Construction and validation of a machine learning based prognostic prediction model for children with traumatic brain injury by Yongwei Wei, Jiandong Wang, Yu Su, Fan Zhou, Huaili Wang

“…ObjectiveThis study aimed to establish a prediction model for the short-term prognosis of children with traumatic brain injury (TBI) using machine learning algorithms.MethodsThe clinical data of children with TBI who were treated in the First Affiliated Hospital of Zhengzhou University were retrospectively analyzed. …”
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Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets by Dinesh Chellappan, Harikumar Rajaguru

“…The study explores the potential of machine learning for improved diabetes prediction using microarray gene expression data and PIMA data set. …”
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Construction and validation of immune prognosis model for lung adenocarcinoma based on machine learning by Jinyu Zheng, Xiaoyi Xu, Xianguo Chen, Xianshuai Li, Miao Fu, Yiping Zheng, Jie Yang

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Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data by Paul Fergus, Carl Chalmers, Naomi Matthews, Stuart Nixon, André Burger, Oliver Hartley, Chris Sutherland, Xavier Lambin, Steven Longmore, Serge Wich

“…Here, we present an integrated approach that combines deep learning-based vision and language models to improve ecological reporting using data from camera traps. …”
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Supervised Machine Learning Thyroid Carcinoma Diagnosis Using Wide-Field SHG Microscopy by Yaraslau Padrez, Radu Hristu, Igor Timoshchenko, Lucian G. Eftimie, Danielis Rutkauskas, Lena Golubewa

“…Since the structure of the collagen capsule surrounding thyroid nodules provides diagnostically valuable information, label-free imaging with second harmonic generation (SHG) microscopy combined with machine learning (ML)-based analysis offers a promising approach for automated classification. …”
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Context-aware implicit neural representations to compress Earth systems model data by Farinaz Mostajeran, Nikhil M. Pawar, Jonathan M. Villarreal, Salah A. Faroughi

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A bibliometric review on in silico drug repurposing: Performance analysis, science mapping and text mining (2000–2023) by Alejandro I. Trejo-Castro, Emmanuel Martinez-Ledesma, Antonio Martinez-Torteya

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Enhancing Pollen Prediction in Beijing, a Chinese Megacity: Leveraging Ensemble Learning Models for Greater Accuracy by Wenxi Ruan, Ziming Li, Zhaobin Sun, Xingqin An, Yuxin Zhao, Shuwen Zhang, Yinglin Liang, Yaqin Bu, Jingyi Xin, Xiaoyi Hang

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Deep learning-based semantic segmentation for rice yield estimation by analyzing the dynamic change of panicle coverage by Hyeok-Jin Bak, Eun-Ji Kim, Ji-Hyeon Lee, Sungyul Chang, Dongwon Kwon, Woo-Jin Im, Woon-Ha Hwang, Jae-Ki Chang, Nam-Jin Chung, Wan-Gyu Sang

“…Time-series panicle coverage data, extracted from the segmented images, were fitted to a piecewise function to model their growth and decline dynamics. …”
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Forecasting Eruptions at Steamboat Geyser: Time Scales, Differentiability, and Detectability of Seismic Precursors Through Data‐Driven Methods by Alberto Ardid, Anna Barth, David Dempsey, Michael Manga, Shane J. Cronin

“…Four seismic amplitude measures and 700 time‐series features were computed from the seismic data. A template matching analysis identified an optimal 18‐hr window for detecting precursors. …”
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Integrating Experimental Analysis and Gradient Boosting for the Durability Assessment of Lime-Based Mortar in Acidic Environment by Ali Taheri, Nima Azimi, Daniel V. Oliveira, Joaquim Tinoco, Paulo B. Lourenço

“…This paper presents a comprehensive study of the mechanical properties of lime-based mortar in an acidic environment, employing both experimental analysis and machine learning to model techniques. …”
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BGATT-GR: accurate identification of glucocorticoid receptor antagonists based on data augmentation combined with BiGRU-attention by Watshara Shoombuatong, Pakpoom Mookdarsanit, Nalini Schaduangrat, Lawankorn Mookdarsanit

“…Therefore, this study proposes an innovative deep learning-based hybrid framework (termed BGATT-GR) that leverages a data augmentation method, a bidirectional gated recurrent unit (BiGRU), and a self-attention mechanism (ATT) to attain more accurate identification of GR antagonists. …”
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Geospatial analysis of short sleep duration and cognitive disability in US adults: a multi-state study using machine learning techniques by Tue T. Te, Alex A. T. Bui, Constance H. Fung, Mary Regina Boland

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Listen, look, link and learn: a stepwise approach to use narrative quality data within resident-family-nursing staff triads in nursing homes for quality improvements by Jos M G A Schols, Jan P H Hamers, Katya Y J Sion, Johanna E R Rutten, Erica de Vries, Sandra M G Zwakhalen, Gaby Odekerken-Schröder, Hilde Verbeek

“…Therefore, this study introduces an approach that enables the use of narrative quality of care data to learn from and improve with.Design A cross-sectional mixed-methods study in which qualitative data were collected with the narrative quality assessment method Connecting Conversations and interpreted for analysis.Methods Connecting Conversations was used to collect narrative data about experienced quality of care in nursing homes according to residents, their families and nursing staff (triads). …”
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Health Care Professionals and Data Scientists’ Perspectives on a Machine Learning System to Anticipate and Manage the Risk of Decompensation From Patients With Heart Failure: Quali... by Joana Seringa, Anna Hirata, Ana Rita Pedro, Rui Santana, Teresa Magalhães

“…ObjectiveThis study aimed to explore the perspectives of health care professionals and data scientists regarding the relevance, challenges, and potential benefits of using machine learning (ML) models to predict decompensation from patients with HF. …”
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Spatio-Temporal Agnostic Sampling for Imbalanced Multivariate Seasonal Time Series Data: A Study on Forest Fires by Abdul Mutakabbir, Chung-Horng Lung, Kshirasagar Naik, Marzia Zaman, Samuel A. Ajila, Thambirajah Ravichandran, Richard Purcell, Srinivas Sampalli

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A Study of the Non-Linear Relationship Between Urban Morphology and Vitality in Heritage Areas Based on Multi-Source Data and Machine Learning: A Case Study of Dalian by He Li, Li Miao

“…This study employs XGBoost and SHAP, utilizing geographic big data and deep learning techniques, to determine whether the urban form elements impacting the vitality of heritage and urban areas are the same or exhibit different spatial distributions and diurnal variations. …”
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A distributionally robust machine learning model of simultaneous classification and feature selection under data uncertainty: Theory, methods, and application to the identification... by Q.Y. Huang, N.D. Dizon, N. Jeyakumar, V. Jeyakumar

“…In this paper, we introduce an efficient machine learning method based on robust Support Vector Machines (SVMs) that simultaneously classifies data and selects relevant features whilst accounting for data uncertainty. …”
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A critical realist analysis of nursing educators' willingness to learn and teach patient safety in Sri Lanka: Study protocol. by D M A P Dissanayake, S S P Warnakulasuriya

“…The data will be analyzed using SPSS and AMOS for quantitative data, and NVivo for deductive thematic analysis.…”
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Evaluating the digital transformation in health sciences education: a thematic analysis of higher education teachers’ perspectives by Mirko Prosen, Sabina Ličen

“…Data were analysed using thematic analysis. Three main themes were identified: (1) Digital transformation in education, (2) Educational efficacy and evolution, and (3) Overcoming barriers and embracing opportunities. …”
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