Image_1_The prognostic role of an optimal machine learning model based on clinical available indicators in HCC patients.TIF by Xiaoying Lou (9700261)

“…In general, the optimal OCC model based on RSF algorithm shows prognostic guidance value in HCC patients undergoing individualized treatment.…”

Image_2_The prognostic role of an optimal machine learning model based on clinical available indicators in HCC patients.TIF by Xiaoying Lou (9700261)

“…In general, the optimal OCC model based on RSF algorithm shows prognostic guidance value in HCC patients undergoing individualized treatment.…”

Data_Sheet_1_Validation of Risk Scores for Predicting Atrial Fibrillation Detected After Stroke Based on an Electronic Medical Record Algorithm: A Registry-Claims-Electronic Medica... by Cheng-Yang Hsieh (528536)

“…Based on the optimal EMR algorithm, the incidence rate of AFDAS was estimated. …”

Calibration of logistic regression. by Ryan P. Strum (12411239)

“…<div><p>Introduction</p><p>The closest emergency department (ED) may not always be the optimal hospital for certain stable high acuity patients if further distanced ED’s can provide specialized care or are less overcrowded. …”

Calibration of lasso logistic regression. by Ryan P. Strum (12411239)

“…<div><p>Introduction</p><p>The closest emergency department (ED) may not always be the optimal hospital for certain stable high acuity patients if further distanced ED’s can provide specialized care or are less overcrowded. …”

Calibration of random forest. by Ryan P. Strum (12411239)

“…<div><p>Introduction</p><p>The closest emergency department (ED) may not always be the optimal hospital for certain stable high acuity patients if further distanced ED’s can provide specialized care or are less overcrowded. …”

Calibration of gradient boosted trees. by Ryan P. Strum (12411239)

“…<div><p>Introduction</p><p>The closest emergency department (ED) may not always be the optimal hospital for certain stable high acuity patients if further distanced ED’s can provide specialized care or are less overcrowded. …”

Table_1_Viability Study of Machine Learning-Based Prediction of COVID-19 Pandemic Impact in Obsessive-Compulsive Disorder Patients.DOCX by María Tubío-Fungueiriño (12069125)

“…Machine learning may be valuable tool for helping clinicians to rapidly identify patients at higher risk and therefore provide optimized care, especially in future pandemics. However, further validation of these models is required to ensure greater reliability of the algorithms for clinical implementation to specific objectives of interest.…”

Image 6_Differentiating pancreatic ductal adenocarcinoma and autoimmune pancreatitis using a machine learning model based on ultrasound clinical features.jpeg by Lihua Zhang (22614)

Image 3_Differentiating pancreatic ductal adenocarcinoma and autoimmune pancreatitis using a machine learning model based on ultrasound clinical features.jpeg by Lihua Zhang (22614)

Image 7_Differentiating pancreatic ductal adenocarcinoma and autoimmune pancreatitis using a machine learning model based on ultrasound clinical features.jpeg by Lihua Zhang (22614)

Image 8_Differentiating pancreatic ductal adenocarcinoma and autoimmune pancreatitis using a machine learning model based on ultrasound clinical features.jpeg by Lihua Zhang (22614)

Image 1_Differentiating pancreatic ductal adenocarcinoma and autoimmune pancreatitis using a machine learning model based on ultrasound clinical features.jpeg by Lihua Zhang (22614)

Image 2_Differentiating pancreatic ductal adenocarcinoma and autoimmune pancreatitis using a machine learning model based on ultrasound clinical features.jpeg by Lihua Zhang (22614)

Image 5_Differentiating pancreatic ductal adenocarcinoma and autoimmune pancreatitis using a machine learning model based on ultrasound clinical features.jpeg by Lihua Zhang (22614)

Image 4_Differentiating pancreatic ductal adenocarcinoma and autoimmune pancreatitis using a machine learning model based on ultrasound clinical features.jpeg by Lihua Zhang (22614)

Data Sheet 1_Machine learning-based ultrasomics for predicting response to tyrosine kinase inhibitor in combination with anti-PD-1 antibody immunotherapy in hepatocellular carcinom... by Yiwen Hu (615619)

“…Furthermore, ultrasomic features were associated with PFS, which was significantly different between patients with radiomic scores (Rad-score) greater than 0.057 and those with Rad-score less than 0.057 in both the training (HR = 0.488, 95% CI: 0.299-0.796, p = 0.003) and validation cohorts (HR = 0.451, 95% CI: 0.229-0.887, p = 0.02).…”

Image_13_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG by Niklas Wulms (11928755)

“…In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.…”

Image_2_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG by Niklas Wulms (11928755)

“…In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.…”

Image_1_The Effect of Training Sample Size on the Prediction of White Matter Hyperintensity Volume in a Healthy Population Using BIANCA.JPEG by Niklas Wulms (11928755)

“…In this study, we tested whether WMH volumetry with FMRIB software library v6.0 (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) Brain Intensity AbNormality Classification Algorithm (BIANCA), a customizable and trainable algorithm that quantifies WMH volume based on individual data training sets, can be optimized for a normal aging population.…”