ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
Predictive Modeling of the Risk of Acute Kidney Injury in Critical Care: A Systematic Investigation of The Class Imbalance Problem.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Validation of a cancer population derived AKI machine learning algorithm in a general critical care scenario.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Forecasting acute kidney injury and resource utilization in ICU patients using longitudinal, multimodal models.Journal of biomedical informatics · 2024Article
- Forecasting Acute Kidney Injury and Resource Utilization in ICU patients using longitudinal, multimodal models.medRxiv : the preprint server for health sciences · 2024Article
- An adaptive federated learning framework for clinical risk prediction with electronic health records from multiple hospitals.Patterns (New York, N.Y.) · 2024Article
- Enhancing Clinical Predictive Modeling through Model Complexity-Driven Class Proportion Tuning for Class Imbalanced Data: An Empirical Study on Opioid Overdose Prediction.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup.Nature reviews. Nephrology · 2023Review
- Knowledge Graph Embeddings for ICU readmission prediction.BMC medical informatics and decision making · 2023Article
- Improving suicide risk prediction via targeted data fusion: proof of concept using medical claims data.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Integration of NLP2FHIR Representation with Deep Learning Models for EHR Phenotyping: A Pilot Study on Obesity Datasets.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational ScienceArticle
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Acute Kidney Injury (AKI) in critical care is often a quickly-evolving clinical event with high morbidity and mortality. Early prediction of AKI risk in critical care setting can facilitate early interventions that are likely to provide ben- efit. Recently there have been some research on AKI prediction with patient Electronic Health Records (EHR). The class imbalance problem is encountered in such prediction setting where the number of AKI cases is usually much smaller than the controls. This study systematically investigates the impact of class imbalance on the performance of AKI prediction. We systematically investigate several class-balancing strategies to address class imbalance, includ- ing traditional statistical approaches and the proposed methods (case-control matching approach and individualized prediction approach). Our results show that the proposed class-balancing strategies can effectively improve the AKI prediction performance. Additionally, some important predictors (e.g., creatinine, chloride, and urine) for AKI can be found based on the proposed methods.
Identifiers
31259038PMC6568062What OpenQuestion holds
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.