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October 15, 2025Open Access

Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises

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This review identifies challenges in machine learning models from MIMIC data, highlighting progress and future directions in digital health.

Data integration challenges hinder clinical decision support and outcome prediction from MIMIC datasets, posing significant limitations.
Key issues include data granularity and heterogeneous coding schemes, which restrict the generalizability of machine learning models.
Progress highlighted includes advancements in causal inference and dimensionality reduction techniques for enhanced analytics.
Future promises of hybrid modeling and federated learning aim to address ethical constraints and improve implementation in healthcare.

Khaled et al. (2025) studied this question.

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October 15, 2025Open Access

View Full Paper

Ask AI

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Authors

This review identifies challenges in machine learning models from MIMIC data, highlighting progress and future directions in digital health.

Data integration challenges hinder clinical decision support and outcome prediction from MIMIC datasets, posing significant limitations.
Key issues include data granularity and heterogeneous coding schemes, which restrict the generalizability of machine learning models.
Progress highlighted includes advancements in causal inference and dimensionality reduction techniques for enhanced analytics.
Future promises of hybrid modeling and federated learning aim to address ethical constraints and improve implementation in healthcare.

Khaled et al. (2025) studied this question.

View Full Paper

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