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Journal articles on the topic 'Predictive parameters'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Schoettler, Jochen J., Kathrin Brohm, Sonani Mindt, et al. "Mortality Prediction by Kinetic Parameters of Lactate and S-Adenosylhomocysteine in a Cohort of Critically Ill Patients." International Journal of Molecular Sciences 25, no. 12 (2024): 6391. http://dx.doi.org/10.3390/ijms25126391.

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Tissue hypoxia is associated with the development of organ dysfunction and death in critically ill patients commonly captured using blood lactate. The kinetic parameters of serial lactate evaluations are superior at predicting mortality compared with single values. S-adenosylhomocysteine (SAH), which is also associated with hypoxia, was recently established as a useful predictor of septic organ dysfunction and death. We evaluated the performance of kinetic SAH parameters for mortality prediction compared with lactate parameters in a cohort of critically ill patients. For lactate and SAH, maxim

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Lin, Chia-Ying, Yi-Ting Yen, Li-Ting Huang, et al. "An MRI-Based Clinical-Perfusion Model Predicts Pathological Subtypes of Prevascular Mediastinal Tumors." Diagnostics 12, no. 4 (2022): 889. http://dx.doi.org/10.3390/diagnostics12040889.

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This study aimed to build machine learning prediction models for predicting pathological subtypes of prevascular mediastinal tumors (PMTs). The candidate predictors were clinical variables and dynamic contrast–enhanced MRI (DCE-MRI)–derived perfusion parameters. The clinical data and preoperative DCE–MRI images of 62 PMT patients, including 17 patients with lymphoma, 31 with thymoma, and 14 with thymic carcinoma, were retrospectively analyzed. Six perfusion parameters were calculated as candidate predictors. Univariate receiver-operating-characteristic curve analysis was performed to evaluate

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Chien, Wen T., and W. C. Hung. "Investigation on the Predictive Model for Burr in Laser Cutting Titanium Alloy." Materials Science Forum 526 (October 2006): 133–38. http://dx.doi.org/10.4028/www.scientific.net/msf.526.133.

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The purpose of this study is to develop two predictive models for burr height in cutting titanium alloy plates by using Nd:YAG laser. Firstly, Taguchi method has been used to arrange the experimental scheme and analyze the results via analysis of mean . The important laser cutting parameters affecting burr height can be found. It shows that the pressure of assistant gas, the focusing position and the pulsed frequency are the most important cutting parameters in order. Then they have been chosen as the input variables for response surface methodology and used to construct a mathematical equatio

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Hu, Yawei, Ran Wei, Yang Yang, et al. "Performance Degradation Prediction Using LSTM with Optimized Parameters." Sensors 22, no. 6 (2022): 2407. http://dx.doi.org/10.3390/s22062407.

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Predicting the degradation of mechanical components, such as rolling bearings is critical to the proper monitoring of the condition of mechanical equipment. A new method, based on a long short-term memory network (LSTM) algorithm, has been developed to improve the accuracy of degradation prediction. The model parameters are optimized via improved particle swarm optimization (IPSO). Regarding how this applies to the rolling bearings, firstly, multi-dimension feature parameters are extracted from the bearing’s vibration signals and fused into responsive features by using the kernel joint approxi

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G, Ottos C., and Isaac E. O. "Modeling of Predictive interaction of Water Parameters in Groundwater." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 1091–96. http://dx.doi.org/10.31142/ijtsrd11292.

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Chien, Wen T., and S. W. Chang. "Study on the Predictive Model for Shear Strength in Laser Welding Stainless Steel." Materials Science Forum 505-507 (January 2006): 205–10. http://dx.doi.org/10.4028/www.scientific.net/msf.505-507.205.

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A predictive model is presented for the prediction of shear strength in laser welding AISI304 stainless steel. Welding experiments conducted using a pulsed Nd:YAG laser machine while the laser welding parameters and their levels have been arranged according to design of experiments of Taguchi method. The tensile tests are performed after welding and the measurements of tensile strength are further calculated for shear strength. The data can be analyzed using the principles of Taguchi method for determining the optimal laser welding parameters and for investigating the most significant laser we

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Nagler, Harris M., and Michael Rotman. "Predictive parameters for microsurgical reconstruction." Urologic Clinics of North America 29, no. 4 (2002): 913–19. http://dx.doi.org/10.1016/s0094-0143(02)00094-0.

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Zakuskin, Aleksandr S., and Timur A. Labutin. "StarkML: application of machine learning to overcome lack of data on electron-impact broadening parameters." Monthly Notices of the Royal Astronomical Society 527, no. 2 (2023): 3139–45. http://dx.doi.org/10.1093/mnras/stad3387.

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ABSTRACT Parameters of electron-impact (Stark) broadening and shift of spectral lines are of key importance in various studies of plasma spectroscopy and astrophysics. To overcome the lack of accurately known Stark parameters, we developed a machine learning approach for predicting Stark parameters of neutral atoms’ lines. By implementing a data pre-processing routine and explicitly testing models’ predictive ability and generalizability, we achieve a high level of accuracy in parameters prediction as well as physically meaningful temperature dependence. The applicability of the results is dem

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Yatmanov, Alexey N., Vasiliy Ya Apchel, Dmitrii V. Ovchinnikov, et al. "Use of value-based and motivational parameters with artificial intelligence technology to predict cadet maladjustment." Bulletin of the Russian Military Medical Academy 26, no. 4 (2024): 587–96. https://doi.org/10.17816/brmma635764.

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The paper demonstrates the potential for using value-based and motivational parameters with artificial intelligence technology to predict cadet maladjustment. A retrospective cohort study was conducted. For 2013–2021, 734 cadets of the Navy Military Training and Research Center “Soviet Union Fleet Admiral N.G. Kuznetsov Naval Academy” were examined, 48 of them were diagnosed with maladjustment. Neural networks were used for mathematical modeling of maladjustment prediction. The study included 8 cycles of neural network training and 7 cycles of neural network model testing. As the actual materi

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Zhao, Yunxiang, Ning Gao, Jian Cheng, et al. "Genetic Parameter Estimation and Genomic Prediction of Duroc Boars’ Sperm Morphology Abnormalities." Animals 9, no. 10 (2019): 710. http://dx.doi.org/10.3390/ani9100710.

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Artificial insemination (AI) has been used globally as a routine technology in the swine production industry. However, genetic parameters and genomic prediction accuracy of semen traits have seldom been reported. In this study, we estimated genetic parameters and conducted genomic prediction for five types of sperm morphology abnormalities in a large Duroc boar population. The estimated heritability of the studied traits ranged from 0.029 to 0.295. In the random cross-validation scenario, the predictive ability ranged from 0.212 to 0.417 for genomic best linear unbiased prediction (GBLUP) and

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Piras, Antonio, Rosario Corso, Viviana Benfante, et al. "Artificial Intelligence and Statistical Models for the Prediction of Radiotherapy Toxicity in Prostate Cancer: A Systematic Review." Applied Sciences 14, no. 23 (2024): 10947. http://dx.doi.org/10.3390/app142310947.

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Background: Prostate cancer (PCa) is the second most common cancer in men, and radiotherapy (RT) is one of the main treatment options. Although effective, RT can cause toxic side effects. The accurate prediction of dosimetric parameters, enhanced by advanced technologies and AI-based predictive models, is crucial to optimize treatments and reduce toxicity risks. This study aims to explore current methodologies for predictive dosimetric parameters associated with RT toxicity in PCa patients, analyzing both traditional techniques and recent innovations. Methods: A systematic review was conducted

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Nakashima, Toshihisa, Takayuki Ohno, Keiichi Koido, Hironobu Hashimoto, and Hiroyuki Terakado. "Accuracy of predicting the vancomycin concentration in Japanese cancer patients by the Sawchuk–Zaske method or Bayesian method." Journal of Oncology Pharmacy Practice 26, no. 3 (2019): 543–48. http://dx.doi.org/10.1177/1078155219851834.

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Background In cancer patients treated with vancomycin, therapeutic drug monitoring is currently performed by the Bayesian method that involves estimating individual pharmacokinetics from population pharmacokinetic parameters and trough concentrations rather than the Sawchuk–Zaske method using peak and trough concentrations. Although the presence of malignancy influences the pharmacokinetic parameters of vancomycin, it is unclear whether cancer patients were included in the Japanese patient populations employed to estimate population pharmacokinetic parameters for this drug. The difference of p

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Eschmann, Emmanuel, Patrick Emanuel Beeler, Markus Schneemann, and Jürg Blaser. "Developing strategies for predicting hyperkalemia in potassium-increasing drug-drug interactions." Journal of the American Medical Informatics Association 24, no. 1 (2016): 60–66. http://dx.doi.org/10.1093/jamia/ocw050.

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Objective: To compare different strategies predicting hyperkalemia (serum potassium level ≥5.5 mEq/l) in hospitalized patients for whom medications triggering potassium-increasing drug-drug interactions (DDIs) were ordered. Materials and Methods: We investigated 5 strategies that combined prediction triggered at onset of DDI versus continuous monitoring and taking into account an increasing number of patient parameters. The considered patient parameters were identified using generalized additive models, and the thresholds of the prediction strategies were calculated by applying Youden’s J stat

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Myasnikova, Ekaterina, and Konstantin N. Kozlov. "Statistical method for estimation of the predictive power of a gene circuit model." Journal of Bioinformatics and Computational Biology 12, no. 02 (2014): 1441002. http://dx.doi.org/10.1142/s0219720014410029.

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In this paper, a specific aspect of the prediction problem is considered: high predictive power is understood as a possibility to reproduce correct behavior of model solutions at predefined values of a subset of parameters. The problem is discussed in the context of a specific mathematical model, the gene circuit model for segmentation gap gene system in early Drosophila development. A shortcoming of the model is that it cannot be used for predicting the system behavior in mutants when fitted to wild type (WT) data. In order to answer a question whether experimental data contain enough informa

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Gomah, Mohamed Elgharib, Guichen Li, Naseer Muhammad Khan, et al. "Prediction of Strength Parameters of Thermally Treated Egyptian Granodiorite Using Multivariate Statistics and Machine Learning Techniques." Mathematics 10, no. 23 (2022): 4523. http://dx.doi.org/10.3390/math10234523.

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The mechanical properties of rocks, such as uniaxial compressive strength and elastic modulus of intact rock, must be determined before any engineering project by employing lab or in situ tests. However, there are some circumstances where it is impossible to prepare the necessary specimens after exposure to high temperatures. Therefore, the propensity to estimate the destructive parameters of thermally heated rocks based on non-destructive factors is a helpful research field. Egyptian granodiorite samples were heated to temperatures of up to 800 °C before being treated to two different cooling

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Chen, Sha-Yan, Xue-Jing Song, Jiong-Tang Lu, et al. "Application of alkaline phosphatase-to-hemoglobin and lactate dehydrogenase-to-hemoglobin ratios as novel noninvasive indices for predicting severe acute pancreatitis in patients." PLOS ONE 19, no. 11 (2024): e0312181. http://dx.doi.org/10.1371/journal.pone.0312181.

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Preventing the progression of acute pancreatitis (AP) to severe acute pancreatitis (SAP) is crucial for AP patients. The use of clinical parameters in laboratory facilities for predicting SAP can be rapid, efficient, and cost-effective. This study aimed to investigate the predictive and prognostic value of collected clinical detection parameters, such as serum alkaline phosphatase (ALP) and lactate dehydrogenase (LDH) levels, and their ratios, such as ALP-to-hemoglobin (Hb) and LDH-to-Hb ratios, for the prediction of SAP occurrence, complications, and mortality. In all, 50 healthy controls (CO

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Baranov, L. A., E. P. Balakina, and Yungqiang Zhang. "Prediction error analysis for intelligent management and predictive diagnostics systems." Dependability 23, no. 2 (2023): 12–18. http://dx.doi.org/10.21683/1729-2646-2023-23-2-12-18.

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Random signal prediction is efficient for intelligent management and predictive diagnostics systems. Aim. The paper aims to analyse the error of random signal prediction. To develop recommendations for the selection of random signal extrapolator parameters. Methods. The paper uses the mathematics of the theory of random functions, formalization adopted in the theory of pulse systems, mathematical description of extrapolators with Chebyshev polynomials orthogonal over a set of equally spaced points. The coefficients of the predicting polynomial are selected according to the minimal least square

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Di, Yu, Ying Li, and Yan Luo. "Prediction of Implantable Collamer Lens Vault Based on Preoperative Biometric Factors and Lens Parameters." Journal of Refractive Surgery 39, no. 5 (2023): 332–39. http://dx.doi.org/10.3928/1081597x-20230207-03.

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Purpose: To establish and validate the accuracy of implantable collamer lens (ICL) vault size prediction formula based on preoperative biometric factors and lens parameters. Methods: This study included 300 patients (300 eyes) with Visian ICL V4c (STAAR Surgical) implantation. They were randomly divided into the formula establishment group and formula validation group. Anterior segment measurements, ICL V4c size and power, and vault 1 week postoperatively were collected from all patients. Multiple linear regression analysis was performed to establish the prediction formula. Mean absolute error

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PRASHANT, MAHAJAN, DESHPANDE PRATIK, NANAWARE TEJAS, and MAHENDRA PATIL PROF. "IMPROVING PREDICTIONS USING QUALITATIVE PARAMETERS." JournalNX - a Multidisciplinary Peer Reviewed Journal 3, no. 8 (2017): 77–82. https://doi.org/10.5281/zenodo.1420793.

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Selecting appropriate parameters while making any prediction model is a tedious task. Often, while constructing a prediction model, categorical variables are ignored. If we include more qualitative parameters for prediction, the observed results will have more accuracy. Neural networks help in a proper learning methodology which utilizes the concept of machine learning. When prediction is to be made, the human behavioral patterns hamper the test results as it plays a crucial role in any decision making. Employing qualitative parameters in decision making, accurate conjectures are possible. Qua

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Shan, Kun, Liaoyuan Zhang, Bo Tan, et al. "Prediction Model for Material Removal Rate of TC4 Titanium Alloy Processed by Vertical Vibratory Finishing." Coatings 15, no. 3 (2025): 286. https://doi.org/10.3390/coatings15030286.

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To establish a high-precision prediction model for the material removal rate (MRR) of TC4 titanium alloy material in vertical vibratory finishing equipment, an orthogonal experiment was conducted using TC4 titanium alloy plate as the experimental specimen. We performed variance analysis of the impact of vibration frequency, the phase difference, the mass of upper eccentric block, and the mass of lower eccentric block on the MRR. We then drew the main effect diagram and analyzed the influence of various process parameters on the MRR. Mathematical regression and a neural network were used to con

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Sakamoto, Shuichi, Tetsushi Shintani, and Tsukasa Hasegawa. "Simplified Limp Frame Model for Application to Nanofiber Nonwovens (Selection of Dominant Biot Parameters)." Nanomaterials 12, no. 17 (2022): 3050. http://dx.doi.org/10.3390/nano12173050.

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This study aimed to discover an easy and precise prediction model for the acoustic properties of nanofiber nonwoven fabrics. For this purpose, a prediction model focusing on the two dominant parameters in the Limp frame model—bulk density and flow resistivity—was suggested. The propagation constant and characteristic impedance was generated from the effective density and effective volume modulus generated by the predictive model and treated as a one-dimensional transfer matrix. The sound absorption coefficient was then estimated using the transfer matrix approach. The trend of the normal Incid

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Hu, Jing, Zhengbao Zhang, Senwei Lin, et al. "Application of All-Ages Lead Model Based on Monte Carlo Simulation of Preschool Children’s Exposure to Lead in Guangdong Province, China." Sustainability 15, no. 2 (2023): 1068. http://dx.doi.org/10.3390/su15021068.

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Introduction: Lead (Pb) poisoning in children is a major public health issue worldwide. The physiologically based pharmacokinetic model (PBPK model) has been extensively utilized in Pb exposure risk assessment and can connect external exposure with biological monitoring data. This study aimed to combine a Monte Carlo simulation with the all-ages lead model (ALLM) to quantify the heterogeneity and uncertainty of certain parameters in the population. The parameters of the all-ages lead model based on Monte Carlo simulation (ALLM + MC) were localized in Guangdong Province. Our study discusses the

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Wang, Zuoyan, Lihui Ren, Na Liu, and Jianjun Peng. "Utility of Hematological Parameters in Predicting No-Reflow Phenomenon After Primary Percutaneous Coronary Intervention in Patients With ST-Segment Elevation Myocardial Infarction." Clinical and Applied Thrombosis/Hemostasis 24, no. 7 (2018): 1177–83. http://dx.doi.org/10.1177/1076029618761005.

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Objective: Because the no-reflow phenomenon in patients with ST- segment elevation myocardial infarction can lead to poor outcomes and early identification of patients at high risk may alter the clinical outcome, we aimed to study possible differences in the predictive utility among hematological parameters for early identification of patients at high risk of the no-reflow phenomenon during the primary percutaneous coronary intervention. Methods: A total of 612 patients with ST-segment elevation myocardial infarction who underwent primary percutaneous coronary intervention were enrolled. The p

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Yoo, Jang, Jaeho Lee, Miju Cheon, et al. "Predictive Value of 18F-FDG PET/CT Using Machine Learning for Pathological Response to Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell Lung Cancer." Cancers 14, no. 8 (2022): 1987. http://dx.doi.org/10.3390/cancers14081987.

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We investigated predictions from 18F-FDG PET/CT using machine learning (ML) to assess the neoadjuvant CCRT response of patients with stage III non-small cell lung cancer (NSCLC) and compared them with predictions from conventional PET parameters and from physicians. A retrospective study was conducted of 430 patients. They underwent 18F-FDG PET/CT before initial treatment and after neoadjuvant CCRT followed by curative surgery. We analyzed texture features from segmented tumors and reviewed the pathologic response. The ML model employed a random forest and was used to classify the binary outco

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Zhang, Junling, Min Mei, Jun Wang, et al. "The Construction and Application of a Deep Learning-Based Primary Support Deformation Prediction Model for Large Cross-Section Tunnels." Applied Sciences 14, no. 2 (2024): 912. http://dx.doi.org/10.3390/app14020912.

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The deformation of tunnel support structures during tunnel construction is influenced by geological factors, geometrical factors, support factors, and construction factors. Accurate prediction of tunnel support structure deformation is crucial for engineering safety and optimizing support parameters. Traditional methods for tunnel deformation prediction have often relied on numerical simulations and model experiments, which may not always meet the time-sensitive requirements. In this study, we propose a fusion deep neural network (FDNN) model that combines multiple algorithms with a complement

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Virgili, A., F. Osti, C. Maranini, and M. Corazza. "Photodynamic therapy: parameters predictive of pain." British Journal of Dermatology 162, no. 2 (2009): 460–61. http://dx.doi.org/10.1111/j.1365-2133.2009.09583.x.

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Claas, Frans H. J. "Predictive parameters for in vivo alloreactivity." Transplant Immunology 10, no. 2-3 (2002): 137–42. http://dx.doi.org/10.1016/s0966-3274(02)00060-6.

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Tang, Yi-Wei, Gary W. Procop, Xiaotian Zheng, Jeffrey L. Myers, and Glenn D. Roberts. "Histologic Parameters Predictive of Mycobacterial Infection." American Journal of Clinical Pathology 109, no. 3 (1998): 331–34. http://dx.doi.org/10.1093/ajcp/109.3.331.

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Marks, Jeffrey L., Ross McMahon, and Larry I. Lipshultz. "Predictive Parameters of Successful Varicocele Repair." Journal of Urology 136, no. 3 (1986): 609–12. http://dx.doi.org/10.1016/s0022-5347(17)44990-1.

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Kacker, Ravi, Lee C. Zhao, Amanda M. Macejko, and Robert B. Nadler. "RADIOGRAPHIC PARAMETERS PREDICTIVE OF ESWL SUCCESS." Journal of Urology 179, no. 4S (2008): 462. http://dx.doi.org/10.1016/s0022-5347(08)61357-9.

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King, Bethany, Joshua Geary, Donna Crown, et al. "Implementing Novel Predictive Parameters in Apheresis." Biology of Blood and Marrow Transplantation 19, no. 2 (2013): S353—S354. http://dx.doi.org/10.1016/j.bbmt.2012.11.552.

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Ahn, S., D. A. Kahn, S. Zhou, et al. "Dosimetric parameters predictive for esophageal injury." International Journal of Radiation Oncology*Biology*Physics 57, no. 2 (2003): S217. http://dx.doi.org/10.1016/s0360-3016(03)01030-7.

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Mahalingashetti, Prashant B., Kavya Nair, Ashwini Kolur, Sneha Kukanur F, and Prachi S. "Unfamiliar hematological parameters predictive of dengue." Panacea Journal of Medical Sciences 15, no. 1 (2025): 163–67. https://doi.org/10.18231/pjms.v.15.i.1.163-167.

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Dengue is an endemic viral disease in India with frequent outbreaks. The clinical manifestations are protean and can vary from persistent fever for few days and minor hematological changes to severe forms which include life threatening complication of dengue shock syndrome. The present study was aimed to identify hematological markers which can aid reflex dengue serology testing for timely diagnosis. Hematological profile of a total of 53 patients with serological diagnosis of dengue was studied. Thrombocytopenia and leucopenia were seen in 32% and 26% of cases respectively. Raised hematocrit

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Uke, Punam, Akash Bang, and Bhavana Lakhkar. "Blood Investigations as Predictive Tool for Predicting Bleeding Manifestations and Death with Dengue like Illnesses." Indian Journal of Trauma and Emergency Pediatrics 13, no. 1 (2021): 15–19. http://dx.doi.org/10.21088/ijtep.2348.9987.13121.2.

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Background: Dengue has a broad range spectrum of clinical presentations and many times unpredictable clinical evolution and outcome. Though the disease is complex in its manifestations, management is relatively simple hence it is very important to be able to predict which patients are more likely to land up in complications like bleeding manifestation and death. There is a paucity of literature on this. Objective: To study blood investigation parameters as predictive tool for predicting bleeding manifestations and death in children with dengue like illnesses. Methods: We enrolled all the conse

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Mukhametshin, Rustam F., Olga P. Kovtun, and Nadezhda S. Davydova. "Respiratory parameters as a predictor of hospital outcomes in newborns requiring medical evacuation." Russian Journal of Pediatric Surgery, Anesthesia and Intensive Care 12, no. 4 (2023): 441–52. http://dx.doi.org/10.17816/psaic1292.

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BACKGROUND: Assessment of the clinical condition, prediction of risks and possible outcomes during the transfer of newborns remains an important part of the work of transport teams. Respiratory disorders remain a significant indication for transfer to medical organizations of a higher level of care. AIM: To study the predictive value of the parameters of respiratory support in newborns requiring medical evacuation for the outcomes of treatment. MATERIALS AND METHODS: The observational, cohort, retrospective study included data from neonatal to patients on ventilators (286 newborns)

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Wang, Jing, Lei Cui, and Zhengliang Guo. "Predictive value of platelet-related parameters combined with pneumonia severity index score for mortality rate of patients with severe pneumonia." African Health Sciences 23, no. 2 (2023): 202–7. http://dx.doi.org/10.4314/ahs.v23i2.22.

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Background: To analyse the predictive value of platelet-related parameters combined with pneumonia severity index (PSI) score for the mortality rate of patients with severe pneumonia. Methods: The clinical data of 428 severe pneumonia patients were retrospectively analysed. They were divided into survivor and death groups according to 28-day prognosis. Platelet-related parameters platelet count (PLT), mean platelet volume (MPV), platelet-large-cell ratio (P-LCR) and platelet distribution width (PDW) were measured within 24 hours after admission. The receiver operating characteristic (ROC

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Giwa, Abdulwahab, Abel Adekanmi Adeyi, and Saidat Olanipekun Giwa. "Control of a Reactive Distillation Process Using Model Predictive Control Toolbox of MATLAB." International Journal of Engineering Research in Africa 30 (May 2017): 167–80. http://dx.doi.org/10.4028/www.scientific.net/jera.30.167.

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This research work has been carried out to investigate the application of the Model Predictive Control Toolbox contained in MATLAB in controlling a reactive distillation process used for the production of a biodiesel, the model of which was obtained from the work of Giwa et al.1. The optimum values of the model predictive control parameters were obtained by running the mfile program written for the implementation of the control simulation varying the model predictive control parameters (control horizon and prediction horizon) and recording the corresponding integral squared error (ISE). Therea

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Amry, Zul. "Bayesian Estimate of Parameters for ARMA Model Forecasting." Tatra Mountains Mathematical Publications 75, no. 1 (2020): 23–32. http://dx.doi.org/10.2478/tmmp-2020-0002.

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AbstractThis paper presents a Bayesian approach to finding the Bayes estimator of parameters for ARMA model forecasting under normal-gamma prior assumption with a quadratic loss function in mathematical expression. Obtaining the conditional posterior predictive density is based on the normal-gamma prior and the conditional predictive density, whereas its marginal conditional posterior predictive density is obtained using the conditional posterior predictive density. Furthermore, the Bayes estimator of parameters is derived from the marginal conditional posterior predictive density.

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Sobotka, Roman, Otakar Čapoun, Viktor Soukup, and Tomáš Hanuš. "Predictive parameters of metastatic renal cell cancer." Czech Urology 18, no. 2 (2014): 101–11. https://doi.org/10.48095/cccu2014022.

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Yang, Zhao Jun, Yin Kai Wang, Fei Chen, et al. "Prediction of Reliability Model for CNC Machine Tool Based on Exponential Smoothing Model." Advanced Materials Research 548 (July 2012): 495–99. http://dx.doi.org/10.4028/www.scientific.net/amr.548.495.

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In order to manage equipment maintenance work and reduce enterprise cost, a new prediction method of reliability parameters is proposed based on failure time in this paper. The reliability model was built based on failure time, and the reliability parameters were obtained by the empirical modeling method. Then parameters from historical data were used as predictive model parameters, which applied the exponential smoothing methods to establish predictive models based on historical data. Finally, prediction model of reliability was built by the predicted parameters used the above method. With fa

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Veisman, Ido, Amit Oppenheim, Ronny Maman, et al. "A Novel Prediction Tool for Endoscopic Intervention in Patients with Acute Upper Gastro-Intestinal Bleeding." Journal of Clinical Medicine 11, no. 19 (2022): 5893. http://dx.doi.org/10.3390/jcm11195893.

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(1) Background: Predicting which patients with upper gastro-intestinal bleeding (UGIB) will receive intervention during urgent endoscopy can allow for better triaging and resource utilization but remains sub-optimal. Using machine learning modelling we aimed to devise an improved endoscopic intervention predicting tool. (2) Methods: A retrospective cohort study of adult patients diagnosed with UGIB between 2012–2018 who underwent esophagogastroduodenoscopy (EGD) during hospitalization. We assessed the correlation between various parameters with endoscopic intervention and examined the predicti

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Peng, Huachen, Wencheng Tang, Yan Xing, and Xin Zhou. "Semi-Empirical Prediction of Turned Surface Residual Stress for Inconel 718 Grounded in Experiments and Finite Element Simulations." Materials 14, no. 14 (2021): 3937. http://dx.doi.org/10.3390/ma14143937.

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The surface residual stress after machining, especially for finishing, has a vital influence on the shape stability and fatigue life of components. The current study focuses on proposing an original empirical equation to predict turned surface residual stress for Inconel 718 material, taking tool parameters into consideration. The tool cutting-edge angle, rake angle, and inclination angle are introduced for the first time in the equation based on the Inconel 718 material turning experiments and finite element simulations. In this study, the reliability of simulation parameters’ setting is firs

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Na, Myung Hwan, Wanhyun Cho, Sora Kang, and Inseop Na. "Comparative Analysis of Statistical Regression Models for Prediction of Live Weight of Korean Cattle during Growth." Agriculture 13, no. 10 (2023): 1895. http://dx.doi.org/10.3390/agriculture13101895.

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Measuring weight during cattle growth is essential for determining their status and adjusting the feed amount. Cattle must be weighed on a scale, which is laborious and stressful and could hinder growth. Therefore, automatically predicting cattle weight could reduce stress on cattle and farm laborers. This study proposes a prediction system to measure the change in weight automatically during growth using three regression models, using environmental factors, feed intake, and weight during the period. The Bayesian inference and likelihood estimation principles estimate parameters that determine

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Bivoleanu, Anca, Liliana Gheorghe, Bogdan Doroftei, et al. "Predicting Adverse Neurodevelopmental Outcomes in Premature Neonates with Intrauterine Growth Restriction Using a Three-Layered Neural Network." Diagnostics 15, no. 1 (2025): 111. https://doi.org/10.3390/diagnostics15010111.

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Background/Objectives: There is a constant need to improve the prediction of adverse neurodevelopmental outcomes in growth-restricted neonates who were born prematurely. The aim of this retrospective study was to evaluate the predictive performance of a three-layered neural network for the prediction of adverse neurodevelopmental outcomes determined at two years of age by the Bayley Scales of Infant and Toddler Development, 3rd edition (Bayley-III) scale in prematurely born infants by affected by intrauterine growth restriction (IUGR). Methods: This observational retrospective study included p

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Ottos, C. G., and E. O. Isaac. "Modeling of Predictive interaction of Water Parameters in Groundwater." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 1091–96. https://doi.org/10.31142/ijtsrd11292.

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The assessment presented in this article is centred on investigating the interaction of turbidity, total suspended solids and total dissolved solids interaction within the water bearing aquifer of Obite to Oboburu communities of Ogba Egbema Ndoni local government area of Rivers State, Nigeria. Experimental and modeled turbidity, total suspended solids and total dissolved solids investigated are within recommended standard of World Health Organization revealing the reliability of model equation in predicting groundwater parameters distribution upon influence of time, recharge, flow rate. Ottos

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Hussein, Eslam A., Mehrdad Ghaziasgar, Christopher Thron, Mattia Vaccari, and Antoine Bagula. "Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters." Atmosphere 12, no. 5 (2021): 539. http://dx.doi.org/10.3390/atmos12050539.

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Machine learning (ML) has been utilized to predict climatic parameters, and many successes have been reported in the literature. In this paper, we scrutinize the effectiveness of five widely used ML algorithms in the monthly prediction of seasonal climatic parameters using monthly image data. Specifically, we quantify the predictive performance of these algorithms applied to five climatic parameters using various combinations of features. We compare the predictive accuracy of the resulting trained ML models to that of basic statistical estimators that are computed directly from the training da

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Tunthanathip, Thara, Jarunee Duangsuwan, Niwan Wattanakitrungroj, Sasiporn Tongman, and Nakornchai Phuenpathom. "Comparison of intracranial injury predictability between machine learning algorithms and the nomogram in pediatric traumatic brain injury." Neurosurgical Focus 51, no. 5 (2021): E7. http://dx.doi.org/10.3171/2021.8.focus2155.

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OBJECTIVE The overuse of head CT examinations has been much discussed, especially those for minor traumatic brain injury (TBI). In the disruptive era, machine learning (ML) is one of the prediction tools that has been used and applied in various fields of neurosurgery. The objective of this study was to compare the predictive performance between ML and a nomogram, which is the other prediction tool for intracranial injury following cranial CT in children with TBI. METHODS Data from 964 pediatric patients with TBI were randomly divided into a training data set (75%) for hyperparameter tuning an

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Ju, Yeong Jo, Jeong Ran Lim, and Euy Sik Jeon. "Prediction of AI-Based Personal Thermal Comfort in a Car Using Machine-Learning Algorithm." Electronics 11, no. 3 (2022): 340. http://dx.doi.org/10.3390/electronics11030340.

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Defining a passenger’s thermal comfort in a car cabin is difficult because of the narrow environment and various parameters. Although passenger comfort is predicted using a thermal-comfort scale in the overall cabin or a local area, the scale’s range of passenger comfort may differ owing to psychological factors and individual preferences. Among the many factors affecting such comfort levels, the temperature of the seat is one of the direct and significant environmental factors. Therefore, it is necessary to predict the cabin environment and seat-related personal thermal comfort. Accordingly,

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Lo Feudo, Chiara Maria, Luca Stucchi, Giovanni Stancari, et al. "Evaluation of fitness parameters in relation to racing results in 245 Standardbred trotter horses submitted for poor performance examination: A retrospective study." PLOS ONE 18, no. 10 (2023): e0293202. http://dx.doi.org/10.1371/journal.pone.0293202.

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In racehorses, the associations between physiological parameters obtained by exercise testing and racing results have been questioned. We hypothesized that fitness variables measured during a treadmill incremental test may be related with racing outcomes and lifetime career. Our study aimed to investigate the role of fitness parameters obtained during a treadmill test in performance evaluation and career prediction in poorly performing Standardbreds, through a retrospective review of the clinical records of 245 trotters that underwent an incremental treadmill test. Several fitness parameters w

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Xiao, Yang, Han Wang, Lina Han, Zhibin Huang, Guorong Lyu, and Shilin Li. "Predictive value of anthropometric and biochemical indices in non-alcoholic fatty pancreas disease: a cross-sectional study." BMJ Open 14, no. 4 (2024): e081131. http://dx.doi.org/10.1136/bmjopen-2023-081131.

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ObjectivesTriglyceride (TG), triglyceride-glucose index (TyG), body mass index (BMI), TyG-BMI and triglyceride to high-density lipoprotein ratio (TG/HDL) have been reported to be reliable predictors of non-alcoholic fatty liver disease. However, there are few studies on potential predictors of non-alcoholic fatty pancreas disease (NAFPD). Our aim was to evaluate these and other parameters for predicting NAFPD.DesignCross-sectional study design.SettingPhysical examination centre of a tertiary hospital in China.ParticipantsThis study involved 1774 subjects who underwent physical examinations fro

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