ArticleProceedings of machine learning research2022
An Extensive Data Processing Pipeline for MIMIC-IV.
Article in Proceedings of machine learning research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- Visual prompt engineering for multimodal and irregularly sampled medical data.Communications medicine · 2026Article
- Associations of NLR, MLR, and (neutrophil + monocyte)/lymphocyte ratio with 30-day mortality in diabetic ICU patients with heart failure: A retrospective cohort study.International journal of cardiology. Heart & vasculature · 2026Article
- Censoring-adjusted tree-based policy learning for estimating dynamic treatment regimes with censored outcomes.Scientific reports · 2026Article
- Mitigating algorithmic unfairness arising from forgetfulness of medical records in clinical artificial intelligence.Nature communications · 2026Article
- Research Topics and Trends in MIMIC-IV: A Large ICU Database Relevant for Critical Care Nursing.Nursing in critical care · 2026Review
- Discovery of data quality issues in electronic health records: profound consequences for critical care medicine applications - a systematized review.Critical care (London, England) · 2026Review
- Bridging data gaps of rare conditions in ICU: a multi-disease adaptation approach for clinical prediction.NPJ digital medicine · 2026Article
- Partial VOROS: A Cost-aware Performance Metric for Binary Classifiers with Precision and Capacity Constraints.Proceedings of machine learning research · 2026Article
- NutriSighT: Interpretable Transformer Model for Dynamic Prediction of Underfeeding Enteral Nutrition in Mechanically Ventilated Patients.Nature communications · 2025Article
- A multi-view validation framework for LLM-generated knowledge graphs of chronic kidney disease.International journal of computer assisted radiology and surgery · 2025Article
- Methods for Addressing Missingness in Electronic Health Record Data for Clinical Prediction Models: Comparative Evaluation.JMIR medical informatics · 2025Article
- Challenges and Recommendations for Electronic Health Records Data Extraction and Preparation for Dynamic Prediction Modeling in Hospitalized Patients: Practical Guide and Tutorial.Journal of medical Internet research · 2025Article
- Large language models forecast patient health trajectories enabling digital twins.NPJ digital medicine · 2025Article
- Predicting appropriateness of antibiotic treatment among ICU patients with hospital-acquired infection.NPJ digital medicine · 2025Article
- Development and validation of a cardiac surgery-associated acute kidney injury prediction model using the MIMIC-IV database.PloS one · 2025Article
- Enhancing gastroenterology with multimodal learning: the role of large language model chatbots in digestive endoscopy.Frontiers in medicine · 2025Article
- EHRchitect: An open-source software tool for medical event sequences data extraction from Electronic Health Records.Journal of clinical and translational science · 2025Article
- Forecasting acute kidney injury and resource utilization in ICU patients using longitudinal, multimodal models.Journal of biomedical informatics · 2024Article
- HealthGAT: Node Classifications in Electronic Health Records using Graph Attention Networks....IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies · 2024Article
- Early prediction of long hospital stay for Intensive Care units readmission patients using medication information.Computers in biology and medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
An increasing amount of research is being devoted to applying machine learning methods to electronic health record (EHR) data for various clinical purposes. This growing area of research has exposed the challenges of the accessibility of EHRs. MIMIC is a popular, public, and free EHR dataset in a raw format that has been used in numerous studies. The absence of standardized preprocessing steps can be, however, a significant barrier to the wider adoption of this rare resource. Additionally, this absence can reduce the reproducibility of the developed tools and limit the ability to compare the results among similar studies. In this work, we provide a greatly customizable pipeline to extract, clean, and preprocess the data available in the fourth version of the MIMIC dataset (MIMIC-IV). The pipeline also presents an end-to-end wizard-like package supporting predictive model creations and evaluations. The pipeline covers a range of clinical prediction tasks which can be broadly classified into four categories - readmission, length of stay, mortality, and phenotype prediction. The tool is publicly available at https://github.com/healthylaife/MIMIC-IV-Data-Pipeline.
Indexed as
Identifiers
36686986PMC9854277What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.