ArticlePloS one2018
Predicting acute kidney injury in cancer patients using heterogeneous and irregular data.
Article in PloS one, 2018. 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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Who cites it
11 citing papers in PubMed, 42 citations in OpenAlex.
- Prediction of Acute Kidney Injury in Oncology: From Clinical Risk Scores to AI-Enabled Precision Onco-Nephrology.Cancers · 2026Review
- Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis.Critical care (London, England) · 2024Article
- Interpretable machine learning-based individual analysis of acute kidney injury in immune checkpoint inhibitor therapy.PloS one · 2024Article
- Machine learning for acute kidney injury: Changing the traditional disease prediction mode.Frontiers in medicine · 2023Review
- Machine learning models for predicting acute kidney injury: a systematic review and critical appraisal.Clinical kidney journal · 2022Article
- Multimodal machine learning in precision health: A scoping review.NPJ digital medicine · 2022Article
- Artificial Intelligence in Acute Kidney Injury Prediction.Advances in chronic kidney disease · 2022Review
- Artificial intelligence-enabled decision support in nephrology.Nature reviews. Nephrology · 2022Review
- A Deep Neural Network for Early Detection and Prediction of Chronic Kidney Disease.Diagnostics (Basel, Switzerland) · 2022Article
- Clinical Significance of Acute Kidney Injury in Lung Cancer Patients.Cancer research and treatment · 2021Article
- Using machine learning models to predict the initiation of renal replacement therapy among chronic kidney disease patients.PloS one · 2020Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
How can we predict the occurrence of acute kidney injury (AKI) in cancer patients based on machine learning with serum creatinine data? Given irregular and heterogeneous clinical data, how can we make the most of it for accurate AKI prediction? AKI is a common and significant complication in cancer patients, and correlates with substantial morbidity and mortality. Since no effective treatment for AKI still exists, it is important to take timely preventive measures. While several approaches have been proposed for predicting AKI, their scope and applicability are limited as they either assume regular data measured over a short hospital stay, or do not fully utilize heterogeneous data. In this paper, we provide an AKI prediction model with a greater applicability, which relaxes the constraints of existing approaches, and fully utilizes irregular and heterogeneous data for learning the model. In a cohort of 21,022 cancer patients who were registered into Korea Central Cancer Registry (KCCR) in Seoul National University Hospital between January 1, 2004 and December 31, 2013, our method achieves 0.7892 precision, 0.7506 recall, and 0.7576 F-measure in predicting whether a patient will develop AKI during the next 14 days.
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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.