Developing a rapid screening tool for high-risk ICU patients of sepsis: integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients—model establishment, internal and external validation, and visualization by Songchang Shi, Lihui Zhang, Shujuan Zhang, Jinyang Shi, Donghuang Hong, Siqi Wu, Xiaobin Pan, Wei Lin

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Predicting microRNA target genes using pan-cancer correlation patterns by Shuting Lin, Peng Qiu

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The Effectiveness and Mechanisms of China’s Grain Support Policies in Relation to Grain Yield—An Evaluation of a Wide Range of Policies by Tianjian Li, Fan Yang, Haotian Zhang, Qingning Lin

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Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms by Fude Duan, Mahdiyeh Eslami, Mohammad Khajehzadeh, Ali Basem, Dheyaa J. Jasim, Sivaprakasam Palani

“…In this study, a machine learning approach using a multilayer perceptron artificial neural network (MLP-ANN) has been used to forecast solar radiation, wind speed, temperature, and load data. …”
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Evaluation of the Effectiveness of Multiple Machine Learning Methods in Remote Sensing Quantitative Retrieval of Suspended Matter Concentrations: A Case Study of Nansi Lake in North China by Xiuyu Liu, Zhen Zhang, Tao Jiang, Xuehua Li, Yanyi Li

“…The retrieval accuracy of each model shows that the machine learning models are much more accurate than the linear model. …”
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A Comparative Study of Machine Learning Techniques for Predicting Mechanical Properties of Fused Deposition Modelling (FDM)-Based 3D-Printed Wood/PLA Biocomposite by Prashant Anerao, Atul Kulkarni, Yashwant Munde, Namrate Kharate

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Meteorological Anomalies During Earthquake Preparation: A Case Study for the 1995 Kobe Earthquake (M = 7.3) Based on Statistical and Machine Learning-Based Analyses by Masashi Hayakawa, Shinji Hirooka, Koichiro Michimoto, Stelios M. Potirakis, Yasuhide Hobara

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Using machine learning to forecast peak health care service demand in real-time during the 2022-23 winter season: A pilot in England, UK. by Roger A Morbey, Dan Todkill, Phil Moura, Liam Tollinton, Andre Charlett, Conall Watson, Alex J Elliot

“…Indicators including telehealth cough calls and emergency department (ED) bronchiolitis attendances, both in children under 5 years. We utilised machine learning techniques to train and select models that would best forecast the timing and intensity of peaks up to 28 days ahead. …”
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Automatic segmentation model and machine learning model grounded in ultrasound radiomics for distinguishing between low malignant risk and intermediate-high malignant risk of adnexal masses by Lu Liu, Wenjun Cai, Feibo Zheng, Hongyan Tian, Yanping Li, Ting Wang, Xiaonan Chen, Wenjing Zhu

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Comparative analysis of visible and near-infrared (Vis-NIR) spectroscopy and prediction of moisture ratio using machine learning algorithms for jujube dried under different conditions by Seda Günaydın, Necati Çetin, Cevdet Sağlam, Kamil Sacilik, Ahmad Jahanbakhshi

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Harnessing Machine Learning and Nomogram Models to Aid in Predicting Progression-Free Survival for Gastric Cancer Patients Post-Gastrectomy with Deficient Mismatch Repair(dMMR) by Yifan Li, JinFeng Ma, Wenhua Cheng

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Corrigendum for “Development of a machine learning-based model to predict hepatic inflammation in chronic hepatitis B patients with concurrent hepatic steatosis: a cohort study” by Fajuan Rui, Jie Li

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Ionospheric Response to the 24–27 February 2023 Solar Flare and Geomagnetic Storms Over the European Region Using a Machine Learning–Based Tomographic Technique by Ting Li, Dunyong Zheng, Changyong He, Fei Ye, Pengfei Yuan, Yibin Yao, Mengguang Liao, Jian Xie

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Machine learning identification of a novel vasculogenic mimicry-related signature and FOXM1’s role in promoting vasculogenic mimicry in clear cell renal cell carcinoma by Chao Xu, Sujing Zhang, Jingwei Lv, Yilong Cao, Yao Chen, Hao Sun, Shengtao Dai, Bowei Zhang, Meng Zhu, Yuepeng Liu, Junfei Gu

Subjects: “…Clear cell renal cell carcinoma;Vasculogenic mimicry;Machine learning;FOXM1;Tumor microenvironment;Prognosis;Therapeutic targets…”
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Predicting Factors Affecting Survival Rate in Patients Undergoing On‐Pump Coronary Artery Bypass Graft Surgery Using Machine Learning Methods: A Systematic Review by Alireza Jafarkhani, Behzad Imani, Soheila Saeedi, Amir Shams

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A comprehensive dataset of above-ground forest biomass from field observations, machine learning and topographically augmented allometric models over the Kashmir HimalayaZenodo by Syed Danish Rafiq Kashani, Faisal Zahoor Jan, Imtiyaz Ahmad Bhat, Nadeem Ahmad Najar, Irfan Rashid

“…This dataset provides a manually delineated multi-temporal forest inventory and a comprehensive record of above-ground biomass (AGB) across the Kashmir Himalaya, generated from field observations, advanced remote sensing and machine learning. Data were collected and generated through remote sensing techniques and extensive in-situ measurements of 6220 trees (n=275 plots), including tree diameter at breast height, species composition, and tree density to map forest area and model AGB across varied terrain. …”
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Accurate Modeling of GaN HEMTs Oriented to Analysis of Kink Effects in S₂₂ and h₂₁: An Effective Machine Learning Approach by Zegen Zhu, Gianni Bosi, Antonio Raffo, Giovanni Crupi, Jialin Cai

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The Application of Machine Learning Algorithms to Predict HIV Testing in Repeated Adult Population–Based Surveys in South Africa: Protocol for a Multiwave Cross-Sectional Analysis by Musa Jaiteh, Edith Phalane, Yegnanew A Shiferaw, Refilwe Nancy Phaswana-Mafuya

“…Despite the availability of relevant survey data, there exists a research gap in using machine learning (ML) to analyze and predict HIV testing among adults in South Africa. …”
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Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort Study by Niteesh K Choudhry, Shweta Priyadarshini, Jaganath Swamy, Mridul Mehta

“…Results from our study will generate data to facilitate the creation of machine learning models to predict individual PPGR responses and to facilitate the prescription of personalized diets for individuals with T2D. …”
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LiveDrive AI: A Pilot Study of a Machine Learning-Powered Diagnostic System for Real-Time, Non-Invasive Detection of Mild Cognitive Impairment by Firas Al-Hindawi, Peter Serhan, Yonas E. Geda, Francis Tsow, Teresa Wu, Erica Forzani

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