Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study by Chanmin Park, Changho Han, Su Kyeong Jang, Hyungjun Kim, Sora Kim, Byung Hee Kang, Kyoungwon Jung, Dukyong Yoon

“…We evaluated machine learning algorithms (random forest [RF], extra-trees classifier, and light gradient boosting machine) and selected the RF model as the final model based on its performance. …”
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Integrating bioinformatics and machine learning to identify biomarkers of branched chain amino acid related genes in osteoarthritis by Xiao-Zhi ZhaYang, Yan-Xiong Chen, Wen-Da Hua, Zheng-Lin Bai, Yun-Peng Jin, Xing-Wen Zhao, Quan-Fu Liu, Zeng-Dong Meng

“…Subsequently, by combining three machine learning algorithms to identify genes with highly correlated OA features. …”
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Twice against thrice-weekly hemodialysis (TATH): a multicenter nonrandomized trial by Mabel Aoun, Serge Finianos, Chadia Beaini, Ghassan Sleilaty, Rita Ghaleb, Nicole Nourie, Sami Kais, Joseph El Hajal, Rachad Alameddine, Celine Boueri, Balsam El Ghoul, Sandy Zeidan, Hiba Azar, Antoine Dfouni, Jenny Hawi, Zeina Mechref, Valerie Hage, Dania Chelala

“…Missing baseline factors were imputed using multiple imputation algorithms, then entered in a logistic regression model to estimate propensity scores. …”
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Bioinformatics analysis of ferroptosis-related biomarkers and potential drug predictions in doxorubicin-induced cardiotoxicity by Jian Yu, Jian Yu, Jiangtao Wang, Xinya Liu, Xinya Liu, Cancan Wang, Cancan Wang, Li Wu, Yuanming Zhang, Yuanming Zhang

“…In addition, anisomycin may also be a potential drug for treating DIC.…”
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Prognostic analysis of sepsis-induced myocardial injury patients using propensity score matching and doubly robust analysis with machine learning-based risk prediction model develo... by Pan Guo, Pan Guo, Li Xue, Fang Tao, Kuan Yang, YuXia Gao, Chongzhe Pei

“…BackgroundSepsis-induced myocardial injury (SIMI) is a severe and common complication of sepsis; However, its definition remains unclear. Prognostic analyses may vary depending on the definition applied. Early prediction of SIMI is crucial for timely intervention, ultimately improving patient outcomes. …”
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Development of a postoperative recurrence prediction model for stage Ⅰ non-small cell lung cancer patients using multimodal data based on machine learning by ZHANG Di, WU Yi, XU Yu

“…Conclusion‍　‍The combined model integrating preoperative CT radiomic features with clinical risk factors may provide an evidence-based framework for evaluating 5-year postoperative recurrence risk in stage Ⅰ NSCLC patients. …”
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Machine learning technique-based four-autoantibody test for early detection of esophageal squamous cell carcinoma: a multicenter, retrospective study with a nested case–control stu... by Yi-Wei Xu, Yu-Hui Peng, Can-Tong Liu, Hao Chen, Ling-Yu Chu, Hai-Lu Chen, Zhi-Yong Wu, Wen-Qiang Wei, Li-Yan Xu, Fang-Cai Wu, En-Min Li

“…Abstract Background Autoantibodies represent promising diagnostic blood-based biomarkers that may be generated prior to the first clinically detectable signs of cancers. …”
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Using machine learning to predict the risk of developing hypertensive disorders of pregnancy using a contemporary nulliparous cohortAJOG Global Reports at a Glance by Jonathan S. Schor, PhD, Adesh Kadambi, BEng, Isabel Fulcher, PhD, Kartik K. Venkatesh, MD, PhD, Mark A. Clapp, MD, MPH, Senan Ebrahim, MD, PhD, Ali Ebrahim, PhD, Timothy Wen, MD, MPH

“…Sensitivity analysis noted superior sensitivity (AUC 0.80 vs 0.65) and specificity (0.65 vs 0.53) of the model compared to traditional risk factor-based algorithms. Conclusion: In cohort of low-risk nulliparous pregnant individuals, a prediction model may accurately predict HDP diagnosis at the time of initiating prenatal care and aid employment of close interval monitoring and prophylactic measures earlier in pregnancy.…”
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Evaluating the Performance and Potential Bias of Predictive Models for Detection of Transthyretin Cardiac Amyloidosis by Jonathan Hourmozdi, MD, MSAI, MA, Nicholas Easton, MSAI, Simon Benigeri, MSAI, James D. Thomas, MD, Akhil Narang, MD, David Ouyang, MD, Grant Duffy, BS, Ross Upton, PhD, Will Hawkes, PhD, Ashley Akerman, PhD, Ike Okwuosa, MD, Adrienne Kline, MD, PhD, Abel N. Kho, MD, Yuan Luo, PhD, Sanjiv J. Shah, MD, Faraz S. Ahmad, MD, MS

“…Screening for ATTR-CM with artificial intelligence and other algorithms may improve timely diagnosis, but these algorithms have not been directly compared. …”
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Integration of Bulk and Single-Cell Transcriptomics Reveals BCL2L14 as a Novel IGKC+ T Cell-Associated Therapeutic Target in Breast Cancer by He J, Akhtar A, Li J, Wei Q, Yuan Y, Ran J, Ma Y, Chen D

“…Next, we applied bulk RNA-seq deconvolution algorithms to estimate the abundance of this subpopulation across breast cancer cohorts. …”
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Will Artificial Intelligence Replace Physicians or Augment Their Capabilities? by Sara Rahmati Roodsari, Alireza Zali, Mohammad Rahmati-Roodsari, Behina Forouzanmehr

“…In medical imaging, deep learning algorithms, especially those trained with genetic algorithms, have demonstrated immense promise to improve the accuracy of pneumonia and COVID-19 diagnosis from chest X-rays. …”
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Can Different Cultivars of <i>Panicum maximum</i> Be Identified Using a VIS/NIR Sensor and Machine Learning? by Gelson dos Santos Difante, Gabriela Oliveira de Aquino Monteiro, Juliana Caroline Santos Santana, Néstor Eduardo Villamizar Frontado, Jéssica Gomes Rodrigues, Aryadne Rhoana Dias Chaves, Dthenifer Cordeiro Santana, Izabela Cristina de Oliveira, Luis Carlos Vinhas Ítavo, Fabio Henrique Rojo Baio, Gabriela Souza Oliveira, Carlos Antonio da Silva Junior, Vanessa Zirondi Longhini, Alexandre Menezes Dias, Paulo Eduardo Teodoro, Larissa Pereira Ribeiro Teodoro

“…<i>Panicum maximum</i> cultivars have distinct characteristics, especially morphological ones related to the leaf structure and coloration, and there may be differences in the spectral behavior captured by sensors. …”
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Obstructive sleep apnea severity and pathophysiological traits in overlap syndrome: Insights from the SNOOzzzE cohort by Janna Raphelson, Ana Sanchez‐Azofra, Jeremy E. Orr, Gabriela Parra, Lana McGinnis, Alexis Salinas, Steven Luu, Scott A. Sands, Ali Azarbarzin, Robert L. Owens, Jose M. Marin, Atul Malhotra, Christopher N. Schmickl

“…Patients with COPD and OSA may have fewer obstructive events, but underlying mechanisms remain unclear. …”
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A mobile app to improve adherence to colorectal cancer screening and post polypectomy surveillance guidelines by María A. Casas, Francisco Schlottmann, Leandro Steinberg, Xavier Bessa, Anna Serradesanferm, Angels Pozo, Sonia Torres, Antoni Castells, Francesc Balaguer, Jaume Grau, Lisandro Pereyra, María Pellisé

“…Methods A mobile app including ergonomic algorithms integrating all pertinent guideline information was created by a group of experts. …”
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Identification of stemness subtypes and prognostic modeling in thyroid cancer: the critical role of DPYSL3 in tumor progression and immune microenvironment by Jialong Yu, Wei Luo, Guangwei Xu, Mei Tao, Yuqi Wang, Qiman Dong, Linfei Hu, Xiukun Hou, Jingzhu Zhao, Dapeng Li, Songfeng Wei, Xianhui Ruan, Xiangqian Zheng

“…Through the application of four machine learning algorithms and LASSO regression, we identified key genes and constructed a prognostic model based on the stemness signature. …”
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Design and Prototype Verification of a 3-meter Aperture Wrap-rib Reflector by ZHANG Han, YAN Zhongxi, XIANG Ping, WU Minger

“…Currently, the application of this type of deployable reflector mainly faces two challenges: ensuring the reliability of stowage and deployment strategies for large-deformation wrapping, and achieving and maintaining surface accuracy of the flexible wrap-rib reflector. …”
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Integrative multi-omics analysis reveals the interaction mechanisms between gut microbiota metabolites and ferroptosis in rheumatoid arthritis by Lifang Liang, Lifang Liang, Lifang Liang, Huaguo Liang, Min He, Min He, Min He, Huiling Zhang, Huiling Zhang, Huiling Zhang, Peifeng Ke, Peifeng Ke, Peifeng Ke

“…To systematically investigate the regulatory relationship between key ferroptosis genes and gut metabolites in RA, this study employed an integrative multi-omics approach combined with machine learning algorithms and single-cell transcriptomic data, identifying and validating GPX3 and MYC as potential critical ferroptosis regulators in RA.Methods and resultsFirst, 16 candidate genes were obtained by intersecting WGCNA, differential expression analysis results, and targets related to ferroptosis and gut microbiota. …”
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A comprehensive investigation of the relationship between dietary fatty acid intake and preserved ratio impaired spirometry: multimethodology based on NHANES by Chenyuan Deng, Yu Jiang, Yuechun Lin, Hengrui Liang, Wei Wang, Jianxing He, Ying Huang

“…To facilitate the prediction of PRISm, six distinct machine learning algorithms were constructed, followed by the application of SHAP analysis to elucidate the contribution of individual predictors. …”
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Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics by Yushu Xia, Jonathan Sanderman, Jennifer D. Watts, Megan B. Machmuller, Andrew L. Mullen, Charlotte Rivard, Arthur Endsley, Haydee Hernandez, John Kimball, Stephanie A. Ewing, Marcy Litvak, Tomer Duman, Praveena Krishnan, Tilden Meyers, Nathaniel A. Brunsell, Binayak Mohanty, Heping Liu, Zhongming Gao, Jiquan Chen, Michael Abraha, Russell L. Scott, Gerald N. Flerchinger, Patrick E. Clark, Paul C. Stoy, Anam M. Khan, E. N. Jack Brookshire, Quan Zhang, David R. Cook, Thomas Thienelt, Bhaskar Mitra, Marguerite Mauritz‐Tozer, Craig E. Tweedie, Margaret S. Torn, Dave Billesbach

“…Abstract Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. …”
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Metabolic Plasticity and Transcriptomic Reprogramming Orchestrate Hypoxia Adaptation in Yak by Ci Huang, Yilie Liao, Wei Peng, Hai Xiang, Hui Wang, Jieqiong Ma, Zhixin Chai, Zhijuan Wu, Binglin Yue, Xin Cai, Jincheng Zhong, Jikun Wang

“…The protein–protein interaction network identified three consensus hub genes across five topological algorithms (<i>CCNA2</i>, <i>PLK1</i>, and <i>TP53</i>) that may be involved in hypoxia adaptation. …”
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