Published November 5, 2025
| Version v3
Model
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Predicting Heart Failure Using MIMIC-IV and MIMIC-IV-ED: A Comparative Study of Machine Learning and Deep Learning Models
Authors/Creators
-
1.
University of Malaya
- 2. Universiti Malaya Faculty of Engineering
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3.
University Malaya Medical Centre
Description
This study introduces a heart failure (HF) prediction framework built on electronic health records (EHR) from the MIMIC-IV and MIMIC-IV-ED databases. The proposed system integrates structured clinical variables (e.g., demographics, vitals, laboratory results, comorbidities) with unstructured admission notes encoded using PubMedBERT embeddings. After preprocessing with Winsorization, k-nearest neighbor imputation, and Z-score normalization, multiple machine learning (ML) and deep learning (DL) algorithms were trained and compared, including Random Forest (RF), Logistic Regression, Decision Tree, Naïve Bayes, AdaBoost, Dense Neural Network (DNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN).
Files
BERT.ipynb
Files
(95.4 MB)
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Additional details
Additional titles
- Alternative title
- Heart failure prediction models
Software
- Programming language
- Python