Image 1_A machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults.jpeg by Daisu Abe (20498225)

“…Among several machine learning algorithms, eXtreme Gradient Boosting (XGBoost) demonstrated the highest predictive accuracy in cross-validation, with an AUROC of 0.81 (±0.07) and an AUPRC of 0.33 (±0.08). …”

Table 2_A machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults.docx by Daisu Abe (20498225)

“…Among several machine learning algorithms, eXtreme Gradient Boosting (XGBoost) demonstrated the highest predictive accuracy in cross-validation, with an AUROC of 0.81 (±0.07) and an AUPRC of 0.33 (±0.08). …”

Table 3_A machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults.docx by Daisu Abe (20498225)

“…Among several machine learning algorithms, eXtreme Gradient Boosting (XGBoost) demonstrated the highest predictive accuracy in cross-validation, with an AUROC of 0.81 (±0.07) and an AUPRC of 0.33 (±0.08). …”

Table 1_A machine learning model to predict neurological deterioration after mild traumatic brain injury in older adults.docx by Daisu Abe (20498225)

“…Among several machine learning algorithms, eXtreme Gradient Boosting (XGBoost) demonstrated the highest predictive accuracy in cross-validation, with an AUROC of 0.81 (±0.07) and an AUPRC of 0.33 (±0.08). …”

Emergent oscillations in population activity. by Kwangjun Lee (22226994)

Data Sheet 1_Using machine learning methods to investigate the impact of comorbidities and clinical indicators on the mortality rate of COVID-19.docx by Yueh-Chen Hsieh (22287187)

“…After implementing federated learning, the AUC of the Taipei cohort decreased to 0.90, while the performance of other cohorts improved to meet the required standards. …”

AKIBoards: A Structure-Following Multiagent System for Predicting Acute Kidney Injury by David Gordon (264270)

“…Such consensus-driven reasoning reflects individual knowledge contributing to- ward a broader perspective on the patient. In this light, we introduce <i>STRUCture- following for Multiagent Systems (STRUC-MAS), </i>a framework automating the learning of these global models and their incorporation as prior beliefs for agents in multiagent systems (MAS) to follow. …”

Presentation 1_Identification and validation of lactate-related gene signatures in endometriosis for clinical evaluation and immune characterization by WGCNA and machine learning.p... by Jixin Li (5801207)

“…Additionally, we conducted immune-related analysis in endometriosis and identified small molecule compounds targeting core LR-DEGs.…”

Data Sheet 1_Proteome analysis of the prefrontal cortex and the application of machine learning models for the identification of potential biomarkers related to suicide.pdf by Manuel Alejandro Rojo-Romero (20756459)

“…Using Western blotting, we validated the decrease in expression of peroxiredoxin 2 and alpha-internexin in the suicide cases. …”

Data Sheet 1_Prediction model of in-hospital mortality risk in intensive care unit patients with cardiac arrest: a multicenter retrospective cohort study based on an ensemble model... by Li Liu (75607)

“…This study developed an ensemble learning (EL) model based on clinical information to predict IHCA patient death risk.…”

Supplementary file 1_Retinal features as predictive indicators for high myopia: insights from explainable multi-machine learning models.docx by Haohan Zou (15255107)

“…Objectives<p>To investigate the role of retinal characteristics for high myopia (HM) prediction based on multi-machine learning (ML) and to provide an interpretable framework for the results.…”

Supplementary file 2_Retinal features as predictive indicators for high myopia: insights from explainable multi-machine learning models.docx by Haohan Zou (15255107)

“…Objectives<p>To investigate the role of retinal characteristics for high myopia (HM) prediction based on multi-machine learning (ML) and to provide an interpretable framework for the results.…”

Optogenetic simulation to silence interneurons. by Kwangjun Lee (22226994)

Supplementary file 1_Explainable machine learning model predicts response to adjuvant therapy after radical cystectomy in bladder cancer.docx by Jian Hou (93442)

“…Data included tumor morphology (e.g., vascular and perineural invasion), demographic variables (e.g., age, sex), and molecular markers (e.g., PD-L1, HER2, GATA3). …”

Data Sheet 1_Fast perceptual learning induces location-specific facilitation and suppression at early stages of visual cortical processing.docx by Yajie Wang (184298)

“…These findings provide the first evidence that fast PL induces both location-specific facilitation and location-specific suppression at early stages of visual cortical processing. We speculate that while the facilitation effect indicates more efficient allocation of voluntary attention to the trained location induced by fast PL, the suppression effect may reflect learning-associated involuntary suppression of visual processing at the untrained location. …”

DataSheet1_Predicting the solubility of CO2 and N2 in ionic liquids based on COSMO-RS and machine learning.docx by Hongling Qin (5557505)

“…To further improve the performance of COSMO-RS, two options were used, i.e., the polynomial expression to correct the COSMO-RS results and the combination of COSMO-RS and machine learning algorithms (eXtreme Gradient Boosting, XGBoost) to develop a hybrid model. …”

Data Sheet 1_A machine learning model for predicting the risk of diabetic nephropathy in individuals with type 2 diabetes mellitus.docx by Tingting Li (10553)

“…In this study, leveraging extensive clinical datasets, we sought to develop and validate a predictive model employing machine learning techniques to assess the risk of DKD in patients with type 2 diabetes mellitus (T2DM).…”

Image 1_Computer-vision based automatic rider helmet violation detection and vehicle identification in Indian smart city scenarios using NVIDIA TAO toolkit and YOLOv8.jpg by Uttam U. Deshpande (21758837)

“…We propose a two-step deep learning approach that leverages the strengths of pre-trained object detection models to detect two-wheeler riders and specialized helmet classifiers to identify helmet wear status as well as detect number plates. …”

Image 6_Computer-vision based automatic rider helmet violation detection and vehicle identification in Indian smart city scenarios using NVIDIA TAO toolkit and YOLOv8.jpg by Uttam U. Deshpande (21758837)

“…We propose a two-step deep learning approach that leverages the strengths of pre-trained object detection models to detect two-wheeler riders and specialized helmet classifiers to identify helmet wear status as well as detect number plates. …”

Image 7_Computer-vision based automatic rider helmet violation detection and vehicle identification in Indian smart city scenarios using NVIDIA TAO toolkit and YOLOv8.jpg by Uttam U. Deshpande (21758837)

“…We propose a two-step deep learning approach that leverages the strengths of pre-trained object detection models to detect two-wheeler riders and specialized helmet classifiers to identify helmet wear status as well as detect number plates. …”