Prognostic value of serum cystatin C in patients with sepsis by Chunfang Hu, Zhiyuan Zhang, Bin Hou, Han Xue, Xuehua Pu, Jilu Ye

Subjects: “…cystatin c…”
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An ultrasonic-AI hybrid approach for predicting void defects in concrete-filled steel tubes via enhanced XGBoost with Bayesian optimization by Shuai Wan, Shipan Li, Zheng Chen, Yunchao Tang

“…The BO-XGBoost model demonstrated superior performance compared to baseline models (Random Forest, AdaBoost, and Gradient Boosting Decision Tree), achieving an overall prediction accuracy of 0.92, precision and recall of 0.90, and an AUC of 0.98. SHAP (SHapley Additive exPlanations) analysis revealed that sound velocity, sound time, acoustic amplitude, concrete strength, and fly ash content were the most influential features for model predictions. …”
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Enhancing Medical Diagnostics with Machine Learning: A Study on Ensemble Methods and Transfer Learning by Zhang Jiaming

“…According to the study, CNNs performs substantially better when handling uncertainty when using the U-Multiclass technique, as seen by the largest Area Under the Curve (AUC) for Cardiomegaly detection. When it comes to diabetes prediction, Ensemble Methods outperform other approaches, and Transfer Learning works well for modifying trained models for use in novel medical applications. …”
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Analisis Kinerja Algoritma CART dan Naive Bayes Berbasis Particle Swarm Optimization (PSO) untuk Klasifikasi Kelayakan Kredit Koperasi by Eko Arif Riyanto, Tri Juninisvianty, Doddy Ferdian Nasution, Risnandar Risnandar

“…Nilai accuracy yang diperoleh dari penelitian ini adalah 96,43%, nilai recall 94,12%, niilai precision 100%. Dengan nilai AUC sebesar 0,963 , penelitian ini termasuk dalam klasifikasi baik. …”
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Development of clinical decision support for patients older than 65 years with fall-related TBI using artificial intelligence modeling. by Biche Osong, Eric Sribnick, Jonathan Groner, Rachel Stanley, Lauren Schulz, Bo Lu, Lawrence Cook, Henry Xiang

“…The predictive performance of the tree in terms of AUC value (95% confidence intervals) in the training cohort for death, care, and home were 0.66 (0.65-0.67), 0.75 (0.73-0.76), and 0.77 (0.76-0.79), respectively. …”
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Association of baseline and trajectory of triglyceride-glucose index with the incidence of cardiovascular autonomic neuropathy in type 2 diabetes mellitus by Qiong Huang, Wenbin Nan, Baimei He, Zhenhua Xing, Zhenyu Peng

“…The area under the curve (AUC) of receiver operating characteristic (ROC) curve was used to assess the diagnostic value of the TyG index in predicting the risk of CAN. …”
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Construction and validation of a prognostic model for NK/T-cell lymphoma based on random survival forest algorithm by HUANG Zhen, HUANG Zhen, WU Yazhou

“…In the validation cohort, the area under the ROC curve (AUC) for the nomogram model at 1, 3, and 5 years was 0.745, 0.771, and 0.748, respectively, while the AUC for the RSF model was 0.764, 0.792, and 0.761 at the same time points. …”
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Sistem Informasi Geografi untuk Analisis Potensi Sumber Daya Lahan Pesisir Kepulauan Padaido Kabupaten Biak Numfor, Papua by Rosalina Giovani Mandowen, Rinto H Mambrasar

“…Teknik pengolahan dan analisis data ini menggunakan software SIG yakni ArcGIS 10.1 dengan model skoring dan overlay. Hasil penelitian dengan studi kasus Kepulauan Padaido ini dapat disimpulkan bahwa saat ini dengan adanya SIG yang dibangun, Pemerintah Daerah Biak Numfor sudah dapat mengolah lahan pesisir untuk dimanfaatkan sesuai dengan potensi lahan yang seharusnya, seperti potensi lahan untuk usaha budidaya rumput laut seluas 13.269,41 ha atau 94%, untuk budidaya teripang seluas 7.069,91 ha atau 83%, sebagai aktifitas pariwisata pesisir seluas 7.778,45 ha atau 86%, sebagai kegiatan konservasi seluas 2.957,54 ha atau 163%, untuk daerah tangkapan ikan karang seluas 2.078,92 ha atau 80%, dan sebagai daerah tangkapan ikan pelagis 1.585,61 ha atau 87%.   …”
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ECG‐based epileptic seizure prediction: Challenges of current data‐driven models by Sotirios Kalousios, Jens Müller, Hongliu Yang, Matthias Eberlein, Ortrud Uckermann, Gabriele Schackert, Witold H. Polanski, Georg Leonhardt

“…Results The mean receiver operating characteristic (ROC) area under the curve (AUC) for the non‐causal experiment was 0.823 (±0.12), with 208 (82.5%) seizures achieving an improvement over chance (IoC) classification score (p < 0.05, Hanley & McNeil test). …”
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Observation of Pattern Stabilization in a Driven Superfluid by Nikolas Liebster, Marius Sparn, Elinor Kath, Jelte Duchene, Keisuke Fujii, Sarah L. Görlitz, Tilman Enss, Helmut Strobel, Markus K. Oberthaler

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HEXIS CORPORAL Y ESCRITURA by CÁNDIDA ELIZABETH VIVERO MARÍN

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On obtaining the coupled-mode theory using a model of coupled plane waves for symmetric resonant diffraction gratings by D.A. Bykov, E.A. Bezus, L.L. Doskolovich

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Dehumanization and burden of care among caregivers of terminally ill patients by Ines Testoni, Giulia Sicari, Lucia Ronconi, Gianmarco Biancalani, Chiara Franco, Paolo Cottone, Robert Crupi

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Sentiment Analysis of Mobile Phone Reviews Using XGBoost and Word Vectors by Wang Zekai

“…The empirical analysis shows that the accuracy, recall, area under the curve (AUC), and other validation indexes of the constructed sentiment recognition model are further improved compared with the LLM model, which has a certain application value. …”
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Early detection of Bronchopulmonary Dysplasia (BPD) in preterm infants using doppler ultrasound technology by Pin Wang, Lihong Duan, Congxin Sun, Yu Chen, Yanyan Peng, Guihong Chen, Lixia Wu, Yan Li

“…With an area under the curve (AUC) of 0.76, PAAT in particular demonstrated a reasonable capacity for prediction. …”
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Accurate identification of oxygen desaturation status in COPD by using classifier ensemble. by Yue-Fang Wu, Xin Shu, Shiqi Wang, Xiaojun Xu, Pei-Li Sun

“…The comparative computational results on the 6-min walk test (6MWT) of the recruited participants show that the proposed method achieved the best global performance with AUC (Area Under Curve) value of 0.8532, indicating that the proposed method can be effectively used for the identification of EIOD and could assist the clinic diagnosis of COPD.…”
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Diagnostic classification in toxicologic pathology using attention-guided weak supervision and whole slide image features: a pilot study in rat livers by Philip Zehnder, Jeffrey Feng, Trung Nguyen, Philip Shen, Ruth Sullivan, Reina N. Fuji, Fangyao Hu

“…Our model demonstrates improvements in diagnostic classification and attention heatmap quality over the previously described clustering-constrained-attention multiple-instance learning method on several lesion classes in rat livers (38% improvement in AUC). We also demonstrate how an ensemble of binary classifiers improves interpretability and allows for multiclass classification and the classification of diagnostic regions of interest in each slide. …”
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PRRSV-2 nsp2 Ignites NLRP3 inflammasome through IKKβ-dependent dispersed trans-Golgi network translocation. by Lujie Zhang, Yanni Gao, Haoran Zhou, Xiao Liang, Xiaolin Jiang, Wenqin Gong, Yangyang Sun, Desheng Zhang, Xianwei Wang, Hans Nauwynck, Juan Bai, Ping Jiang

“…This process facilitates ASC polymerization, culminating in the activation of the NLRP3 inflammasome. …”
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Structural conditions for relay ramp fault development on the edge of the collision wedge: a case study from the East Slovak Basin by Jacko, Stanislav, Ďuriška, Igor, Janočko, Juraj, Farkašovský, Roman, Thiessen, Alexander Dean

“…The Neogene evolution of the Pannonian extensional back-arc basin was associated with the development of transform faults. …”
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Advancing risk stratification in kidney transplantation: integrating HLA-derived T-cell epitope and B-cell epitope matching algorithms for enhanced predictive accuracy of HLA compa... by Matthias Niemann, Matthias Niemann, Benedict M. Matern, Benedict M. Matern, Gaurav Gupta, Bekir Tanriover, Fabian Halleck, Klemens Budde, Eric Spierings, Eric Spierings

“…We have statistically evaluated their co-dependency and synergistic effects between models systematically on 400,935 kidney transplantations using Cox proportional hazards and XGBoost models.ResultsMultivariable models of histocompatibility generally outperformed univariable predictors, with a combined model of HLA-A, -B, -DR matching, Snow and PIRCHE-II yielding highest AUC in XGBoost and lowest BIC in Cox models. Augmentation of a clinical prediction model of pre-transplant parameters by molecular compatibility metrics improved model performance particularly considering long-term outcomes.DiscussionOur study demonstrates that the use of multiple specialized molecular HLA matching predictors improves prediction performance, thereby improving risk classification and supporting informed decision-making in kidney transplantation.…”
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