Defining analytical skills for human resources analytics: A call for standardization by Konrad Kulikowski

“…PURPOSE: Human resources (HR) analytics systems, powered by big data, AI algorithms, and information technology, are increasingly adopted by organizations to enhance HR’s impact on business performance. …”
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A network toxicology and machine learning approach to investigate the mechanism of kidney injury from melamine and cyanuric acid co-exposure by Zhan Wang, Zhaokai Zhou, Zihao Zhao, Junjie Zhang, Shengli Zhang, Luping Li, Yingzhong Fan, Qi Li

“…Potential target proteins were identified using ChEMBL, STITCH, and GeneCards databases, and hub genes were screened using three machine learning algorithms: LASSO regression, Random Forest, and Molecular Complex Detection. …”
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Modeling pine forest growing stock volume in subtropical regions of China using airborne Lidar data by Zige Lan, Xiandie Jiang, Guiying Li, Yagang Lu, Hongwen Yao, Dengsheng Lu

“…More research is needed to quantitatively examine different contribution of sample sizes, modeling algorithms, variables from different sources, and stratification factors on modeling results, so that we can design an optimal procedure for GSV modeling using airborne Lidar data.…”
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Metabolic dysfunction-associated steatotic liver disease (MASLD) biomarkers and progression of lower limb arterial calcification in patients with type 2 diabetes: a prospective coh... by Damien Denimal, Maharajah Ponnaiah, Franck Phan, Anne-Caroline Jeannin, Alban Redheuil, Joe-Elie Salem, Samia Boussouar, Pauline Paulstephenraj, Suzanne Laroche, Chloé Amouyal, Agnès Hartemann, Fabienne Foufelle, Olivier Bourron

“…The predictive ability of these biomarkers of MASLD on LLACS progression was assessed through univariate and multivariate linear regression models, principal component regression analysis, as well as machine learning algorithms. Results During the follow-up period, LLACS increased in 127 (85%) of the 150 patients with T2D. …”
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Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study by Chengkun Sun, Erin Mobley, Michael Quillen, Max Parker, Meghan Daly, Rui Wang, Isabela Visintin, Ziad Awad, Jennifer Fishe, Alexander Parker, Thomas George, Jiang Bian, Jie Xu

“…Given the distinct pathology of colon cancer (CC) and rectal cancer (RC), we created separate prediction models for each cancer type with various ML algorithms. We assessed multiple prediction time windows (ie, 0, 1, 3, and 5 y) and ensured robustness through propensity score matching to account for confounding variables including sex, race, ethnicity, and birth year. …”
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Prediction of acute and chronic kidney diseases during the post-covid-19 pandemic with machine learning models: utilizing national electronic health records in the USResearch in co... by Yue Zhang, Nasrollah Ghahramani, Runjia Li, Vernon M. Chinchilli, Djibril M. Ba

“…We aimed to use large electronic health records (EHR) and ML algorithms to predict the incidence of AKI and CKD during the post-pandemic period, assess the necessity of including COVID-19 infection history as a predictor, and develop a practical webpage application for clinical use. …”
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Intercomparison of Total Column Ozone Between Ozonesonde Observations and Multi-source Products Across 4 Regions by Zhu Langfeng, Wang Rongbo, Fang Yonglin, Liu Mengqi, Zheng Xiangdong, Wu Hao

“…The accuracy and stability of Cef(Z) in stratosphere can be improved through improvements in mechanical design or updates to correction algorithms. Further improvements are expected to reduce measurement bias and enhance data reliability under varying atmospheric conditions.…”
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The Comprehensive Analysis of Weighted Gene Co-Expression Network Analysis and Machine Learning Revealed Diagnostic Biomarkers for Breast Implant Illness Complicated with Breast Ca... by Huang Z, Wang H, Pang H, Zeng M, Zhang G, Liu F

“…Enrichment analysis, the protein–protein interaction network (PPI), and machine learning algorithms were performed to explore the hub genes. …”
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Polyamine metabolism related gene index prediction of prognosis and immunotherapy response in breast cancer by Ruoya Wang, Shouliang Cai, Qing Gao, Yidong Chen, Xue Han, Fangjian Shang, Chunyan Liang, Guolian Zhu, Bo Chen

“…Additionally, we analyzed the immune microenvironment and enriched pathways across different subtypes using multiple algorithms. Finally, the “oncoPredict” R package was used to assess potential drug sensitivities in high-risk and low-risk groups.ResultsSeventeen polyamine metabolism genes were identified. …”
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Daily time-use compositions of physical behaviours and its association with evaluative and experienced wellbeing: a multilevel compositional analysis by Anantha Narayanan, Scott Duncan, Conal Smith, Flora Le, Lisa Mackay, Julia McPhee, Basile Chaix, Tom Stewart

“…Time-use data were processed using UK Biobank machine learning algorithms. We employed Bayesian multilevel compositional analysis to investigate how time-use behaviours, and reallocating time between behaviours, were associated with both life satisfaction and momentary affective states. …”
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Identifying Common Diagnostic Biomarkers and Therapeutic Targets between COPD and Sepsis: A Bioinformatics and Machine Learning Approach by Li X, Xiao Y, Yang M, Zhang X, Yuan Z, Zhang Z, Zhang H, Liu L, Zhao M

“…Functional enrichment analyses were conducted to explore the biological roles of these genes. LASSO and SVM-RFE algorithms identified shared diagnostic genes, which were evaluated using receiver operating characteristic (ROC) curves. …”
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Urban heat island classification through alternative normalized difference vegetation index by N. Chanpichaigosol, C. Chaichana, D. Rinchumphu

“…Future studies could expand to other urban areas, incorporate additional variables, and refine predictive algorithms for broader applications. This study will serve as a foundation for the development of future real-time monitoring tools that will enable proactive and sustainable solutions to UHI problems.…”
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Exploring the role of breastfeeding, antibiotics, and indoor environments in preschool children atopic dermatitis through machine learning and hygiene hypothesis by Jinyang Wang, Haonan Shi, Xiaowei Wang, Enhong Dong, Jian Yao, Yonghan Li, Ye Yang, Tingting Wang

“…Furthermore, advanced machine learning algorithms have provided fresh insights into the interactions among various risk factors. …”
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Identifying Data-Driven Clinical Subgroups for Cervical Cancer Prevention With Machine Learning: Population-Based, External, and Diagnostic Validation Study by Zhen Lu, Binhua Dong, Hongning Cai, Tian Tian, Junfeng Wang, Leiwen Fu, Bingyi Wang, Weijie Zhang, Shaomei Lin, Xunyuan Tuo, Juntao Wang, Tianjie Yang, Xinxin Huang, Zheng Zheng, Huifeng Xue, Shuxia Xu, Siyang Liu, Pengming Sun, Huachun Zou

“…CCP2–4 had significantly higher risks of CIN2+ (CCP2: OR 2.07 [95% CI: 2.03‐2.12], CCP3: 3.88 [3.78‐3.97], and CCP4: 4.47 [4.33‐4.63]) and CIN3+ (CCP2: 2.10 [2.05‐2.14], CCP3: 3.92 [3.82‐4.02], and CCP4: 4.45 [4.31‐4.61]) compared to CCP1 (P ConclusionsThis study underscores the potential of leveraging machine learning algorithms and large-scale routine electronic health records to enhance CCP strategies. …”
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Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review by Kapongo D. Lumamba, Gordon Wells, Delon Naicker, Threnesan Naidoo, Adrie J. C. Steyn, Mandlenkosi Gwetu

“…The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection. …”
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Evaluation of Neural, Systemic and Extracerebral Activations During Active Walking Tasks in Older Adults Using fNIRS by Meltem Izzetoglu, Roee Holtzer

“…Such involved designs further allowed the implementation of advanced signal processing algorithms to separate and evaluate neural, systemic and extracerebral signal contributions on the overall measurements. …”
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Construction and Validation of a Hospital Mortality Risk Model for Advanced Elderly Patients with Heart Failure Based on Machine Learning by Shang S, Wei M, Lv H, Liang X, Lu Y, Tang B

“…Shuai Shang,1,2,* Meng Wei,1,2,* Huasheng Lv,1,2,* Xiaoyan Liang,1,2 Yanmei Lu,1,2 Baopeng Tang1,2 1Department of Cardiac Pacing and Electrophysiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, People’s Republic of China; 2Xinjiang Key Laboratory of Cardiac Electrophysiology and Remodeling, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Baopeng Tang, Department of Cardiac Pacing and Electrophysiology, Xinjiang Key Laboratory of Cardiac Electrophysiology and Remodeling, The First Affiliated Hospital of Xinjiang Medical University, No. 137, South Liyushan Road, Xinshi Zone, Urumqi, People’s Republic of China, Email tangbaopeng1111@163.com Yanmei Lu, Department of Cardiac Pacing and Electrophysiology, Xinjiang Key Laboratory of Cardiac Electrophysiology and Remodeling, The First Affiliated Hospital of Xinjiang Medical University, No. 137, South Liyushan Road, Xinshi Zone, Urumqi, People’s Republic of China, Email gracy@189.cnPurpose: This study aimed to develop and validate a model based on machine learning algorithms to predict the risk of in-hospital death among advanced elderly patients with Heart Failure (HF).Methods: A total of 4580 advanced elderly patients who were admitted to the hospital and diagnosed with HF from May 2012 to September 2023 were included in this study, among whom 552 cases (12.5%) died. …”
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Research and Design of an Active Light Source System for UAVs Based on Light Intensity Matching Model by Rui Ming, Tao Wu, Zhiyan Zhou, Haibo Luo, Shahbaz Gul Hassan

“…The experimental results show that the UAV equipped with an active light source has improved the recall of yoloV7 and RT-DETR recognition algorithms by 30% and 29.6%, the mAP50 by 21% and 19.5%, and the recognition accuracy by 13.1% and 13.6, respectively. …”
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Leveraging machine learning in nursing: innovations, challenges, and ethical insights by Sophie So Wan Yip, Sheng Ning, Niki Yan Ki Wong, Jeffrey Chan, Kei Shing Ng, Bernadette Oi Ting Kwok, Robert L. Anders, Simon Ching Lam

“…However, key challenges include ethical considerations, such as data privacy, algorithmic bias, and patient autonomy, which necessitate ongoing research and regulatory oversight.ConclusionsML in nursing offers transformative potential across patient care, education, and operational efficiency, which is balanced by significant challenges and ethical considerations. …”
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The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT by Krzysztof Wach, Cong Doanh Duong, Joanna Ejdys, Rūta Kazlauskaitė, Pawel Korzynski, Grzegorz Mazurek, Joanna Paliszkiewicz, Ewa Ziemba

“…In our opinion they are as follows: (i) no regulation of the AI market and urgent need for regulation, (ii) poor quality, lack of quality control, disinformation, deepfake content, algorithmic bias, (iii) automation-spurred job losses, (iv) personal data violation, social surveillance, and privacy violation, (v) social manipulation, weakening ethics and goodwill, (vi) widening socio-economic inequalities, and (vii) AI technostress. …”
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