Summary of Acute Kidney Injury Prediction For Non-critical Care Patients: a Retrospective External and Internal Validation Study, by Esra Adiyeke et al.
Acute kidney injury prediction for non-critical care patients: a retrospective external and internal validation study
by Esra Adiyeke, Yuanfang Ren, Benjamin Shickel, Matthew M. Ruppert, Ziyuan Guan, Sandra L. Kane-Gill, Raghavan Murugan, Nabihah Amatullah, Britney A. Stottlemyer, Tiffany L. Tran, Dan Ricketts, Christopher M Horvat, Parisa Rashidi, Azra Bihorac, Tezcan Ozrazgat-Baslanti
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study develops and compares deep learning and conventional machine learning models to predict the progression of acute kidney injury (AKI) in hospitalized patients. The researchers trained local models for each site and a combined model using data from both sites, then validated these models on their respective test sets. The results show that while locally developed models performed similarly across sites, they still displayed some variation in predictive accuracy. The top features influencing the predictions were kidney function measures, nephrotoxic drug burden, and blood urea nitrogen. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps doctors predict when someone with acute kidney injury might get worse. They used special computer programs to look at patient data from two hospitals. The results showed that these programs did a good job of predicting what would happen next, but they worked slightly better when using information from just one hospital. The most important things the program looked at were how well the kidneys were working, if the patient was taking certain medicines, and if their blood urea nitrogen levels were high. |
Keywords
* Artificial intelligence * Deep learning * Machine learning
