ArticleJournal of healthcare engineering2018
A Framework of Rebalancing Imbalanced Healthcare Data for Rare Events' Classification: A Case of Look-Alike Sound-Alike Mix-Up Incident Detection.
Article in Journal of healthcare engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Augmenting Electronic Health Records for Adverse Event Detection.medRxiv : the preprint server for health sciences · 2026Article
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- Predicting hospital admissions, ICU utilization, and prolonged length of stay among febrile pediatric emergency department patients using incomplete and imbalanced electronic health record (EHR) data strategies.International journal of medical informatics · 2025Article
- A holistic framework for intradialytic hypotension prediction using generative adversarial networks-based data balancing.BMC medical informatics and decision making · 2025Article
- Comment on "Use of an elastic-scattering spectroscopy and artificial intelligence device in the assessment of lesions suggestive of skin cancer: A comparative effectiveness study".JAAD international · 2024Article
- Computational Hemodynamics-Based Growth Prediction for Small Abdominal Aortic Aneurysms: Laminar Simulations Versus Large Eddy Simulations.Annals of biomedical engineering · 2024Article
- Evaluating the Role of Data Enrichment Approaches towards Rare Event Analysis in Manufacturing.Sensors (Basel, Switzerland) · 2024Article
- Retrieval-Based Diagnostic Decision Support: Mixed Methods Study.JMIR medical informatics · 2024Article
- Supporting the decision to perform molecular profiling for cancer patients based on routinely collected data through the use of machine learning.Clinical and experimental medicine · 2024Article
- Utilizing Large Language Models to Generate Synthetic Data to Increase the Performance of BERT-Based Neural Networks.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- A self-inspected adaptive SMOTE algorithm (SASMOTE) for highly imbalanced data classification in healthcare.BioData mining · 2023Article
- Improving Non-Invasive Aspiration Detection With Auxiliary Classifier Wasserstein Generative Adversarial Networks.IEEE journal of biomedical and health informatics · 2022Article
- Toward biophysical markers of depression vulnerability.Frontiers in psychiatry · 2022Article
- Mortality-Risk Prediction Model from Road-Traffic Injury in Drunk Drivers: Machine Learning Approach.International journal of environmental research and public health · 2021Article
- Artificial Intelligence for Unstructured Healthcare Data: Application to Coding of Patient Reporting of Adverse Drug Reactions.Clinical pharmacology and therapeutics · 2021Article
- Evaluating resampling methods and structured features to improve fall incident report identification by the severity level.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Prediction of Drug-Induced Long QT Syndrome Using Machine Learning Applied to Harmonized Electronic Health Record Data.Journal of cardiovascular pharmacology and therapeutics · 2021Article
- Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets.PeerJ. Computer science · 2021Article
- Combining Resampling Strategies and Ensemble Machine Learning Methods to Enhance Prediction of Neonates with a Low Apgar Score After Induction of Labor in Northern Tanzania.Risk management and healthcare policy · 2021Article
- External validation of the PAR-Risk Score to assess potentially avoidable hospital readmission risk in internal medicine patients.PloS one · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying rare but significant healthcare events in massive unstructured datasets has become a common task in healthcare data analytics. However, imbalanced class distribution in many practical datasets greatly hampers the detection of rare events, as most classification methods implicitly assume an equal occurrence of classes and are designed to maximize the overall classification accuracy. In this study, we develop a framework for learning healthcare data with imbalanced distribution via incorporating different rebalancing strategies. The evaluation results showed that the developed framework can significantly improve the detection accuracy of medical incidents due to look-alike sound-alike (LASA) mix-ups. Specifically, logistic regression combined with the synthetic minority oversampling technique (SMOTE) produces the best detection results, with a significant 45.3% increase in recall (recall = 75.7%) compared with pure logistic regression (recall = 52.1%).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.