A roadmap to implementing machine learning in healthcare: from concept to practice by Adam Paul Yan, Adam Paul Yan, Lin Lawrence Guo, Jiro Inoue, Santiago Eduardo Arciniegas, Emily Vettese, Agata Wolochacz, Nicole Crellin-Parsons, Brandon Purves, Steven Wallace, Azaz Patel, Medhat Roshdi, Karim Jessa, Karim Jessa, Bren Cardiff, Lillian Sung, Lillian Sung, Lillian Sung

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Artificial Neural Network (ANN) Approach to Predict Tensile Properties of Longitudinally Placed Fiber Reinforced Polymeric Composites including Interphase by Sagar Chokshi, Piyush Gohil, Vijay Parmar, Vijaykumar Chaudhary

“…Machine Learning has become prevalent nowadays for predicting data on the mechanical properties of various materials and is widely used in various polymeric applications. …”
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Predicting of Ki-67 Expression Level Using Diffusion-Weighted and Synthetic Magnetic Resonance Imaging in Invasive Ductal Breast Cancer by Liying Zhang, Jisen Hao, Jia Guo, Xin Zhao, Xing Yin

“…Pre-T1 could be used to predict the pretreatment Ki-67 expression level in invasive ductal breast cancer.…”
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Machine learning predicted fast progression after initiation of immune checkpoint inhibitors in advanced non-small cell lung cancer by Martin Stockler, Yu Yang Soon, Chee Khoon Lee, Teng Hwee Tan

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Predicting high sensitivity C-reactive protein levels and their associations in a large population using decision tree and linear regression by Somayeh Ghiasi Hafezi, Toktam Sahranavard, Alireza Kooshki, Marzieh Hosseini, Amin Mansoori, Elham Amir Fakhrian, Helia Rezaeifard, Mark Ghamsary, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…Abstract High-sensitivity C-reactive protein (hs-CRP) is a biomarker of inflammation predicting the incidence of different health pathologies. …”
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A Hybrid Model for Soybean Yield Prediction Integrating Convolutional Neural Networks, Recurrent Neural Networks, and Graph Convolutional Networks by Vikram S. Ingole, Ujwala A. Kshirsagar, Vikash Singh, Manish Varun Yadav, Bipin Krishna, Roshan Kumar

“…Soybean yield prediction is one of the most critical activities for increasing agricultural productivity and ensuring food security. …”
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Performance evaluation and prediction of optimal operational conditions for a compact date seeds milling unit using feedforward neural networks by Khaled Abdeen Mousa Ali, Changyou Li, Wang Han, Sali Issa, Mohamed Hamdy Eid, Samy F. Mahmoud, Marwa Abd-Elnaby Mohammed

“…With FNN model accuracy R2 of 0.99974, demonstrating high prediction reliability. Meanwhile, the operating cost was 0.027 $/kg, suitable for small to medium-scale operations. …”
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3D Web Application of Wind Prediction Under Typhoon on Coastal Cities Based on Ensemble k-NN and OpenFOAM by Mamad Tamamadin, Seong-Hoon Kee, Jurng-Jae Yee

“…The adjustment of grid positions through an ensemble k-NN-based typhoon prediction model on the global weather forecast data for OpenFOAM input significantly contributes to the accuracy of urban-scale wind prediction. …”
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The Indigo Molecule Revisited Again: Assessment of the Minnesota Family of Density Functionals for the Prediction of Its Maximum Absorption Wavelengths in Various Solvents by Francisco Cervantes-Navarro, Daniel Glossman-Mitnik

“…The maximum absorption wavelengths predicted for each functional were compared with the known experimental results.…”
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Uric Acid to High-Density Lipoprotein Cholesterol Ratio is a Novel Marker to Predict Functionally Significant Coronary Artery Stenosis by Fanqi Li, Donghui Zhao, Qiuyu Li, Xiaolong Lin, Haoxuan Sun, Qian Fan

“…Studies have shown that some clinical indicators can be used to predict FFR. However, there is little research on this in the Chinese population. …”
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The state-of-the-art review on biochar as green additives in cementitious composites: performance, applications, machine learning predictions, and environmental and economic implications by Ping Ye, Binglin Guo, Huyong Qin, Cheng Wang, Yang Liu, Yuyang Chen, Pengfei Bian, Di Lu, Lei Wang, Weiping Zhao, Yonggan Yang, Li Hong, Peng Gao, Peiyong Ma, Binggen Zhan, Qijun Yu

“…Besides, recent advances in machine learning for predicting the properties of composites and the environmental-economic implications of material are reviewed. …”
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Predicting Axillary Lymph Node Metastasis in Young Onset Breast Cancer: A Clinical-Radiomics Nomogram Based on DCE-MRI by Dong X, Meng J, Xing J, Jia S, Li X, Wu S

“…This study underscores the potential of merging advanced imaging data with clinical insights to refine oncological predictive models. Future research should expand to multicentric studies and include genomic data to boost the nomogram’s generalizability and precision.Keywords: young onset breast cancer, clinical-radiomics nomogram, axillary lymph node metastasis, dynamic contrast-enhanced MRI…”
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Circadian rhythm related genes signature in glioma for drug resistance prediction: a comprehensive analysis integrating transcriptomics and machine learning by Junbo Liao, Yingxing Duan, Xiangwang Xu, Yaxue Liu, Chaohong Zhan, Gelei Xiao

“…Based on the CRRS model, an optimal comprehensive nomogram was created, exhibiting a favorable potential for predicting drug resistance in samples. Immune infiltration, cell–cell communication, and single cell analysis all indicated that high CRRS group was closely related to innate immune cells. …”
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Improving the predictive capability of Framingham Risk Score for the risk of myocardial infarction based on coronary artery calcium score in healthy Singaporeans by Ching Yee Ivory Yeo, John Carson Jr Allen, Weiting Huang, Wei Ying Tan, Siew Ching Kong, Khung Keong Yeo

“…SG FRS++ significantly increases accuracy in risk prediction (NRI = 0.219, P = 0.00254). Conclusion: Existing Singapore cardiovascular disease risk prediction guidelines should be updated to improve risk prediction accuracy. …”
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Clinical characteristics and predictive biomarkers of intensive care unit-acquired weakness in patients with cardiogenic shock requiring mechanical circulatory support by Tae Higuchi, Tomomi Ide, Takeo Fujino, Takeshi Tohyama, Yuta Nagatomi, Tomoyuki Nezu, Masataka Ikeda, Toru Hashimoto, Shouji Matsushima, Keisuke Shinohara, Masaaki Nishihara, Takeshi Iyonaga, Tomohiko Akahoshi, Tomoki Ushijima, Akira Shiose, Shintaro Kinugawa, Hiroyuki Tsutsui, Kohtaro Abe

“…In addition, IL-15 can be a potential biomarker for the early prediction of ICU-AW.…”
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Prediction Model of Cutting Parameters for Turning High Strength Steel Grade-H: Comparative Study of Regression Model versus ANFIS by Adel T. Abbas, Mohanad Alata, Adham E. Ragab, Magdy M. El Rayes, Ehab A. El Danaf

“…In this paper the artificial neural network was used for predicting the surface roughness for different cutting parameters in CNC turning operations. …”
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Predictive value of different baseline optical coherence tomography biomarkers for visual acuity changes in neovascular age-related macular degeneration by Hamid Riazi-Esfahani, Hooshang Faghihi, Fatemeh Bazvand, Mohammadreza Mehrabi Bahar, Hassan Khojasteh, Ahmed Husein Ahmed, Shahin Faghihi, Ali Fakhraie, Mohammad Hossein Zamani, Samin Ghasemi, Esmaeil Asadi Khameneh, Elias Khalili Pour

“…Conclusion Baseline OCT biomarkers, including SIPED, IRF, and PED width, may predict visual acuity changes in nAMD patients treated with aflibercept, highlighting the need for individualized monitoring. …”
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Automated Measurement of Coronary Artery Calcifications and Routine Perioperative Blood Tests Predict Survival in Resected Stage I Lung Cancer by Federica Sabia, MSc, Camilla Valsecchi, MSc, Roberta Eufrasia Ledda, MD, Giorgio Bogani, MD, Riccardo Orlandi, MD, Luigi Rolli, MD, Michele Ferrari, MD, Maurizio Balbi, MD, Alfonso Marchianò, MD, Ugo Pastorino, MD

“…This study aimed to evaluate the performance of a fully automated CAC scoring alone and combined with a prognostic index on the basis of perioperative routine blood tests in predicting 5-year survival of patients with stage I LC. …”
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Long noncoding RNAs predict the survival of patients with colorectal cancer as revealed by constructing an endogenous RNA network using bioinformation analysis by Hui Zhang, Zhuo Wang, Jianzhong Wu, Rong Ma, Jifeng Feng

“…Additionally, combing the risk score and other clinicopathological features, we can better predict the patient's survival probabilities. Furthermore, we validate our model in the GSE38832 dataset. …”
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A machine learning technique for optimizing load demand prediction within air conditioning systems utilizing GRU/IASO model by Meng He, Hui Wang, Myo Thwin

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