Synthetic data generation for tabular health records: A systematic review
Synthetic data generation (SDG) research has been ongoing for some time with promising
results in different application domains, including healthcare, biometrics and energy …
results in different application domains, including healthcare, biometrics and energy …
Bias in reinforcement learning: A review in healthcare applications
Reinforcement learning (RL) can assist in medical decision making using patient data
collected in electronic health record (EHR) systems. RL, a type of machine learning, can use …
collected in electronic health record (EHR) systems. RL, a type of machine learning, can use …
Deep neural networks and tabular data: A survey
Heterogeneous tabular data are the most commonly used form of data and are essential for
numerous critical and computationally demanding applications. On homogeneous datasets …
numerous critical and computationally demanding applications. On homogeneous datasets …
The value of standards for health datasets in artificial intelligence-based applications
A Arora, JE Alderman, J Palmer, S Ganapathi… - Nature Medicine, 2023 - nature.com
Artificial intelligence as a medical device is increasingly being applied to healthcare for
diagnosis, risk stratification and resource allocation. However, a growing body of evidence …
diagnosis, risk stratification and resource allocation. However, a growing body of evidence …
Generative table pre-training empowers models for tabular prediction
Recently, the topic of table pre-training has attracted considerable research interest.
However, how to employ table pre-training to boost the performance of tabular prediction …
However, how to employ table pre-training to boost the performance of tabular prediction …
Prediction of lung metastases in thyroid cancer using machine learning based on SEER database
W Liu, S Wang, Z Ye, P Xu, X Xia, M Guo - Cancer Medicine, 2022 - Wiley Online Library
Purpose Lung metastasis (LM) is one of the most frequent distant metastases of thyroid
cancer (TC). This study aimed to develop a machine learning algorithm model to predict …
cancer (TC). This study aimed to develop a machine learning algorithm model to predict …
SMOTE-NC and gradient boosting imputation based random forest classifier for predicting severity level of covid-19 patients with blood samples
An increase in the number of patients and death rates make Covid-19 a serious pandemic
situation. This problem has effects on health security, economical security, social life, and …
situation. This problem has effects on health security, economical security, social life, and …
GAN-based synthetic time-series data generation for improving prediction of demand for electric vehicles
Demand forecasting is essential for any business to grow and manage its different business
activities. With the basic needs of customers, it is hard to predict the future demand using …
activities. With the basic needs of customers, it is hard to predict the future demand using …
[HTML][HTML] Geometric SMOTE for imbalanced datasets with nominal and continuous features
Imbalanced learning can be addressed in 3 different ways: Resampling, algorithmic
modifications and cost-sensitive solutions. Resampling, and specifically oversampling, are …
modifications and cost-sensitive solutions. Resampling, and specifically oversampling, are …
Risk prediction model of clinical mastitis in lactating dairy cows based on machine learning algorithms
W Luo, Q Dong, Y Feng - Preventive Veterinary Medicine, 2023 - Elsevier
Mastitis is the most common disease among dairy cows and is known to have negative
effects on both animal welfare and the profitability of dairy farms. Early detection of clinical …
effects on both animal welfare and the profitability of dairy farms. Early detection of clinical …