ArticleNPJ digital medicine2019
Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients.
Article in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 28 citations in OpenAlex.
- Interpretable machine learning models for predicting in-hospital and 30 days adverse events in acute coronary syndrome patients in Kuwait.Scientific reports · 2024Article
- SpeCamX: mobile app that turns unmodified smartphones into multispectral imagers.Biomedical optics express · 2023Article
- Entering the new digital era of intensive care medicine: an overview of interdisciplinary approaches to use artificial intelligence for patients' benefit.European journal of anaesthesiology and intensive care · 2023Article
- Machine learning-based risk factor analysis and prevalence prediction of intestinal parasitic infections using epidemiological survey data.PLoS neglected tropical diseases · 2022Article
- Use of Artificial Intelligence in Non-Oncologic Interventional Radiology: Current State and Future Directions.Digestive disease interventions · 2021Article
- Deep learning with robustness to missing data: A novel approach to the detection of COVID-19.PloS one · 2021Article
- Medical Information Mart for Intensive Care: A Foundation for the Fusion of Artificial Intelligence and Real-World Data.Frontiers in artificial intelligence · 2021Article
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Authors and funding
9 authors at 8 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Patients admitted to the intensive care unit frequently have anemia and impaired renal function, but often lack historical blood results to contextualize the acuteness of these findings. Using data available within two hours of ICU admission, we developed machine learning models that accurately (AUC 0.86-0.89) classify an individual patient's baseline hemoglobin and creatinine levels. Compared to assuming the baseline to be the same as the admission lab value, machine learning performed significantly better at classifying acute kidney injury regardless of initial creatinine value, and significantly better at predicting baseline hemoglobin value in patients with admission hemoglobin of <10 g/dl.
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Registered trials
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