ArticleJMIR medical informatics2023
Dealing With Missing, Imbalanced, and Sparse Features During the Development of a Prediction Model for Sudden Death Using Emergency Medicine Data: Machine Learning Approach.
Article in JMIR medical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Large language models and conditional rules in clinical decision support systems.Health information science and systems · 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 Machine Learning Approach to Differentiate Cold and Hot Syndrome in Viral Pneumonia Integrating Traditional Chinese Medicine and Modern Medicine: Machine Learning Model Development and Validation.JMIR medical informatics · 2025Article
- Machine learning-based prediction of respiratory depression during sedation for liposuction.Scientific reports · 2025Article
- Machine Learning-Based Risk Factor Analysis and Prediction Model Construction for the Occurrence of Chronic Heart Failure: Health Ecologic Study.JMIR medical informatics · 2025Article
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- Application effect study of a combination of TeamSTEPPS with modularization teaching in the context of clinical instruction in trauma care.Scientific reports · 2024Article
- A machine learning-based prediction model for postoperative delirium in cardiac valve surgery using electronic health records.BMC cardiovascular disorders · 2024Article
- A methodological showcase: utilizing minimal clinical parameters for early-stage mortality risk assessment in COVID-19-positive patients.PeerJ. Computer science · 2024Article
- Clinical Validation of Explainable Deep Learning Model for Predicting the Mortality of In-Hospital Cardiac Arrest Using Diagnosis Codes of Electronic Health Records.Reviews in cardiovascular medicine · 2023Article
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6 authors.
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Abstract
backgroundIn emergency departments (EDs), early diagnosis and timely rescue, which are supported by prediction modes using ED data, can increase patients' chances of survival. Unfortunately, ED data usually contain missing, imbalanced, and sparse features, which makes it challenging to build early identification models for diseases.
objectiveThis study aims to propose a systematic approach to deal with the problems of missing, imbalanced, and sparse features for developing sudden-death prediction models using emergency medicine (or ED) data.
methodsWe proposed a 3-step approach to deal with data quality issues: a random forest (RF) for missing values, k-means for imbalanced data, and principal component analysis (PCA) for sparse features. For continuous and discrete variables, the decision coefficient R
resultsA total of 1085 patients with rescue records and 17,959 patients without rescue records were selected and significantly imbalanced. We extracted 275, 402, and 891 variables from laboratory tests, medications, and diagnosis, respectively. After data preprocessing, the median R
conclusionsThe proposed systematic approach is valid for building a prediction model for emergency patients.
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