Predicting Discharge Location among Low-Energy Hip Fracture Patients Using the Score for Trauma Triage in the Geriatric and Middle-Aged (STTGMA) by Sanjit R. Konda, Hesham Saleh, Ariana Lott, Kenneth A. Egol

“…The STTGMA tool allows for prediction of unfavorable discharge location in hip fracture patients. …”
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Advanced Network Traffic Prediction Using Deep Learning Techniques: A Comparative Study of SVR, LSTM, GRU, and Bidirectional LSTM Models by Wang Yuxin

“…Accurate prediction of network traffic patterns is essential for optimizing network resource allocation, managing congestion, and strengthening cybersecurity. …”
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Can the Implied Information of Options Predict the Liquidity of Stock Market? A Data-Driven Research Based on SSE 50ETF Options by Hairong Cui, Jinfeng Fei, Xunfa Lu

“…It shows that implied volatility and implied volatility spread are good indicators to predict stock market liquidity. In contrast, variance risk premium cannot predict the liquidity of stock market. …”
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Investigation and prediction of fatigue performance of SLM 316 L stainless steel based on small build orientation variations and heat treatment effects by Andrei Yankin, Asma Perveen, Didier Talamona

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Very Short-Term Blackout Prediction for Grid-Tied PV Systems Operating in Low Reliability Weak Electric Grids of Developing Countries by Benson H. Mbuya, Aleksandar Dimovski, Marco Merlo, Thomas Kivevele

“…The model was composed of an adaptive similar day (ASD) module that predicts 15 minutes ahead from a sliding window lookup table spanning 2 weeks prior to the prediction target day, after which ASD prediction was fused with RF prediction, giving a final optimised RF-ASD blackout prediction model. …”
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Machine learning analysis of pathological images to predict 1-year progression-free survival of immunotherapy in patients with small-cell lung cancer by Hirotaka Matsumoto, Hiroaki Akamatsu, Nobuyuki Yamamoto, Yuki Sato, Daichi Fujimoto, Yoshihiko Taniguchi, Motohiro Tamiya, Yasuhiro Koh, Junya Fukuoka, Hisashi Tanaka, Naoki Furuya, Ryota Shibaki, Tsukasa Nozawa, Akira Sano, Yuka Kitamura, Takashi Kijima, Toshihide Yokoyama, Satoru Miura, Akito Hata, Jun Sugisaka

“…The primary outcome was the mean area under the curve (AUC) of machine learning models in predicting the 1-year PFS.Results We analyzed 100,544 patches of pathological images from 78 patients. …”
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A prediction model of liver fat fraction and presence of non-alcoholic fatty liver disease (NAFLD) among patients with overweight or obesity by Jie Chen, Jiang Yue, Jingjing Fu, Shengyun He, Qianjing Liu, Minglan Yang, Wang Zhang, Hua Xu, Qing Lu, Jing Ma

“…The Spearman correlation coefficient between liver fat fraction ascertained by MRI-PDFF and that predicted by the SHSI equation amounts to 0.74. Consequently, the SHSI equation affords a dependable means of predicting the presence of NAFLD and liver fat accumulation within the overweight and obese population.…”
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Gallbladder Wall Thickness as a Non-Invasive Tool for Prediction of Esophageal Varices in Patients with Hepatitis C Related Chronic Liver Disease by Munir Ahmed, Maliha Aziz, Shahzad Riyaz, Mohammad Salih, Rashed Nazir, Muslim Atiq

“…GBWT at 3.7mm predicted advanced/high-risk varices with a sensitivity of 80.65%, specificity of 75.76%, PPV of 90.36%, NPV of 58.15%, and diagnostic accuracy of 79.37% with AUC of 0.813. …”
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Predicting individuals’ behaviors during a pandemic: why we might have acted as we did during the COVID-19 pandemic by Shaikha S. Aldousari, William Ickes

“…Because individual differences in this situation matter, it is important to determine which variables accurately predict which behaviors.…”
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Predicted indirectly recognizable HLA epitopes scores and clinical outcomes after haploidentical stem cell transplantation in pediatric patients with relapsed neuroblastoma by Eun Seop Seo, Eun Seop Seo, In Hwa Jeong, In Hwa Jeong, Hee Young Ju, Ju Kyung Hyun, Ji Won Lee, Keon Hee Yoo, Won Young Heo, Ki Woong Sung, Hee Won Cho, Eun-Suk Kang

“…IntroductionThe Predicted Indirectly ReCognizable HLA Epitopes (PIRCHE) model is a recently developed algorithm that predicts indirect T-cell recognition by calculating the number of such epitopes in donor-recipient pairs.MethodsIn this study, the clinical significance of PIRCHE was evaluated in pediatric patients with relapsed/progressed neuroblastoma undergoing haploidentical stem cell transplantation (haplo-SCT).ResultsA higher PIRCHE-I score was associated with faster platelet recovery (P = 0.007) and lower incidence of hemorrhagic cystitis (13% vs. 41%, P = 0.028) and invasive fungal infections (0% vs. 18%, P = 0.045). …”
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Modeling the Differences in Biochemical Capabilities of Pseudomonas Species by Flux Balance Analysis: How Good Are Genome-Scale Metabolic Networks at Predicting the Differences? by Parizad Babaei, Tahereh Ghasemi-Kahrizsangi, Sayed-Amir Marashi

“…We found out that iMO1086 and iJP962 were able to predict the experimental data and were much more accurate than iSB1139.…”
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Causal machine learning models for predicting low birth weight in midwife-led continuity care intervention in North Shoa Zone, Ethiopia by Wudneh Ketema Moges, Awoke Seyoum Tegegne, Aweke A. Mitku, Esubalew Tesfahun, Solomon Hailemeskel

“…This study adopted causal machine learning (CML) algorithms for predicting LBW in newborns, drawing from midwife-led continuity care (MLCC). …”
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Application of Artificial Intelligence Models to Predict the Tensile Strength of Glass Fiber-Modified Cemented Backfill Materials during the Mine Backfill Process by Lei Zhu, Wenzhe Gu, Zhicheng Liu, Fengqi Qiu

“…The results indicate that the four hybrid artificial intelligence models show a latent capacity for forecasting the tensile strength of GFCBM, and according to the order from high to low, the prediction ability of the four prediction models is as follows: ABC-SVM, GA-SVM, SSA-SVM, and DE-SVM, and the corresponding R values are 0.9555, 0.9539, 0.9413, and 0.9359, respectively. …”
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Prediction of angiogenesis suppression by myricetin from Aeginetia indica via inhibiting VEGFR2 signaling pathway using computer-aided analysis by Marjanur Rahman Bhuiyan, Khondoker Shahin Ahmed, Md Sharif Reza, Hemayet Hossain, Syed Mumtahin Mannan Siam, Shahriar Nayan, Sarah Jafrin, Sadikur Rahman Shuvo, A.F.M.Shahid Ud Daula

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A Novel Data-Driven Terminal Iterative Learning Control with Iteration Prediction Algorithm for a Class of Discrete-Time Nonlinear Systems by Shangtai Jin, Zhongsheng Hou, Ronghu Chi

“…Furthermore, the control performance is improved by using more information of predictive terminal outputs, which are predicted along the iteration axis and used to update the control law and estimation law. …”
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Trusts on nerd immunity and health-associated apparatus predict the level of obedience of indonesians toward health resilience threat-anticipation enforced by the government by Nurlaila Ika, Amalya Dewi Meta

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Hybrid Method for Predictive Transient Nonlinear Analysis of a Radiative-Convective Moving Porous Fin with Temperature-variant Internal Heat Generation by Gbeminiyi Musibau Sobamowo, Ridwan Oladotun Olagbadamosi, Arinola Bola Ajayi, Antonio Marcos de Oliveira Siqueira

“…The semi-numerical solution provides a very good platform for the predictive analyses of the extended surfaces. …”
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Predictive value of the New Zealand Early Warning Score for early mortality in low-acuity patients discharged at scene by paramedics: an observational study by Bridget Dicker, Verity Frances Todd, Tony Smith, Andy Swain, Graham Howie, Melanie Moylan, Aroha Brett

“…Objectives The utility of New Zealand Early Warning Score (NZEWS) for prediction of adversity in low-acuity patients discharged at scene by paramedics has not been investigated. …”
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GA-Attention-Fuzzy-Stock-Net: An optimized neuro-fuzzy system for stock market price prediction with genetic algorithm and attention mechanism by Burak Gülmez

Subjects: “…Stock market price prediction…”
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Optimizing Pile Bearing Capacity Prediction Using Specific Random Forest Models Optimized by Meta-Heuristic Algorithms for Enhanced Geomechanically Applications by Nengyuan Chen

“…To achieve highly accurate predictions of Pile Bearing Capacity (PBC), the study employs a cutting-edge approach featuring Specific Random Forest (RF) prediction models, strategically enhanced with two potent meta-heuristic algorithms: the Snake Optimizer (SO) and the Equilibrium Optimizer (EO). …”
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