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

arXiv: https://arxiv.org/abs/2506.12808 · HTML: https://mlsanity.com/paper/2506.12808/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 210 references that are in the map.
- First posted on arXiv 15 Jun 2025.

## Builds on (most cited first, 29 of 29)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [Deep Structural Causal Models for Tractable Counterfactual Inference](https://mlsanity.com/paper/2006.06485/) (2020)
- [Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis](https://mlsanity.com/paper/1706.03446/) (2017)
- [MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III](https://mlsanity.com/paper/1907.08322/) (2019)
- [A Review of Challenges and Opportunities in Machine Learning for Health](https://mlsanity.com/paper/1806.00388/) (2018)
- [An Extensive Data Processing Pipeline for MIMIC-IV](https://mlsanity.com/paper/2204.13841/) (2022)
- [Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health Records](https://mlsanity.com/paper/1709.01648/) (2017)
- [Clinical Intervention Prediction and Understanding using Deep Networks](https://mlsanity.com/paper/1705.08498/) (2017)
- [Benchmarking machine learning models on multi-centre eICU critical care dataset](https://mlsanity.com/paper/1910.00964/) (2019)
- [Learning from data with structured missingness](https://mlsanity.com/paper/2304.01429/) (2023)
- [Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data](https://mlsanity.com/paper/2010.01149/) (2020)
- [Improving Visualization Interpretation Using Counterfactuals](https://mlsanity.com/paper/2107.10309/) (2021)
- [Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets](https://mlsanity.com/paper/1710.08531/) (2017)
- [ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU](https://mlsanity.com/paper/1901.08201/) (2019)
- [Benchmarking with MIMIC-IV, an irregular, spare clinical time series dataset](https://mlsanity.com/paper/2401.15290/) (2024)
- [Generalized and Transferable Patient Language Representation for Phenotyping with Limited Data](https://mlsanity.com/paper/2103.00482/) (2021)
- [From controlled to undisciplined data: estimating causal effects in the era of data science using a potential outcome framework](https://mlsanity.com/paper/2012.06865/) (2020)
- [Processing of Electronic Health Records using Deep Learning: A review](https://mlsanity.com/paper/1804.01758/) (2018)
- [Patient trajectory prediction in the Mimic-III dataset, challenges and pitfalls](https://mlsanity.com/paper/1909.04605/) (2019)
- [Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or pre-processing](https://mlsanity.com/paper/1909.07214/) (2019)
- [ReproduceMeGit: A Visualization Tool for Analyzing Reproducibility of Jupyter Notebooks](https://mlsanity.com/paper/2006.12110/) (2020)
- [Causal inference for observational longitudinal studies using deep survival models](https://mlsanity.com/paper/2101.10643/) (2021)
- [A Primer on the Data Cleaning Pipeline](https://mlsanity.com/paper/2307.13219/) (2023)
- [Machine Learning-Based Prediction of ICU Readmissions in Intracerebral Hemorrhage Patients: Insights from the MIMIC Databases](https://mlsanity.com/paper/2501.01183/) (2025)

## Built on by (most cited first, 2 of 2)

- [A Multimodal Data Processing Pipeline for MIMIC-IV Dataset](https://mlsanity.com/paper/2601.11606/) (2026)
- [Resilient Vision-Tabular Multimodal Learning under Modality Missingness](https://mlsanity.com/paper/2605.12031/) (2026)

## Abstract (arXiv)

The Medical Information Mart for Intensive Care (MIMIC) datasets have become the Kernel of Digital Health Research by providing freely accessible, deidentified records from tens of thousands of critical care admissions, enabling a broad spectrum of applications in clinical decision support, outcome prediction, and healthcare analytics. Although numerous studies and surveys have explored the predictive power and clinical utility of MIMIC based models, critical challenges in data integration, representation, and interoperability remain underexplored. This paper presents a comprehensive survey that focuses uniquely on open problems. We identify persistent issues such as data granularity, cardinality limitations, heterogeneous coding schemes, and ethical constraints that hinder the generalizability and real-time implementation of machine learning models. We highlight key progress in dimensionality reduction, temporal modelling, causal inference, and privacy preserving analytics, while also outlining promising directions including hybrid modelling, federated learning, and standardized preprocessing pipelines. By critically examining these structural limitations and their implications, this survey offers actionable insights to guide the next generation of MIMIC powered digital health innovations.