Summary of Enhancing Readmission Prediction with Deep Learning: Extracting Biomedical Concepts From Clinical Texts, by Rasoul Samani et al.
Enhancing Readmission Prediction with Deep Learning: Extracting Biomedical Concepts from Clinical Texts
by Rasoul Samani, Mohammad Dehghani, Fahime Shahrokh
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 abstract discusses a machine learning approach for predicting hospital readmission within 30 days using discharge report texts from electronic health records (EHRs). The study leverages the Bio-Discharge Summary Bert (BDSS) model, principal component analysis (PCA), and deep learning to develop a classification model. The researchers analyzed the MIMIC-III dataset, finding that their approach outperformed state-of-the-art methods with a recall of 94% and an area under the curve (AUC) of 75%. This study contributes to predictive modeling in healthcare by integrating text mining techniques with deep learning algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research predicts when patients will be readmitted to hospital within 30 days using computer analysis. The scientists used special software that understands medical language, called Bio-Discharge Summary Bert (BDSS), and a technique called principal component analysis (PCA) to prepare the data for the model. They tested their approach on the MIMIC-III dataset and found it was better than other methods at predicting readmissions. |
Keywords
» Artificial intelligence » Auc » Bert » Classification » Deep learning » Machine learning » Pca » Principal component analysis » Recall
