Differentiating Pulmonary Nodule Malignancy Using Exhaled Volatile Organic Compounds: A Prospective Observational Study by Guangyu Lu, Zhixia Su, Xiaoping Yu, Yuhang He, Taining Sha, Kai Yan, Hong Guo, Yujian Tao, Liting Liao, Yanyan Zhang, Guotao Lu, Weijuan Gong

“…We applied five machine learning (ML) algorithms to develop predictive models which were evaluated using area under the curve (AUC), sensitivity, specificity, and other relevant metrics. …”
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Identification of EARS2 as a Potential Biomarker with Diagnostic, Prognostic, and Therapeutic Implications in Colorectal Cancer by Wang L, Deng X, Tang J, Gong Y, Bu S, Li Z, Liao B, Ding Y, Dai T, Liao Y, Li Y

“…This study identifies key genes associated with lactic acid metabolism and explore their impact on CRC.Patients and Methods: This study utilized data from The Cancer Genome Atlas, Gene Expression Omnibus, other public databases, and our institutional resources. Machine learning identified key lactate metabolism-related genes. …”
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CYP3A5 promotes glioblastoma stemness and chemoresistance through fine-tuning NAD+/NADH ratio by Wentao Hu, Xiaoteng Cui, Hongyu Liu, Ze Li, Xu Chen, Qixue Wang, Guolu Zhang, Er Wen, Jinxin Lan, Junyi Chen, Jialin Liu, Chunsheng Kang, Ling Chen

“…Methods A multi-step process of machine learning algorithms was implemented to construct the glioma stemness-related score (GScore). …”
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Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals by Gulay Tasci, Prabal Datta Barua, Dahiru Tanko, Tugce Keles, Suat Tas, Ilknur Sercek, Suheda Kaya, Kubra Yildirim, Yunus Talu, Burak Tasci, Filiz Ozsoy, Nida Gonen, Irem Tasci, Sengul Dogan, Turker Tuncer

“…<b>Background:</b> Electroencephalography (EEG) signal-based machine learning models are among the most cost-effective methods for information retrieval. …”
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Risk prediction models for feeding intolerance in patients with enteral nutrition: a systematic review and meta-analysis by Huijiao Chen, Jin Han, Jing Li, Jianhua Xiong, Dong Wang, Mingming Han, Yuehao Shen, Wenli Lu

“…In the field of model construction, only one study employed the use of multiple machine-learning techniques for the development of a model. …”
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Spatial deconvolution from bulk DNA methylation profiles determines intratumoral epigenetic heterogeneity by Binbin Liu, Yumo Xie, Yu Zhang, Guannan Tang, Jinxin Lin, Ze Yuan, Xiaoxia Liu, Xiaolin Wang, Meijin Huang, Yanxin Luo, Huichuan Yu

“…Conclusion By developing a 7-loci panel using a machine learning approach combined with the QASM assay for PCR-based application, we present a valuable method for evaluating intratumoral heterogeneity. …”
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Quo vadis autoimmune hepatitis? - Summary of the 5th international autoimmune hepatitis group research workshop 2024Keypoints by Bastian Engel, David N. Assis, Mamatha Bhat, Jan Clusmann, Joost PH. Drenth, Alessio Gerussi, María-Carlota Londoño, Ye Htun Oo, Ida Schregel, Marcial Sebode, Richard Taubert

“…The specific objectives of this year's 5th Workshop were: (1) To further improve diagnostics. (2) Initiate clinical trials including knowledge transfer on drugs from extrahepatic immune-mediated diseases, including B cell-depleting CAR T cells. (3) Utilisation of multi-omics approaches to improve the understanding of disease pathogenesis. (4) Application of machine learning-based approaches established in oncology or transplantation medicine to improve diagnosis and outcome prediction in AIH.…”
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AI prediction model for endovascular treatment of vertebrobasilar occlusion with atrial fibrillation by Zhi-Xin Huang, Andrea M. Alexandre, Alessandro Pedicelli, Xuying He, Quanlong Hong, Yongkun Li, Ping Chen, Qiankun Cai, Aldobrando Broccolini, Luca Scarcia, Serena Abruzzese, Carlo Cirelli, Mauro Bergui, Andrea Romi, Erwah Kalsoum, Giulia Frauenfelder, Grzegorz Meder, Simona Scalise, Maria Porzia Ganimede, Luigi Bellini, Bruno Del Sette, Francesco Arba, Susanna Sammali, Andrea Salcuni, Sergio Lucio Vinci, Giacomo Cester, Luisa Roveri, Xianjun Huang, Wen Sun

“…While 45.1% achieved favorable outcomes at 90 days, our advanced machine learning approach unveiled subtle interaction effects among clinical variables not captured by traditional statistical methods. …”
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QUIET WARRIOR – Rationale and design: An ancillary study to the Women's IschemiA TRial to Reduce Events in Nonobstructive CAD (WARRIOR) by Osama Dasa, Eileen Handberg, Damini Dey, Pinaki Sarder, Margaret C. Lo, Balaji K. Tamarappoo, Steven M. Smith, Leslee J. Shaw, C. Noel Bairey Merz, Carl J. Pepine

“…Advanced imaging techniques and machine-learning models will be employed to quantify plaque features and predict clinical outcomes. …”
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In-Season Automated Mapping of Xinjiang Cotton Based on Cumulative Spectral and Phenological Characteristics by Yongsheng Huang, Yaozhong Pan, Yu Zhu, Xiufang Zhu, Xingsheng Xia, Qiong Chen, Jufang Hu, Hongyan Che, Xuechang Zheng, Lingang Wang

“…Methods based on machine learning, and deep learning, rely on a large number of training samples, which is time-consuming and laborious. …”
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Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska by Pratima Khatri-Chhetri, Hans-Erik Andersen, Bruce Cook, Sean M. Hendryx, Liz van Wagtendonk, Van R. Kane

“…In this study, we present a framework for forest type classification combining field plots and high-resolution remote sensing data using machine learning models in the boreal forest of Interior Alaska. …”
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Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial. by Hee Yun Seol, Pragya Shrestha, Joy Fladager Muth, Chung-Il Wi, Sunghwan Sohn, Euijung Ryu, Miguel Park, Kathy Ihrke, Sungrim Moon, Katherine King, Philip Wheeler, Bijan Borah, James Moriarty, Jordan Rosedahl, Hongfang Liu, Deborah B McWilliams, Young J Juhn

“…<h4>Measurements</h4>Intervention was a quarterly A-GPS report to clinicians including relevant clinical information for asthma management from EHRs and machine learning-based prediction for risk of asthma exacerbation (AE). …”
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Empirical estimation of saturated soil-paste electrical conductivity in the EU using pedotransfer functions and Quantile Regression Forests: A mapping approach based on LUCAS topso... by Calogero Schillaci, Simone Scarpa, Felipe Yunta, Aldo Lipani, Fernando Visconti, Gábor Szatmári, Kitti Balog, Triven Koganti, Mogens Greve, Giulia Bondi, Georgios Kargas, Paraskevi Londra, Fuat Kaya, Giuseppe Lo Papa, Panos Panagos, Luca Montanarella, Arwyn Jones

“…In this work, using the LUCAS 2018 dataset, we provide an empirically-derivedpedotransfer function to convert diluted EC1:5 to saturated ECe using the LUCAS soil texture and soil organic carbon, and a framework for ECe mapping with a machine-learning algorithm named Quantile Regression Forest. …”
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Consecutive prediction of adverse maternal outcomes of preeclampsia, using the PIERS-ML and fullPIERS models: A multicountry prospective observational study. by Guiyou Yang, Tünde Montgomery-Csobán, Wessel Ganzevoort, Sanne J Gordijn, Kimberley Kavanagh, Paul Murray, Laura A Magee, Henk Groen, Peter von Dadelszen

“…Among women whose pregnancies are complicated by preeclampsia, the Preeclampsia Integrated Estimate of RiSk (PIERS) models (i.e., the PIERS Machine Learning [PIERS-ML] model, and the logistic regression-based fullPIERS model) accurately identify individuals at greatest or least risk of adverse maternal outcomes within 48 h following admission. …”
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Toward accurate prediction of carbon dioxide (CO2) compressibility factor using tree-based intelligent schemes (XGBoost and LightGBM) and equations of state by Behnam Amiri-Ramsheh, Aydin Larestani, Saeid Atashrouz, Elnaz Nasirzadeh, Meriem Essakhraoui, Ali Abedi, Mehdi Ostadhassan, Ahmad Mohaddespour, Abdolhossein Hemmati-Sarapardeh

“…In this study, two powerful and robust tree-based machine learning (ML) algorithms, namely light gradient boosted machine (LightGBM) and extreme gradient boosting (XGBoost) were utilized to precisely estimate CO2 Z-factor. …”
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Mental health phenotypes of well-controlled HIV in Uganda by Leah H. Rubin, Leah H. Rubin, Leah H. Rubin, Leah H. Rubin, Kyu Cho, Jacob Bolzenius, Julie Mannarino, Rebecca E. Easter, Raha M. Dastgheyb, Aggrey Anok, Stephen Tomusange, Deanna Saylor, Maria J. Wawer, Noeline Nakasujja, Gertrude Nakigozi, Robert Paul

“…We leverage the analytic strengths of machine learning combined with inferential methods to identify novel MH phenotypes among PWH and the underlying explanatory features.MethodsA total of 277 PWH (46% female, median age = 44; 93% virally suppressed [&lt;50copies/mL]) were included in the analyses. …”
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Artificial intelligence can help individualize Wilms tumor treatment by predicting tumor response to preoperative chemotherapy by Ahmed Nashat, Ahmed Alksas, Rasha T. Aboulelkheir, Ahmed Elmahdy, Sherry M. Khater, Hossam M. Balaha, Israa Sharaby, Mohamed Shehata, Mohammed Ghazal, Salama Abd El-Wadoud, Ayman El-Baz, Ahmed Mosbah, Ahmed Abdelhalim

“…Favorable volumetric and histologic responses were achieved in 46 tumors (73.0%) and 38 tumors (60.3%), respectively. Among machine learning classifiers, support vector machine had the best diagnostic performance with an accuracy, sensitivity, and specificity of 95.24%, 95.65%, and 94.12% for volumetric and 84.13%, 89.47%, 88% for histologic response prediction. …”
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Comparative analysis of ChatGPT and Gemini (Bard) in medical inquiry: a scoping review by Fattah H. Fattah, Fattah H. Fattah, Abdulwahid M. Salih, Abdulwahid M. Salih, Ameer M. Salih, Ameer M. Salih, Saywan K. Asaad, Saywan K. Asaad, Abdullah K. Ghafour, Rawa Bapir, Rawa Bapir, Rawa Bapir, Berun A. Abdalla, Berun A. Abdalla, Snur Othman, Sasan M. Ahmed, Sasan M. Ahmed, Sabah Jalal Hasan, Yousif M. Mahmood, Fahmi H. Kakamad, Fahmi H. Kakamad, Fahmi H. Kakamad

“…IntroductionArtificial intelligence and machine learning are popular interconnected technologies. …”
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An atmospheric correction method for Himawari-8 imagery based on a multi-layer stacking algorithm by Menghui Wang, Donglin Fan, Hongchang He, You Zeng, Bolin Fu, Tianlong Liang, Xinyue Zhang, Wenhan Hu

“…For comparative analysis, a near-infrared–shortwave infrared AC method and a general machine learning AC method were also implemented. Model evaluation and validation were performed using a test subset of simulated data and in-situ datasets. …”
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Comprehensive analysis of scRNA-seq and bulk RNA-seq reveals the non-cardiomyocytes heterogeneity and novel cell populations in dilated cardiomyopathy by Siyu He, Chunyu Li, Mingxin Lu, Fang Lin, Sangyu Hu, Junfang Zhang, Luying Peng, Li Li

“…Based on gene-specific expression and prior marker genes, we identified 9 distinct subtypes, including fibroblasts, endothelial cells, myeloid cells, pericytes, T/NK cells, smooth muscle cells, neuronal cells, B cells, and cardiomyocytes. Using machine learning methods to quantify bulk RNA-seq data, we found significant differences in fibroblasts, T cells, and macrophages between DCM and normal samples. …”
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