A comparative performance analysis of data resampling methods on imbalance medical data
Medical datasets are usually imbalanced, where negative cases severely outnumber
positive cases. Therefore, it is essential to deal with this data skew problem when training …
positive cases. Therefore, it is essential to deal with this data skew problem when training …
Development and internal validation of supervised machine learning algorithms for predicting clinically significant functional improvement in a mixed population of …
KN Kunze, EM Polce, BU Nwachukwu, J Chahla… - … : The Journal of …, 2021 - Elsevier
Purpose To (1) develop and validate a machine learning algorithm to predict clinically
significant functional improvements after hip arthroscopy for femoroacetabular impingement …
significant functional improvements after hip arthroscopy for femoroacetabular impingement …
Hybridization of ring theory-based evolutionary algorithm and particle swarm optimization to solve class imbalance problem
Many real-life datasets are imbalanced in nature, which implies that the number of samples
present in one class (minority class) is exceptionally less compared to the number of …
present in one class (minority class) is exceptionally less compared to the number of …
[HTML][HTML] Adversarial neural network with sentiment-aware attention for detecting adverse drug reactions
Adverse drug reaction (ADR) detection is an important issue in drug safety. ADRs are health
threats caused by medication. Identifying ADRs in a timely manner can reduce harm to …
threats caused by medication. Identifying ADRs in a timely manner can reduce harm to …
Predicting the need for vasopressors in the intensive care unit using an attention based deep learning model
Background: Previous models on prediction of shock mostly focused on septic shock and
often required laboratory results in their models. The purpose of this study was to use deep …
often required laboratory results in their models. The purpose of this study was to use deep …
Bootstrapping your own positive sample: contrastive learning with electronic health record data
Electronic Health Record (EHR) data has been of tremendous utility in Artificial Intelligence
(AI) for healthcare such as predicting future clinical events. These tasks, however, often …
(AI) for healthcare such as predicting future clinical events. These tasks, however, often …
Performance Improvement of Classification Model with Imbalanced Dataset Classification models based on machine learning for the application of real life carry out …
V Khattri - Turkish Journal of Computer and Mathematics …, 2021 - turcomat.org
Classification models based on machine learning for the application of real life carry out
classification tasks using real life dataset. Classification models have class imbalance …
classification tasks using real life dataset. Classification models have class imbalance …
An Integrated Resampling Methods for Imbalanced Sporadic Temporal Data in EHRs
Q Ye, T Kuroda, T Ruan, W Zhang… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
Most real-world applications in EHRs involve temporal data with skewed distributions. The
imbalanced classification problem becomes more difficult in sporadic temporal data that …
imbalanced classification problem becomes more difficult in sporadic temporal data that …
[PDF][PDF] Adverse drug reaction extraction on electronic health records written in Spanish: a PhD thesis overview
SS González - isca-archive.org
The aim of this work is the automatic extraction of Adverse Drug Reactions (ADRs) in
Electronic Health Records (EHRs) written in Spanish. From Natural Language Processing …
Electronic Health Records (EHRs) written in Spanish. From Natural Language Processing …