assessing-accuracy-fitness-smartwatch-data-for-cardiovascular

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

This study aims to validate the accuracy of low-cost fitness smartwatches by comparing their data with gold-standard measurements for cardiovascular and ...

Universiti Malaya

This project employs artificial intelligence, including machine learning and deep learning, to assess COVID-19 readmission risk in Malaysia. It offers tools to mitigate healthcare resource strain and enhance patient outcomes. This study outlines a methodology for classifying COVID-19 readmissions. It starts with dataset description and pre-processing, while the data balancing was computed through Random Oversampling, Borderline SMOTE, and Adaptive Synthetic Sampling. Nine machine learning and ten deep learning techniques are applied, with five-fold cross-validation for evaluation. Optuna is used for hyperparameter selection, while the consistency in training hyperparameters is maintained. Evaluation metrics encompass accuracy, AUC, and training/inference times. Results were based on stratified five-fold cross-validation and different data-balancing methods. Notably, CatBoost consistently excelled in accuracy and AUC across all tables. Using ROS, CatBoost achieved the highest accuracy (0.9882 ± 0.0020) with an AUC of 1.0000 ± 0.0000. CatBoost maintained its superiority in BSMOTE and ADASYN as well. Deep learning approaches performed well, with SAINT leading in ROS and TabNet leading in BSMOTE and ADASYN. Decision Tree ensembles like Random Forest and XGBoost consistently showed strong performance.

Predictive Modeling of COVID-19 Readmissions: Insights from Machine Learning and Deep Learning Approaches.

Abnormal liver biochemistry (ALB) is common among patients with COVID-19 infection due to various factors. It is uncertain if it persists after the acute infection. We aimed to investigate this.

Article	Year
Predictive Modeling of COVID-19 Readmissions: Insights from Machine Learning and Deep Learning Approaches. Diagnostics (Basel, Switzerland)\| PubMed ID: 39061647	2024
Liver dysfunction in adults with COVID-19 infection: A longitudinal study with transient elastography evaluation. JGH open : an open access journal of gastroenterology and hepatology\| PubMed ID: 39114430	2024

Hang Cheng Ong

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