Summary of Advanced Predictive Modeling For Enhanced Mortality Prediction in Icu Stroke Patients Using Clinical Data, by Armin Abdollahi and Negin Ashrafi and Maryam Pishgar
Advanced Predictive Modeling for Enhanced Mortality Prediction in ICU Stroke Patients Using Clinical Data
by Armin Abdollahi, Negin Ashrafi, Maryam Pishgar
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper proposes a deep learning-based approach to predict the mortality risk of ischemic stroke patients in intensive care units (ICUs). The authors acquire data from the MIMIC-IV database, including clinical information, vital signs, laboratory tests, and treatments. They develop a model that combines XGBoost for feature selection and deep learning to minimize false positives and improve accuracy. The results show that the proposed model achieves an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.903 on the first day and 0.945 during training, outperforming other machine learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to predict whether someone who has had a stroke will die or survive while in the hospital. They used a big database of information about people who have had strokes, including things like their blood pressure, temperature, and what medicines they are taking. The new approach is better than previous methods because it looks at fewer but more important details, which helps it be more accurate. |
Keywords
» Artificial intelligence » Deep learning » Feature selection » Machine learning » Temperature » Xgboost
