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

• URL: http://arxiv.org/abs/2506.12808v1
• Date: Sun, 15 Jun 2025 10:47:07 GMT
• Title: Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises
• Authors: Afifa Khaled, Mohammed Sabir, Rizwan Qureshi, Camillo Maria Caruso, Valerio Guarrasi, Suncheng Xiang, S Kevin Zhou,
• Abstract summary: The Medical Information Mart for Intensive Care (MIMIC) datasets have become the Kernel of Digital Health Research.<n>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.<n>This survey offers actionable insights to guide the next generation of MIMIC powered digital health innovations.
• Score: 15.072974454383925
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications [59.721265428780946]
  Large Language Models (LLMs) in medicine have enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning.<n>This paper provides the first systematic review of this emerging field.<n>We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies and test-time mechanisms.
  arXiv  Detail & Related papers  (2025-08-01T14:41:31Z)
• Anomaly Detection and Generation with Diffusion Models: A Survey [51.61574868316922]
  Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing.<n>Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest.<n>This survey aims to guide researchers and practitioners in leveraging DMs for innovative AD solutions across diverse applications.
  arXiv  Detail & Related papers  (2025-06-11T03:29:18Z)
• A systematic review of challenges and proposed solutions in modeling multimodal data [0.9674145073701153]
  Multimodal data modeling has emerged as a powerful approach in clinical research.<n>This systematic review synthesizes findings from 69 studies to identify common obstacles.<n>We highlight recent methodological advances, such as transfer learning, generative models, attention mechanisms, and neural architecture search that offer promising solutions.
  arXiv  Detail & Related papers  (2025-05-11T11:23:51Z)
• Artificial Intelligence for Personalized Prediction of Alzheimer's Disease Progression: A Survey of Methods, Data Challenges, and Future Directions [0.8249694498830561]
  Alzheimer's Disease (AD) is marked by significant inter-individual variability in its progression.<n>This review aims to consolidate current knowledge and guide future efforts in developing clinically relevant AI tools for personalized AD prognostication.
  arXiv  Detail & Related papers  (2025-04-29T21:45:28Z)
• Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [61.633126163190724]
  Mental illness is a widespread and debilitating condition with substantial societal and personal costs.<n>Recent advances in Artificial Intelligence (AI) hold great potential for recognizing and addressing conditions such as depression, anxiety disorder, bipolar disorder, schizophrenia, and post-traumatic stress disorder.<n>Privacy concerns, including the risk of sensitive data leakage from datasets and trained models, remain a critical barrier to deploying these AI systems in real-world clinical settings.
  arXiv  Detail & Related papers  (2025-02-01T15:10:02Z)
• Electronic Health Records: Towards Digital Twins in Healthcare [1.702436955667761]
  This chapter explores the evolution and significance of healthcare information systems.<n>It begins with an examination of the implementation of EHR in the UK and the USA.<n>It provides a comprehensive overview of the International Classification of Diseases (ICD) system, tracing its development from ICD-9 to ICD-10.<n>Central to this discussion is the MIMIC-III database, a landmark achievement in healthcare data sharing and arguably the most comprehensive critical care database freely available to researchers worldwide.
  arXiv  Detail & Related papers  (2025-01-16T16:30:02Z)
• TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [54.98321887435557]
  This paper presents a suite of 23 meticulously curated AI-ready datasets covering multi-modal input features and 8 crucial prediction challenges in clinical trial design.<n>We provide basic validation methods for each task to ensure the datasets' usability and reliability.<n>We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
  arXiv  Detail & Related papers  (2024-06-30T09:13:10Z)
• A Survey of Few-Shot Learning for Biomedical Time Series [3.845248204742053]
  Data-driven models have tremendous potential to assist clinical diagnosis and improve patient care. An emerging approach to overcome the scarcity of labeled data is to augment AI methods with human-like capabilities to learn new tasks with limited examples, called few-shot learning. This survey provides a comprehensive review and comparison of few-shot learning methods for biomedical time series applications.
  arXiv  Detail & Related papers  (2024-05-03T21:22:27Z)
• Recent Advances in Predictive Modeling with Electronic Health Records [71.19967863320647]
  utilizing EHR data for predictive modeling presents several challenges due to its unique characteristics. Deep learning has demonstrated its superiority in various applications, including healthcare.
  arXiv  Detail & Related papers  (2024-02-02T00:31:01Z)
• Clairvoyance: A Pipeline Toolkit for Medical Time Series [95.22483029602921]
  Time-series learning is the bread and butter of data-driven *clinical decision support* Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a software toolkit. Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML.
  arXiv  Detail & Related papers  (2023-10-28T12:08:03Z)
• Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions [9.662980267339375]
  We focus on medical applications of acFL, particularly in the context of global cancer diagnosis. Recent developments in acFL have made it possible to train complex machine-learned models in a distributed manner.
  arXiv  Detail & Related papers  (2022-08-05T21:41:15Z)
• Data-Centric Epidemic Forecasting: A Survey [56.99209141838794]
  This survey delves into various data-driven methodological and practical advancements. We enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems.
  arXiv  Detail & Related papers  (2022-07-19T16:15:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.