ArticleBMC medical informatics and decision making2022
Predicting acute kidney injury following open partial nephrectomy treatment using SAT-pruned explainable machine learning model.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Role of artificial intelligence in predicting the renal function after nephrectomy in renal cell carcinoma: a systematic review and meta-analysis.International urology and nephrology · 2025Pooled it
- Leveraging ICT Tools to Improve Kidney Health: A Comprehensive Review of Innovations in Nephrology.Healthcare (Basel, Switzerland) · 2026Review
- Kidney cancer diagnosis and surgery selection by double decker convolutional neural network from CT scans combined with great wall construction algorithm.Abdominal radiology (New York) · 2025Article
- Implementation and Updating of Clinical Prediction Models: A Systematic Review.Mayo Clinic proceedings. Digital health · 2025Review
- A Machine Learning-Based Guide for Repeated Laboratory Testing in Pediatric Emergency Departments.Diagnostics (Basel, Switzerland) · 2025Article
- Predicting postoperative nausea and vomiting using machine learning: a model development and validation study.BMC anesthesiology · 2025Article
- Intraoperative hypotension trajectories and their predictive value for major postoperative complications: a retrospective cohort study.Frontiers in medicine · 2025Article
- Machine learning approaches toward an understanding of acute kidney injury: current trends and future directions.The Korean journal of internal medicine · 2024Review
- Predicting lung cancer's metastats' locations using bioclinical model.Frontiers in medicine · 2024Article
- Investigation toward the economic feasibility of personalized medicine for healthcare service providers: the case of bladder cancer.Frontiers in medicine · 2024Article
- BrachySound: machine learning based assessment of respiratory sounds in dogs.Scientific reports · 2023Article
- Mathematical modeling of BCG-based bladder cancer treatment using socio-demographics.Scientific reports · 2023Article
- Machine learning for acute kidney injury: Changing the traditional disease prediction mode.Frontiers in medicine · 2023Review
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOne of the most prevalent complications of Partial Nephrectomy (PN) is Acute Kidney Injury (AKI), which could have a negative impact on subsequent renal function and occurs in up to 24.3% of patients undergoing PN. The aim of this study was to predict the occurrence of AKI following PN using preoperative parameters by applying machine learning algorithms.
methodsWe included all adult patients (n = 723) who underwent open PN in our department since 1995 and on whom we have data on the pre-operative renal function. We developed a random forest (RF) model with Boolean satisfaction-based pruned decision trees for binary classification (AKI or non-AKI). Hyper-parameter grid search was performed to optimize the model's performance. Fivefold cross-validation was applied to evaluate the model. We implemented a RF model with greedy feature selection to binary classify AKI and non-AKI cases based on pre-operative data.
resultsThe best model obtained a 0.69 precision and 0.69 recall in classifying the AKI and non-AKI groups on average (k = 5). In addition, the model's probability to correctly classify a new prediction is 0.75. The proposed model is available as an online calculator.
conclusionsOur model predicts the occurrence of AKI following open PN with (75%) accuracy. We plan to externally validate this model and modify it to minimally-invasive PN.
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