Schematic of observation window and prediction tasks employed in the study. by Nowell M. Fine (13874645)

Table 1_Establishment of reliable identification algorithms for acute heart failure or acute exacerbation of chronic heart failure using clinical data from a medical information da... by Ryusuke Inoue (4617505)

“…Introduction<p>This study aimed to evaluate the validity of algorithms based on electronic health data in identifying cases of acute heart failure and acute exacerbation of chronic heart failure at multiple institutions using the Medical Information Database Network (MID-NET®) in Japan.…”

Code repository by Dimitrios Iason Papadopoulos (21556811)

“…This includes an extended investigation into damage classification under multiple, interacting failure modes, enhancing the diagnostic resolution of the system. …”

Data Sheet 1_Establishment of reliable identification algorithms for acute heart failure or acute exacerbation of chronic heart failure using clinical data from a medical informati... by Ryusuke Inoue (4617505)

“…Introduction<p>This study aimed to evaluate the validity of algorithms based on electronic health data in identifying cases of acute heart failure and acute exacerbation of chronic heart failure at multiple institutions using the Medical Information Database Network (MID-NET®) in Japan.…”

Trainable parameters for different algorithms. by Anwar Hossain Efat (19942283)

Comparison of results from multiple runs. by Naiwei Tu (22177688)

“…The experimental results under multiple strategies show the feasibility and effectiveness of this paper strategy, which ensures the effective recovery of fault nodes after power failure.…”

Clinical characteristic comparison. by Xihao Shen (20347942)

Study workflow diagram. by Alemu Birara Zemariam (17540938)

“…Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of stunting among adolescent girls in Ethiopia.…”

Stunting final dataset. by Alemu Birara Zemariam (17540938)

“…Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of stunting among adolescent girls in Ethiopia.…”

A mean SHAP value report. by Alemu Birara Zemariam (17540938)

“…Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of stunting among adolescent girls in Ethiopia.…”

A waterfall plot analysis. by Alemu Birara Zemariam (17540938)

“…Therefore, this study aimed to employ multiple machine learning algorithms to identify the most effective model for the prediction of stunting among adolescent girls in Ethiopia.…”

Study flow diagram. by Shahrokh Mousavi (21989418)

“…We aimed to develop machine learning-based models using multiple algorithms to predict and identify the predictors of angina pectoris in an elderly community-dwelling population.…”

Analysis of sensitivity and specificity. by Xiaoli Shen (2208601)

“…<div><p>This study aimed to develop models for predicting the 30-day mortality of sepsis-associated delirium (SAD) by multiple machine learning (ML) algorithms. …”

Research flowchart. by Xiaoli Shen (2208601)

“…<div><p>This study aimed to develop models for predicting the 30-day mortality of sepsis-associated delirium (SAD) by multiple machine learning (ML) algorithms. …”

Variable selection by RFE. by Xiaoli Shen (2208601)

“…<div><p>This study aimed to develop models for predicting the 30-day mortality of sepsis-associated delirium (SAD) by multiple machine learning (ML) algorithms. …”

Baseline characteristic variables. by Xiaoli Shen (2208601)

“…<div><p>This study aimed to develop models for predicting the 30-day mortality of sepsis-associated delirium (SAD) by multiple machine learning (ML) algorithms. …”

Calibration curve in the validation cohort. by Xiaoli Shen (2208601)

“…<div><p>This study aimed to develop models for predicting the 30-day mortality of sepsis-associated delirium (SAD) by multiple machine learning (ML) algorithms. …”

The study flowchart. by Nguyen Tat Thanh (10296398)

“…Predictors were selected based on clinical expertise, literature review, Akaike Information Criterion and Least Absolute Shrinkage and Selection Operator. Multiple supervised machine-learning algorithms – logistic regression, random forest (RF), support vector machine (SVM), k-nearest neighbor, naïve Bayes, AdaBoost, and XGBoost - were applied. …”

Missing value chart of candidate variables. by Nguyen Tat Thanh (10296398)

“…Predictors were selected based on clinical expertise, literature review, Akaike Information Criterion and Least Absolute Shrinkage and Selection Operator. Multiple supervised machine-learning algorithms – logistic regression, random forest (RF), support vector machine (SVM), k-nearest neighbor, naïve Bayes, AdaBoost, and XGBoost - were applied. …”

Functional and Structural Characterization of Mechanosensitive Piezo1 Channel in Disease by Sophia Liu (722004)

“…PIEZO1 mutations are implicated in a range of conditions including Hereditary Xerocytosis, Lymphatic Dysplasia, Prune Belly Syndrome, and Bone Marrow Failure. Researchers analyzed 101 variants—75 from published datasets and 26 novel ones—using AlphaFold3 (AF3), AlphaMissense, EVE, ESM-1b, and CADD to predict pathogenicity and structural impact.…”