Comparison of time-to-event machine learning models in predicting biliary complication and mortality rate in liver transplant patients by Aref Andishgar, Sina Bazmi, Kamran B. Lankarani, Seyed Alireza Taghavi, Mohammad Hadi Imanieh, Gholamreza Sivandzadeh, Samira Saeian, Nazanin Dadashpour, Alireza Shamsaeefar, Mahdi Ravankhah, Hamed Nikoupour Deylami, Reza Tabrizi, Mohammad Hossein Imanieh

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Construction and Validation of a Machine Learning-Based Risk Prediction Model for Sleep Quality in Patients with OSA by Tong Y, Wen K, Li E, Ai F, Tang P, Wen H, Guo B

“…Yangyang Tong,1 Kuo Wen,2 Enguang Li,3 Fangzhu Ai,4 Ping Tang,5 Hongjuan Wen,3 Botang Guo5 1Department of Pulmonary Oncology, Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Changchun, Jilin, 130117, People’s Republic of China; 2College of Traditional Chinese Medicine, Changchun University of Traditional Chinese Medicine, Changchun, Jilin, 130117, People’s Republic of China; 3College of Health Management, Changchun University of Traditional Chinese Medicine, Changchun, Jilin, 130117, People’s Republic of China; 4School of Nursing, Jinzhou Medical University, Jinzhou, Liaoning Province, 121000, People’s Republic of China; 5Department of General Practice, the Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, 518001, People’s Republic of ChinaCorrespondence: Botang Guo, Department of General Practice, The Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, 518001, People’s Republic of China, Email hmugbt@hrbmu.edu.cn Hongjuan Wen, College of Health Management, Changchun University of Traditional Chinese Medicine, Changchun, Jilin, 130117, People’s Republic of China, Email wenhongjuan2004@163.comObjective: The aim of this study was to establish a risk prediction model for sleep quality in patients with obstructive sleep apnea (OSA) based on machine learning algorithms with optimal predictive performance.Methods: A total of 400 OSA patients were included in this study. …”
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Myoelectric signal and machine learning computing in gait pattern recognition for flat fall prediction by Shuo Zhang, Biao Chen, Chaoyang Chen, Maximillian Hovorka, Jin Qi, Jie Hu, Gui Yin, Marie Acosta, Ruby Bautista, Hussein F. Darwiche, Bryan E. Little, Carlos Palacio, John Hovorka

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Xpert Ultra stool testing to diagnose tuberculosis in children in Ethiopia and Indonesia: a model-based cost-effectiveness analysis by Peter J Dodd, Nyashadzaishe Mafirakureva, Rina Triasih, Debebe Shaweno, Jens Levy, Eveline Klinkenberg, Petra de Haas, Ineke Spruijt, Nastiti Kaswandani, Ahmed Bedru, Melaku Gebremichael, Edine W Tiemersma

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Enhancing ovarian cancer prognosis with an artificial intelligence-derived model: Multi-omics integration and therapeutic implications by You Wu, Kunyu Wang, Yan Song, Bin Li

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Meso-scale network analysis of resting state-fMRI brain network connectivity performs poorly as a prognostic tool in critically ill traumatic brain injury patients by Jonathan Tjerkaski, William H. Thompson, Bo-Michael Bellander, Eric P. Thelin, Peter Fransson

“…To further substantiate these results, we used the Louvain algorithm to detect the community structure of each TBI patient, to assess whether it differs from that of healthy individuals.We performed fMRI on 44 TBI patients who were admitted to the neurocritical care unit. …”
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An Innovative Smart Irrigation Using Embedded and Regression-Based Machine Learning Technologies for Improving Water Security and Sustainability by Abdennabi Morchid, Abdennacer Elbasri, Zahra Oughannou, Hassan Qjidaa, Rachid El Alami, Badre Bossoufi, Saleh Mobayen, Pawel Skruch

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BERT Mutation: Deep Transformer Model for Masked Uniform Mutation in Genetic Programming by Eliad Shem-Tov, Moshe Sipper, Achiya Elyasaf

“…This work represents a pivotal step toward integrating state-of-the-art deep learning into evolutionary algorithms, pushing the boundaries of adaptive optimization in GP.…”
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Radiomic prediction for durable response to high‐dose methotrexate‐based chemotherapy in primary central nervous system lymphoma by Haoyi Li, Mingming Xiong, Ming Li, Caixia Sun, Dao Zheng, Leilei Yuan, Qian Chen, Song Lin, Zhenyu Liu, Xiaohui Ren

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Data augmentation using SMOTE technique: Application for prediction of burst pressure of hydrocarbons pipeline using supervised machine learning models by Afzal Ahmed Soomro, Ainul Akmar Mokhtar, Masdi B. Muhammad, Mohamad Hanif Md Saad, Najeebullah Lashari, Muhammad Hussain, Abdul Sattar Palli

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A Hybrid Ensemble Learning Framework for Predicting Lumbar Disc Herniation Recurrence: Integrating Supervised Models, Anomaly Detection, and Threshold Optimization by Mădălina Duceac (Covrig), Călin Gheorghe Buzea, Alina Pleșea-Condratovici, Lucian Eva, Letiția Doina Duceac, Marius Gabriel Dabija, Bogdan Costăchescu, Eva Maria Elkan, Cristian Guțu, Doina Carina Voinescu

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Innovative Tailored Semantic Embedding and Machine Learning for Precise Prediction of Drug-Drug Interaction Seriousness by Ayman Mohamed Mostafa, Alaa S. Alaerjan, Hisham Allahem, Bader Aldughayfiq, Meshrif Alruily, Alshaimaa A. Tantawy, Mohamed Ezz

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Revolutionizing pharmacology: AI-powered approaches in molecular modeling and ADMET prediction by Irfan Pathan, Arif Raza, Adarsh Sahu, Mohit Joshi, Yamini Sahu, Yash Patil, Mohammad Adnan Raza, Ajazuddin

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Radiomics analysis of thoracic vertebral bone marrow microenvironment changes before bone metastasis of breast cancer based on chest CT by Hao-Nan Zhu, Yi-Fan Guo, YingMin Lin, Zhi-Chao Sun, Xi Zhu, YuanZhe Li

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RGB and RGNIR image dataset for machine learning in plastic waste detectionZENODO by Owen Tamin, Ervin Gubin Moung, Jamal Ahmad Dargham, Samsul Ariffin Abdul Karim, Ashraf Osman Ibrahim, Nada Adam, Hadia Abdelgader Osman

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Medical Data over Sound—CardiaWhisper Concept by Radovan Stojanović, Jovan Đurković, Mihailo Vukmirović, Blagoje Babić, Vesna Miranović, Andrej Škraba

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In-Memory Versus Disk-Based Computing with Random Forest for Stock Analysis: A Comparative Study by Chitra Joshi, Chitrakant Banchorr, Omkaresh Kulkarni, Kirti Wanjale

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Rethinking the Paradigm of Using Ps for Diagnosing Compartment Syndrome by Yasser Bouklouch, BSc, MPH, July Agel, MA, ATC, William T. Obremskey, MD, MPH, MMHC, Andrew H. Schmidt, MD, Kathy Liu, MB, ChB, Jerald R. Westberg, MPH, Matthew Zakariah, BSc, Eli Bunzel, MD, Greer Henry, MSc, Andres Fidel Diaz, MD, Thierry Bégué, MD, Mitchell Bernstein, MD, Edward J. Harvey, MDCM, MSc

“…The combinations were tested for predictive power using 2 machine learning algorithms. Results:. Pressure on palpation was the strongest clinical predictor of ACS while pain was the weakest. …”
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Exploration of ductility for refractory high entropy alloys via interpretive machine learning by Shaolong Zheng, Lingwei Yang, Liyang Fang, Chenran Xu, Guanglong Xu, Yifang Ouyang, Xiaoma Tao

“…This study constructs an ML model for accurate ductility prediction from sparse compositional data, accelerating the design of ductile RHEAs within infinite compositional space. Two ML algorithms, decision tree (DT) and CatBoost, are trained using physical parameters, with CatBoost demonstrating superior performance in RHEA ductility classification. …”
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CO21 | Viscoelastic testing in inherited bleeding disorders: a cross-sectional comparison between viscoelastic coagulation monitoring (VCM) and rotational thromboelastometry (ROTE...

“…Conversely, ROTEM adds EXTEM and FIBTEM channels that examine tissue-factor activation and fibrinogen contribution—features not fully captured by VCM—and therefore could give a more specific characterisation of the coagulation profile for algorithm-based decisions.   …”
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