Summary of Towards Interpretable End-stage Renal Disease (esrd) Prediction: Utilizing Administrative Claims Data with Explainable Ai Techniques, by Yubo Li et al.
Towards Interpretable End-Stage Renal Disease (ESRD) Prediction: Utilizing Administrative Claims Data with Explainable AI Techniques
by Yubo Li, Saba Al-Sayouri, Rema Padman
First submitted to arxiv on: 18 Sep 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 The paper explores using administrative claims data and machine learning/deep learning techniques to predict Chronic Kidney Disease (CKD) progression to End-Stage Renal Disease (ESRD). It develops prediction models using traditional methods like Random Forest and XGBoost, as well as deep learning approaches like Long Short-Term Memory (LSTM) networks. The findings show that the LSTM model with a 24-month observation window outperforms existing models in predicting ESRD progression. Additionally, SHapley Additive exPlanations (SHAP) analysis is used to enhance interpretability and provide insights into individual feature impacts on predictions at the patient level. This study highlights the value of leveraging administrative claims data for CKD management and predicting ESRD progression. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special computer programs to look at old medical records and try to predict when people with kidney disease will get very sick. They used a big bunch of old records from a health insurance company and tried different ways to make predictions, like looking at what happened in the past 2 years or 6 months. The best way they found was by using something called an LSTM network. This helped them predict when people would get very sick with more accuracy than before. They also used another tool to explain why their predictions were correct and what parts of the old records mattered most. |
Keywords
» Artificial intelligence » Deep learning » Lstm » Machine learning » Random forest » Xgboost
