Application of Dragonnet and Conformal Inference for Estimating Individualized Treatment Effects for Personalized Stroke Prevention: Retrospective Cohort Study by Sermkiat Lolak, John Attia, Gareth J McKay, Ammarin Thakkinstian

“…The study also aimed to determine and validate the causal effects of known stroke risk factors—hypertension (HT), diabetes mellitus (DM), dyslipidemia (DLP), and atrial fibrillation (AF)—using both a conventional causal model and machine learning models. MethodsA retrospective cohort study was conducted using data from 275,247 high-risk patients treated at Ramathibodi Hospital, Thailand, between 2010 and 2020. …”
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Evaluación bibliométrica de la investigación sobre Tecnologías Habilitadoras para la Transformación Digital en Cuba by Yudayly Stable-Rodríguez, Lee Yang Díaz-Chieng, Alfredo Javier Pérez Gamboa, Esteban Rodríguez-Torres

“…Hubo mayoría de las temáticas artificial inteligente, machine learning, learning systems, human computer interaction y data minning relacionados con la salud. …”
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Perceptions in 3.6 Million Web-Based Posts of Online Communities on the Use of Cancer Immunotherapy: Data Mining Using BERTopic by Xingyue Wu, Chun Sing Lam, Ka Ho Hui, Herbert Ho-fung Loong, Keary Rui Zhou, Chun-Kit Ngan, Yin Ting Cheung

“…ObjectiveThis study aims to use BERTopic (a topic modeling technique that is an extension of the Bidirectional Encoder Representation from Transformers machine learning model) to explore the perceptions of online cancer communities regarding immunotherapy. …”
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The combination of CDX2 expression status and tumor-infiltrating lymphocyte density as a prognostic factor in adjuvant FOLFOX-treated patients with stage III colorectal cancers by Ji-Ae Lee, Hye Eun Park, Hye-Yeong Jin, Lingyan Jin, Seung Yeon Yoo, Nam-Yun Cho, Jeong Mo Bae, Jung Ho Kim, Gyeong Hoon Kang

“…Methods Stage III CRC tissues were assessed for CDX2 loss using immunohistochemistry and analyzed for their densities of CD8 TILs in both intraepithelial (iTILs) and stromal areas using a machine learning-based analytic method. Results CDX2 loss was significantly associated with a higher density of CD8 TILs in both intraepithelial and stromal areas. …”
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Recent Advances in Surface Functionalized 3D Electrocatalyst for Water Splitting by Nadira Meethale Palakkool, Mike P. C. Taverne, Owen Bell, Jonathan D. Mar, Vincent Barrioz, Yongtao Qu, Chung‐Che Huang, Ying‐Lung Daniel Ho

“…Future research directions include exploring new materials for 3D printing and alternative electrocatalysts alongside leveraging theoretical and machine‐learning approaches to accelerate the development of competitive materials for water electrolysis.…”
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Multi-scale feature fusion of deep convolutional neural networks on cancerous tumor detection and classification using biomedical images by U. M. Prakash, S. Iniyan, Ashit Kumar Dutta, Shtwai Alsubai, Janjhyam Venkata Naga Ramesh, Sachi Nandan Mohanty, Khasim Vali Dudekula

“…Current developments in medical technology, like smart recognition and analysis utilizing machine learning (ML) and deep learning (DL) techniques, have transformed the analysis and treatment of these conditions. …”
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Abnormal Operation Detection of Automated Orchard Irrigation System Actuators by Power Consumption Level by Shahriar Ahmed, Md Nasim Reza, Md Rejaul Karim, Hongbin Jin, Heetae Kim, Sun-Ok Chung

“…Future research will integrate advanced machine learning with signal processing to improve fault detection accuracy and evaluate the scalability and adaptability of the system for larger orchards and diverse agricultural applications.…”
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Advances in the pilot point inverse method: Où En Sommes-Nous maintenant? by White, Jeremy, Lavenue, Marsh

“…The paper ends with newly developed applications of the PPM, given modern machine learning capabilities, and some foreshadowing as to where the PPM might evolve.…”
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Predicting local control of brain metastases after stereotactic radiotherapy with clinical, radiomics and deep learning features by Hemalatha Kanakarajan, Wouter De Baene, Patrick Hanssens, Margriet Sitskoorn

“…A Random Forest machine learning algorithm was employed to train four models using: (1) clinical features only; (2) clinical and radiomics features; (3) clinical and DL features; and (4) clinical, radiomics, and DL features. …”
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Caracterización de fincas productoras de café (Coffea arabica L.) orgánico y convencional en la zona de Intag, Ecuador by Telmo Fernando Basantes-Vizcaíno, Julio Cesar Alegre Orihuela

“…Se realizó un análisis de correspondencia mediante información mutua en RStudio, con la finalidad de verificar si existe relación entre las variables categóricas, en segunda instancia se realizó una Correlación de Person mediante análisis de correlación con un modelo lineal generalizado con elastic nets (Machine Learning Models) y un cruce de validación para separar los datos numéricos, a fin de determinar un modelo de regresión logística. …”
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Depression Detection and Diagnosis Based on Electroencephalogram (EEG) Analysis: A Systematic Review by Kholoud Elnaggar, Mostafa M. El-Gayar, Mohammed Elmogy

“…By focusing on studies that integrate EEG with machine learning (ML) and deep learning (DL) techniques, we systematically analyze methods utilizing EEG signals to identify depression biomarkers. …”
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Soil carbon-food synergy: sizable contributions of small-scale farmers by Toshichika Iizumi, Nanae Hosokawa, Rota Wagai

“…Methods We applied random forest machine learning models to global gridded datasets on crop yield (wheat, maize, rice, soybean, sorghum and millet), soil, climate and agronomic management practices from the 2000s (n = 1808 to 8123). …”
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Real-Time Plant Health Detection Using Deep Convolutional Neural Networks by Mahnoor Khalid, Muhammad Shahzad Sarfraz, Uzair Iqbal, Muhammad Umar Aftab, Gniewko Niedbała, Hafiz Tayyab Rauf

“…In the twenty-first century, machine learning is a significant part of daily life for everyone. …”
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Enhancing cotton irrigation with distributional actor–critic reinforcement learning by Yi Chen, Meiwei Lin, Zhuo Yu, Weihong Sun, Weiguo Fu, Liang He

“…Subsequently, we innovatively integrated a distributional reinforcement learning method—an effective machine learning technique for continuous control problems. …”
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Assessing CNN and Semantic Segmentation Models for Coarse Resolution Satellite Image Classification in Subcontinental Scale Land Cover Mapping by Tesfaye Adugna, Wenbo Xu, Jinlong Fan, Haitao Jia, Xin Luo

“…Based on studies using high-medium resolution images, convolutional neural networks (CNNs) and semantic segmentation have shown superiority over classical machine learning (ML), particularly in small-scale mapping. …”
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Editorial: The Artificial Intelligence in Translational Medicine and Biomedical Research, How Future Can be Shaped?!! by Karzan Abdulmuhsin Mohammad

“…The new era of translational medicine is facing a special and unique transition as the Artificial Intelligence (AI) and Machine Learning (ML) advance. The latest findings in many laboratories around the globes are promising and paved the way toward extraordinary innovative development in the field of medicine and medical research. …”
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Skin Microbiota: Mediator of Interactions Between Metabolic Disorders and Cutaneous Health and Disease by Magdalini Kreouzi, Nikolaos Theodorakis, Maria Nikolaou, Georgios Feretzakis, Athanasios Anastasiou, Konstantinos Kalodanis, Aikaterini Sakagianni

“…For example, elevated butyrate levels in psoriasis have been associated with reduced Th17-mediated inflammation, while the presence of specific Lactobacillus strains has shown potential to modulate immune tolerance in atopic dermatitis. Furthermore, machine learning models are increasingly used to integrate multi-omics data, enabling personalized interventions. …”
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Unleashing the potential of chatbots in mental health: bibliometric analysis by Qing Han, Chenyang Zhao

“…High-frequency terms such as “ChatGPT”, “machine learning”, and “large language models” underscore the current state of research, highlighting the cutting-edge advancements and frontiers in this field.ConclusionsThis study provides an in-depth analysis of the most prominent countries, institutions, publications, collaboration status, and research topics associated with utilization of chatbots in mental health over the last decade. …”
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The global research of magnetic resonance imaging in Alzheimer’s disease: a bibliometric analysis from 2004 to 2023 by Xiaoyu Sun, Xiaoyu Sun, Jianghua Zhu, Jianghua Zhu, Ruowei Li, Ruowei Li, Yun Peng, Yun Peng, Lianggeng Gong, Lianggeng Gong

“…Keyword burst analysis revealed that “machine learning” and “deep learning” were the keywords that frequently appeared in the past 6 years. …”
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Enhancing Tropical Cyclone Risk Assessments: A Multi-Hazard Approach for Queensland, Australia and Viti Levu, Fiji by Jane Nguyen, Michael Kaspi, Kade Berman, Cameron Do, Andrew B. Watkins, Yuriy Kuleshov

“…This study develops an integrated methodology for TC multi-hazard risk assessment that utilises the following individual assessments of key TC risk components: a variable enhanced bathtub model (VeBTM) for storm surge-driven hazards, a random forest (RF) machine learning model for rainfall-induced flooding, and indicator-based indices for exposure and vulnerability assessments. …”
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