Model for End-Stage Liver Disease Score Predicts the Mortality of Patients with Coronary Heart Disease Who Underwent Percutaneous Coronary Intervention by You Chen, Min Han, Ying-Ying Zheng, Feng Zhu, Aikebai Aisan, Tunike Maheshati, Yi-Tong Ma, Xiang Xie

“…This study indicated that the MELD score had a strong prediction for long-term mortality in CHD patients who underwent PCI.…”
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Prediction method of S-wave velocities in tight sandstone reservoirs – a case study of CO2 geological storage area in Ordos Basin by Li Lin, Wang Haofan, Ma Jinfeng

“…The shear wave velocity prediction model for this area was then optimized. Moreover, by analyzing actual well logging data, the methodology is validated and it shows high accuracy when using the dominant pore shapes to predict the S-wave velocity. …”
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Evaluating the predictive potential of RSM and ANN models in treatment of greywater-syrup mixture using Ekowe clay-PEM microbial fuel cell by Livinus A. Obasi, Cornelius O. Nevo

“…A 24 central composite design and a three-layered (4:n:1) feedforward ANN architecture trained by a backpropagation algorithm were used to study and predict the MFC process performance criteria. The ANN gave the best prediction with n = 10 neurons. …”
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Guy’s Stone Score (GSS) Based on Intravenous Pyelogram (IVP) Findings Predicting Upper Pole Access Percutaneous Nephrolithotomy (PCNL) Outcomes by Bannakij Lojanapiwat, Pattara Rod-Ong, Pruit Kitirattrakarn, Wilaiwan Chongruksut

“…Objective. To predict the success rate and complications following percutaneous nephrolithotomy via the upper pole using the Guy’s Stone Score (GSS) based on the findings of a preoperative intravenous pyelogram (IVP). …”
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Assessing machine learning for fair prediction of ADHD in school pupils using a retrospective cohort study of linked education and healthcare data by Johnny Downs, Robert Stewart, Alice Wickersham, Sumithra Velupillai, Lucile Ter-Minassian, Natalia Viani, Lauren Cross

“…To improve access to services, accurate predictions of populations at high risk of ADHD are needed for effective resource allocation. …”
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Childhood trauma and subclinical PTSD symptoms predict adverse effects and worse outcomes across two mindfulness-based programs for active depression. by Nicholas K Canby, Elizabeth A Cosby, Roman Palitsky, Deanna M Kaplan, Josie Lee, Golnoosh Mahdavi, Adrian A Lopez, Roberta E Goldman, Kristina Eichel, Jared R Lindahl, Willoughby B Britton

“…Childhood sexual abuse predicted attrition in study 2. Finally, multiple forms of childhood trauma and PTSD symptoms predicted MRSE, while total childhood trauma, childhood emotional abuse, and subclinical PTSD symptoms predicted lasting MRAE. …”
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Comparison of Different Machine Learning Methodologies for Predicting the Non‐Specific Treatment Response in Placebo Controlled Major Depressive Disorder Clinical Trials by Roberto Gomeni, Françoise Bressolle‐Gomeni

“…At this purpose, six machine learning methodologies (gradient boosting machine, lasso regression, logistic regression, support vector machines, k‐nearest neighbors, and random forests) were compared to the multilayer perceptrons artificial neural network (ANN) methodology for predicting the probability of individual non‐specific treatment response. …”
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Risk Factors Analysis of Cutaneous Adverse Drug Reactions Caused by Targeted Therapy and Immunotherapy Drugs for Oncology and Establishment of a Prediction Model by Zimin Zhang, Mingyang Zhu, Weiwei Jiang

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Applying Clustered KNN Algorithm for Short-Term Travel Speed Prediction and Reduced Speed Detection on Urban Arterial Road Work Zones by Hyun Su Park, Yong Woo Park, Oh Hoon Kwon, Shin Hyoung Park

“…This study developed and verified a travel speed prediction model based on the travel speed and work zone statistics collected from the advanced traffic management system (ATMS) real-time data in Daegu, South Korea. …”
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Risk factors analysis and prediction model establishment of acute kidney injury after heart valve replacement in patients with normal renal function by Xiaofan Huang, Xiaofan Huang, Xiangyu Sun, Jiangang Song, Yongqiang Wang, Jindong Liu, Jindong Liu, Yu Zhang, Yu Zhang

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Prediction of BlastInduced Ground Vibration (BIGV) of Metro Construction Using Difference Evolution AlgorithmOptimized Gaussian Process (DE-GP) by Tengfei Jiang, Annan Jiang, Shuai Zheng, Mengfei Xu

“…Therefore, accurate prediction of blast-induced ground vibration (BIGV) is a prerequisite for safe construction. …”
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A Novel Rubber Composite Sleeper-Deformation-Prediction Model Based on Response Surface Method (RSM) and Machine Learning (ML) Techniques by Abdulmumin Ahmed Shuaibu, Zhiping Zeng, Ibrahim Hayatu Hassan, Wang Weidong, Hassan Suleiman Otuoze, Suleiman Abdulhakeem, Bushrah Baba Abdulrahman

“…To ensure accurate ML predictions, the dataset was split into 70% for training and 30% for testing. …”
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Nomogram based on dual-energy computed tomography to predict the response to induction chemotherapy in patients with nasopharyngeal carcinoma: a two-center study by Huanhuan Ren, Junhao Huang, Yao Huang, Bangyuan Long, Mei Zhang, Jing Zhang, Huarong Li, Tingting Huang, Daihong Liu, Ying Wang, Jiuquan Zhang

“…This study aimed to investigate whether the parameters from DECT can predict the response to induction chemotherapy in NPC patients in two centers. …”
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Characteristics That Predict Psoriatic Arthritis by the Classification Criteria for Psoriatic Arthritis in Patients With Juvenile Idiopathic Arthritis 18 Years After Disease Onset by Agnes Szentpetery, Mia Glerup, Kristiina Aalto, Ellen D. Arnstad, Anders Fasth, Troels Herlin, Charlotte Myrup, Ellen Nordal, Suvi Peltoniemi, Marite Rygg, Veronika Gjertsen Rypdal, Lillemor Berntson

“…Objective The purposes of this study were to assess the clinical characteristics of patients with juvenile idiopathic arthritis (JIA) who fulfill the ClASsification criteria for Psoriatic ARthritis (CASPAR) 18 years after disease onset in a population‐based setting and to identify features likely to predict psoriatic arthritis (PsA). Methods Patients with JIA from defined geographic regions of Denmark, Finland, Norway, and Sweden with disease onset from 1997 to 2000 were enrolled prospectively and followed up for 18 years. …”
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Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models by RN Singh, P. Krishnan, C. Bharadwaj, Sonam Sah, B. Das

“…Abstract Crop health assessment and early yield predictions are highly crucial under biotic stress conditions for crop management and market planning by farmers and policy planners. …”
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Solving of Two-Dimensional Unsteady-State Heat-Transfer Inverse Problem Using Finite Difference Method and Model Prediction Control Method by Shoubin Wang, Rui Ni

“…The residual principle is introduced to estimate the optimized regularization parameter in the model prediction control method, thereby obtaining a more precise inversion result. …”
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Maternal Serum Ferritin, C-Reactive Protein, and Procalcitonin Levels for Predicting Subclinical Intra-Amniotic Infection in Preterm Premature Rupture of Membrane by Balsam N. Nahedh, Mahdi M. Shalal

“…Objectives: To evaluate the use of serum ferritin, C-reactive protein, and procalcitonin as early indicators for predictions of subclinical intra-amniotic fluid infection.   …”
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Lipids and C-reactive protein predict anhedonia and reward circuit functional connectivity responses to anti-cytokine and dopaminergic therapies in patients with depression by Aditya Singh, Mandakh Bekhbat, David R. Goldsmith, Ngoc-Anh Le, Evanthia C. Wommack, Zhihao Li, Ebrahim Haroon, Jennifer C. Felger

“…Lipid scores similarly predicted CRP-related increases in reward circuit FC after L-DOPA (r = 0.53, p < 0.01) but not placebo (r = 0.20, p = 0.34). …”
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Predicting the Impact of Multiwalled Carbon Nanotubes on the Cement Hydration Products and Durability of Cementitious Matrix Using Artificial Neural Network Modeling Technique by Babak Fakhim, Abolfazl Hassani, Alimorad Rashidi, Parviz Ghodousi

“…Another finding of this study was that the ANN prediction in higher ages was more precise.…”
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The Addition of the Geriatric Nutritional Risk Index to the Prognostic Scoring Systems Did Not Improve Mortality Prediction in Trauma Patients in the Intensive Care Unit by Cheng-Shyuan Rau, Ching-Hua Tsai, Sheng-En Chou, Wei-Ti Su, Shiun-Yuan Hsu, Ching-Hua Hsieh

“…However, none of these predictive scoring models showed a significant improvement in prediction when the GNRI variable is incorporated. …”
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