite remote sensing images, the changes in NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), and NDWI (Normalized Difference Water Index) were analyzed at different growth stages of winter wheat in 2020. Multiple regression analysis and Back Propagation (BP) neural network machine learning methods were used to establish prediction models for the harvest time of winter wheat at different growth stages. The results showed that the prediction model based on a BP neural network had high accuracy. The RMSE, MAE and MAPE of the training set and the test set were 0.531 and 0.5947, 0.3001 and 0.3104, 0.0114% and 0.0119%, respectively. The prediction model of winter wheat harvest date based on BP neural network was verified in the main winter wheat producing areas of Henan province in 2020 and 2021. The average errors were 1.67 days and 2.13 days, which were less than 3 days, meeting the needs for winter wheat production and harvest. The grain water content of winter wheat at harvest time calculated by the prediction model reached the grain water standard of the wheat combine harvester. Therefore, the prediction of the winter wheat harvest time can be realized based on multiple remote sensing indicators.

BSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease.

Atopic dermatitis (AD) is an allergic disease with extreme itching that bothers patients. However, diagnosing AD depends on clinicians' subjective judgment, which may be missed or misdiagnosed sometimes. This paper establishes a medical prediction model for the first time on the basis of the enhanced particle swarm optimization (SRWPSO) algorithm and the fuzzy K-nearest neighbor (FKNN), called bSRWPSO-FKNN, which is practiced on a dataset related to patients with AD. In SRWPSO, the Sobol sequence is introduced into particle swarm optimization (PSO) to make the particle distribution of the initial population more uniform, thus improving the population's diversity and traversal. At the same time, this study also adds a random replacement strategy and adaptive weight strategy to the population updating process of PSO to overcome the shortcomings of poor convergence accuracy and easily fall into the local optimum of PSO. In bSRWPSO-FKNN, the core of which is to optimize the classification performance of FKNN through binary SRWPSO. To prove that the study has scientific significance, this paper first successfully demonstrates the core advantages of SRWPSO in well-known algorithms through benchmark function validation experiments. Secondly, this article demonstrates that the bSRWPSO-FKNN has practical medical significance and effectiveness through nine public and medical datasets. The 10 times 10-fold cross-validation experiments demonstrate that bSRWPSO-FKNN can pick up the key features of AD, including the content of lymphocytes (LY), Cat dander, Milk, Dermatophagoides Pteronyssinus/Farinae, Ragweed, Cod, and Total IgE. Therefore, the established bSRWPSO-FKNN method practically aids in the diagnosis of AD.

Extension of Dynamic Mode Decomposition for dynamic systems with incomplete information based on t-model of optimal prediction

The Dynamic Mode Decomposition has proved to be a very efficient technique to study dynamic data. This is entirely a data-driven approach that extracts all necessary information from data snapshots which are commonly supposed to be sampled from measurement. The application of this approach becomes problematic if the available data is incomplete because some dimensions of smaller scale either missing or unmeasured. Such setting occurs very often in modeling complex dynamical systems such as power grids, in particular with reduced-order modeling. To take into account the effect of unresolved variables, the optimal prediction approach based on the Mori-Zwanzig formalism can be applied to obtain the most expected prediction under existing uncertainties. This effectively leads to the development of a time-predictive model accounting for the impact of missing data. In the present paper we provide a detailed derivation of the considered method from the Liouville equation and finalize it with the optimization problem that defines the optimal transition operator corresponding to the observed data. In contrast to the existing approach, we consider a first-order approximation of the Mori-Zwanzig decomposition, state the corresponding optimization problem and solve it with the gradient-based optimization method. The gradient of the obtained objective function is computed precisely through the automatic differentiation technique. The numerical experiments illustrate that the considered approach gives practically the same dynamics as the exact Mori-Zwanzig decomposition, but is less computationally intensive.

Implementation of Data Analytics for the Accuracy of Service Time Prediction Models

The performance of repair services is very important to determine the achievement of consumer confidence. The strategy that needs to be made is to pay attention to the timeliness of repairs so that no one is harmed between consumers and service providers. Tracing information on data from repair services is one effective way to determine the accuracy of computer repair time. The collection of information used comes from the Istidata Indopacific Solution Center (IISC) repair service dataset, consisting of a collection of data on the completion time of product unit repairs that are achieved and not achieved. Repair completion time is the time in accordance with the agreement between the repair service party and the consumer. Data processing is carried out by processing analytical data by utilizing the Weka Tools software with the application of classification with the J48 decision tree method which is the development of the C4.5 algorithm. The effectiveness of this method was tested using 10-fold cross validation, where from the results of the confusion matrix measurement an accuracy of 99.5% was obtained. The result states that the J48 decision tree method is effective and can be used to predict the accuracy of computer repair time.

Model for Prediction of Testing Time of a Computer Program for Automated Reliability Evaluation of Semiconductor Devices

The KLASS computer program planned for development is designed to work as a part of the ARION-plus software package and allows you to perform an automated assessment of the reliability of electronic products, including semiconductor devices. At the stage of work planning on the creation of the KLASS program, as a module of the ARION-plus complex, the question arose about the working time allotted for the procedure for testing a computer program. The approaches described in the scientific literature used to assess the operational reliability of computer programs, taking into account their testing, proceed from the fact that the program code has been written and debugged and there are certain data on the results of testing the computer program. Software developers would like to know the predicted testing time, which ensures a given operational reliability of a computer program, even before starting work on writing program code. Based on the analysis of the experimental data on the reliability of computer programs in various fields of application, a model is proposed for determining the testing time required to ensure the operational reliability of programs. The model was used for the computer program KLASS planned for development and takes into account the programming language, the amount of program code, the speed of the computer processor, and the scope of the program. Based on the obtained model, a nomogram with two binary fields was constructed, which allows one to quickly determine the predicted time for testing computer programs.

Retracted: Prediction Model for Driver Reaction Time Based on PSO-BP Neural Network Model

Retracted: Prediction Model for Driver Reaction Time Based on PSO-BP Neural Network Model

An attention-based recurrent learning model for short-term travel time prediction.

With the advent of Big Data technology and the Internet of Things, Intelligent Transportation Systems (ITS) have become inevitable for future transportation networks. Travel time prediction (TTP) is an essential part of ITS and plays a pivotal role in congestion avoidance and route planning. The novel data sources such as smartphones and in-vehicle navigation applications allow traffic conditions in smart cities to be analyzed and forecast more reliably than ever. Such a massive amount of geospatial data provides a rich source of information for TTP. Gated Recurrent Unit (GRU) has been successfully applied to traffic prediction problems due to its ability to handle long-term traffic sequences. However, the existing GRU does not consider the relationship between various historical travel time positions in the sequences for traffic prediction. We propose an attention-based GRU model for short-term travel time prediction to cope with this problem enabling GRU to learn the relevant context in historical travel time sequences and update the weights of hidden states accordingly. We evaluated the proposed model using FCD data from Beijing. To demonstrate the generalization of our proposed model, we performed a robustness analysis by adding noise obeying Gaussian distribution. The experimental results on test data indicated that our proposed model performed better than the existing deep learning time-series models in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2).

2022 Canadian Surgery Forum Sept. 15–17, 202201. Operative classification of ventral abdominal hernias: new and practical classification02. Watchful waiting for large primary splenic cysts03. Transversus abdominis plane (TAP) blocks with and without dexamethasone in colorectal surgery04. What factors determine publication of resident research day projects?05. Characterization of near-infrared imaging and indocyanine green use amongst general surgeons06. Variation in opioid prescribing

# 01. Operative classification of ventral abdominal hernias: new and practical classification {#article-title-2} Ventral hernias of the abdomen are defined as a noninguinal, nonhiatal defect in the fascia of the abdominal wall. Unfortunately, there is not currently a universal classification system

Evacuation Time Estimation Model in Large Buildings Based on Individual Characteristics and Real-Time Congestion Situation of Evacuation Exit

Fire is one of the most common and harmful disasters in real life. In 2021, firefighting teams in China reported 748,000 fires, resulting in 1987 deaths, 2225 injuries and CNY 6.75 billion of direct property losses, which account for 0.05‰ of GDP. Scientific and accurate estimation of evacuation time can provide decision support for intelligent fire evacuation. This paper aims to effectively improve the evacuation efficiency of people in large buildings, especially for a scenario with intricate evacuation passages. There are many factors that make a difference in evacuation time, such as individual behavior, occupant density, exit width, and so on. The people distribution density is introduced to effectively assess the impact of unstable pedestrian flow and unbalanced distribution in the process of evacuation. The verification results show that there is a strong positive correlation between people distribution density and evacuation time. Combining the people distribution density with many other factors, the training dataset is built by Pathfinder to learn the relationship between evacuation time and influencing factors. Finally, an evacuation time prediction model is established to estimate the consumption time that occupants spend on moving in the evacuation process based on stacking integration. The model can assist occupants in choosing different channels for evacuation in advance. After testing, the average error between the predicted evacuation consumption time and the reference time is 3.63 s. The result illustrates that the model can accurately predict the time consumed in the process of evacuation.

User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known Pharmaceutical-Related Small Molecules

In the pharmaceutical field, and more precisely in quality control laboratories, robust liquid chromatographic methods are needed to separate and analyze mixtures of compounds. The development of such chromatographic methods for new mixtures can result in a long and tedious process even while using the design of experiments methodology. However, developments could be accelerated with the help of in silico screening. In this work, the usefulness of a strategy combining response surface methodology (RSM) followed by multicriteria decision analysis (MCDA) applied to predictions from a quantitative structure-retention relationship (QSRR) model is demonstrated. The developed strategy shows that selecting equations for the retention time prediction models based on the pKa of the compound allows flexibility in the models. The MCDA developed is shown to help to make decisions on different criteria while being robust to the user's decision on the weights for each criterion. This strategy is proposed for the screening phase of the method lifecycle. The strategy offers the possibility to the user to select chromatographic conditions based on multiple criteria without being too sensitive to the importance given to them. The conditions with the highest desirability are defined as the starting point for further optimization steps.

Multicentre registration of wake-up stroke in China (MCRWUSC): a protocol for a prospective, multicentre, registry-based cohort study

IntroductionWake-up stroke (WUS) is a type of acute ischaemic stroke (AIS) that occurs during sleep with unknown time of symptom onset. The best treatment is usually not suitable for WUS,...

A Multi-Dimensional Deep-Learning-Based Evaporation Duct Height Prediction Model Derived from MAGIC Data

The evaporation duct height (EDH) can reflect the main characteristics of the near-surface meteorological environment, which is essential for designing a communication system under this propagation mechanism. This study proposes an EDH prediction network with multi-layer perception (MLP). Further, we construct a multi-dimensional EDH prediction model (multilayer-MLP-EDH) for the first time by adding spatial and temporal “extra data” derived from the meteorological measurements. The experimental results show that: (1) compared with the naval-postgraduate-school (NPS) model, the root-mean-square error (RMSE) of the meteorological-MLP-EDH model is reduced to 2.15 m, and the percentage improvement reached 54.00%; (2) spatial and temporal parameters can reduce the RMSE to 1.54 m with an improvement of 66.96%; (3) the multilayer-MLP- EDH model can match measurements well at both large and small scales by attaching meteorological parameters at extra height, the error is further reduced to 1.05 m, with 77.51% improvement compared with the NPS model. The proposed model can significantly improve the prediction accuracy of the EDH and has great potential to improve the communication quality, reliability, and efficiency of ducting in evaporation ducts.

A Crop Harvest Time Prediction Model for Better Sustainability, Integrating Feature Selection and Artificial Intelligence Methods

Making an accurate crop harvest time prediction is a challenge for agricultural management. Previous studies of crop harvest time prediction were mainly based on statistical methods, and the features (variables) affecting it were determined by experience, resulting in its inaccuracy. To overcome these drawbacks, the objective of this paper is to develop a novel crop harvest time prediction model integrating feature selection and artificial intelligence (long short-term memory) methods based on real production and climate-related data in order to accurately predict harvest time and reduce resource waste for better sustainability. The model integrates a hybrid search for feature selection to identify features (variables) that can effectively represent input features (variables) first. Then, a long short-term memory model taking the selected features (variables) as input is used for harvest time prediction. A practical case (a large fruit and vegetable cooperative) is used to validate the proposed method. The results show that the proposed method (root mean square error (RMSE) = 0.199, mean absolute percentage error (MAPE) = 4.84%) is better than long short-term memory (RMSE = 0.565; MAPE = 15.92%) and recurrent neural networks (RMSE = 1.327; MAPE = 28.89%). Moreover, the nearer the harvest time, the better the prediction accuracy. The RMSE values for the prediction times of one week to harvesting period, two weeks to harvesting period, three weeks to harvesting period, and four weeks to harvesting period are 0.165, 0.185, 0.205, and 0.222, respectively. Compared with other existing studies, the proposed crop harvest time prediction model, LSTMFS, proves to be an effective method.

A Delay Prediction Method for the Whole Process of Transit Flight

In order to strengthen the construction of smart airports and improve the ability of airport managers to identify, intervene and rescue delayed flights, this paper proposes a delay prediction method for the whole process of transit flights through the basic steps of node time and link time prediction and delayed flight identification. By designing the key node time prediction model (ML-DM), the method predicts the important guaranteed node time involved in the process of flight departure from the outstation to the departure from the current station. By constructing the imbalance data classification model, the delayed flight is identified at each predicted guarantee node. The experimental results for a busy airport show that this prediction method can achieve a maximum recognition rate of 96.5% for delayed flights.

Machine-Specific Delivery Sequence Model of Compact Superconducting Synchrocyclotron Proton Therapy Systems – A Multi-Institutional Investigation

<h3>Purpose/Objective(s)</h3> Proton pencil beam scanning delivery sequence varies among institutions due to the vendor, machine type, and configuration. However, having a reliable model of delivery time and sequence is critical to maximizing throughput when proton beam time is limited. In this study, we used an experimental approach to build a precise beam delivery time (BDT) and sequence prediction model for a new compact superconducting synchrocyclotron proton system (PTC#1) and compare it with a different institution using an older version of the same system (PTC#2). We investigated treatment efficiency and the interplay effect for the treatment of mobile tumors. <h3>Materials/Methods</h3> The machine delivery log files from PTC#1 were retrospectively analyzed to model beam delivery parameters. We compared the PTC#1 delivery sequence with PTC#2's published machine-specific model using the same type of proton system. A total of twenty clinical beams from head neck, lung, breast, and esophagus treatment sites were used for model comparison. Also, we used a single clinical treatment day to compare treatment efficiency. The interplay effect is evaluated and compared based on these two different delivery sequence models. A digital thoracic 4DCT phantom image set with a rigid target in the right lung was used for the study. Different delivery techniques such as standard delivery (N_vol=1) and volumetric repainting delivery (N_vol=2-5) were simulated. <h3>Results</h3> The results showed PTC#1 has a much faster spot scanning speed than PTC#2 thanks to a new generation of scanning magnets. The spot switching time (SSWT) is 0s within 11.3mm in x-direction and 8 mm in y-direction. Compared to PTC#2, the average SSWT reduces by 94.37%±3.82% (50.76s±35.10s) in PTC#1. PTC#1 also demonstrated improved burst switching transmission efficiency, which reduced burst switching time (BST) by 39.84%±3.64% (6.00s±2.21s) on average. The energy layer switching time (ELST) dominates the PTC#1 delivery time instead of SSWT for PTC#2 (Table). The total BDT was reduced by 54.61%±12.12% (53.45s±34.47s) on average. Based on the single day patient mix, it was estimated that 67 min irradiation time could be saved by upgrading the scanning magnets in PTC#2 (10% daily patient treatment throughput improvement). The results also indicated the interplay effect would be reduced as volumetric repainting number increased. The system in PTC#2 had better target coverage and homogeneity than PTC#1 for=1-3. Their target coverage converges at=5. <h3>Conclusion</h3> A precise machine-specific delivery sequence model is highly recommended in the interplay effect evaluation and clinical decision. It provided the first quantitative analysis of the impact of the new scanning magnets regarding the improvement in the treatment delivery efficiency.

An Adaptive Long-Term Bus Arrival Time Prediction Model with Cyclic Variations

Real-time bus arrival information systems at transit stops can be useful to passengers for efficient trip planning and reducing waiting times. The accuracy of such systems depends upon the ability of the model to account for variations in the data series and to adjust according to changing traffic conditions. Many of the existing studies on passenger information systems have modeled the system based on stationary relations, not taking into account the cyclic variations in data, which is often suitable for demonstration purposes but not for long-term implementation. The present study models the changing relationships using Double-Seasonal Holt-Winter’s Exponential Smoothing approach, which allows for self-updating parameters at four levels. It accounts for both long-term and short-term seasonal fluctuations in data while maintaining the dynamic treatment of real-time bus information. The model also takes into account delays using the real-time running information of the bus and incorporates it into subsequent forecasts for better accuracy. Real-time data from GPS transmitters in buses were used for validation of the proposed model. The results show that the proposed model performs better than the currently-used elementary field methods and is able to forecast bus travel times with a reasonable accuracy.

Determination of Infinite Dilution Activity Coefficients of Molecular Solutes in Ionic Liquids and Deep Eutectic Solvents by Factorization-Machine-Based Neural Networks

Widely known as “green solvents,” ionic liquids (ILs) and deep eutectic solvents (DESs) have been used as substitutes for traditional organic solvents in separation science. To achieve better separations using ILs and DESs, this work aimed to predict the infinite dilution activity coefficients (IDACs) of molecular solutes in these solvents with the state-of-the-art factorization-machine-based neural network (DeepFM). DeepFM combines the benefits of factorization machines and deep neural networks to learn both low-order and high-order interactions among features. The IDAC prediction model was established with 52,372 experimental IDAC datapoints including 260 solvents (252 ILs and 8 DESs) and 112 molecular solutes collected at various temperatures from 288.15 to 428.15 K. Chemical information describing the ILs, DESs, and molecular solutes was included in the IDAC prediction model, including chemical functional groups, molecular weights, and Abraham solvation parameters. The IDAC prediction model showed an improved accuracy compared with alternative models; additionally, DESs were included in the IDAC prediction model for the first time. The model will reduce the energy and resources needed to optimize the selection of ILs and DESs for specific separations, which will promote the development of these green solvents for sustainable chemical processes.

The automation of the development of classification models and improvement of model quality using feature engineering techniques

Recently pipelines of machine learning-based classification models have become important to codify, orchestrate, and automate the workflow to produce an effective machine learning model. In this article, we propose a framework that combines feature engineering techniques such as data imputation, transformation, and class balancing to compare the performance of different prediction models and select the best final model based on predefined parameters. The proposed framework is extendable and configurable by adding algorithms supported by the CARET package implemented in the R programming language. This framework can generate different machine learning models, which provide comparable results compared to other studies. The framework allows practitioners and researchers to automatically generate different classification models. This research used High-Resolution Orbitrap-based Mass Spectrometers (HRMS) data to create automated prediction models for the first time in literature. We demonstrated the applicability of feature engineering techniques such as data imputation, transformation (e.g., scaling, centering, etc.), and data balancing using several case studies and the proposed semi-automated framework. We showed how the initial prediction models can be improved using the proposed framework.