The Medical Information Mart for Intensive Care (MIMIC)-IV database is comprised of deidentified electronic health records for patients admitted to the Beth Israel Deaconess Medical Center. Access to MIMIC-IV is limited to credentialed users. Here, we have provided an openly-available demo of MIMIC-IV containing a subset of 100 patients. The dataset includes similar content to MIMIC-IV, but excludes free-text clinical notes. The demo may be useful for running workshops and for assessing whether the MIMIC-IV is appropriate for a study before making an access request.

Dataset Source

This dataset is a portion of MIMIC-III Clinical Database, a large, freely-available database comprising deidentified health-related data associated with over forty thousand patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. The MIMIC-III demo provides researchers with an opportunity to review the structure and content of MIMIC-III before deciding whether or not to carry out an analysis on the full dataset. The full dataset is available on PhysioNet this** link**

Dataset Description:

This dataset contains solely 4 tables (extracted from the original dataset), more informations about each table can be found in its corresponding link - admissions.csv
- d_labitems.csv - labevents.csv - patient.csv a nice visualization of this dataset can be found here

Future Perspectives:

This portion of the dataset will be combined to build a comprehensive dataset of simulated medical reports.

Abstract MIMIC-III is a large, freely-available database comprising deidentified health-related data associated with over 40,000 patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012 [1]. The MIMIC-III Clinical Database is available on PhysioNet (doi: 10.13026/C2XW26). Though deidentified, MIMIC-III contains detailed information regarding the care of real patients, and as such requires credentialing before access. To allow researchers to ascertain whether the database is suitable for their work, we have manually curated a demo subset, which contains information for 100 patients also present in the MIMIC-III Clinical Database. Notably, the demo dataset does not include free-text notes.

Background In recent years there has been a concerted move towards the adoption of digital health record systems in hospitals. Despite this advance, interoperability of digital systems remains an open issue, leading to challenges in data integration. As a result, the potential that hospital data offers in terms of understanding and improving care is yet to be fully realized.

MIMIC-III integrates deidentified, comprehensive clinical data of patients admitted to the Beth Israel Deaconess Medical Center in Boston, Massachusetts, and makes it widely accessible to researchers internationally under a data use agreement. The open nature of the data allows clinical studies to be reproduced and improved in ways that would not otherwise be possible.

The MIMIC-III database was populated with data that had been acquired during routine hospital care, so there was no associated burden on caregivers and no interference with their workflow. For more information on the collection of the data, see the MIMIC-III Clinical Database page.

Methods The demo dataset contains all intensive care unit (ICU) stays for 100 patients. These patients were selected randomly from the subset of patients in the dataset who eventually die. Consequently, all patients will have a date of death (DOD). However, patients do not necessarily die during an individual hospital admission or ICU stay.

This project was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA). Requirement for individual patient consent was waived because the project did not impact clinical care and all protected health information was deidentified.

Data Description MIMIC-III is a relational database consisting of 26 tables. For a detailed description of the database structure, see the MIMIC-III Clinical Database page. The demo shares an identical schema, except all rows in the NOTEEVENTS table have been removed.

The data files are distributed in comma separated value (CSV) format following the RFC 4180 standard. Notably, string fields which contain commas, newlines, and/or double quotes are encapsulated by double quotes ("). Actual double quotes in the data are escaped using an additional double quote. For example, the string she said "the patient was notified at 6pm" would be stored in the CSV as "she said ""the patient was notified at 6pm""". More detail is provided on the RFC 4180 description page: https://tools.ietf.org/html/rfc4180

Usage Notes The MIMIC-III demo provides researchers with an opportunity to review the structure and content of MIMIC-III before deciding whether or not to carry out an analysis on the full dataset.

CSV files can be opened natively using any text editor or spreadsheet program. However, some tables are large, and it may be preferable to navigate the data stored in a relational database. One alternative is to create an SQLite database using the CSV files. SQLite is a lightweight database format which stores all constituent tables in a single file, and SQLite databases interoperate well with a number software tools.

DB Browser for SQLite is a high quality, visual, open source tool to create, design, and edit database files compatible with SQLite. We have found this tool to be useful for navigating SQLite files. Information regarding installation of the software and creation of the database can be found online: https://sqlitebrowser.org/

Release Notes Release notes for the demo follow the release notes for the MIMIC-III database.

Acknowledgements This research and development was supported by grants NIH-R01-EB017205, NIH-R01-EB001659, and NIH-R01-GM104987 from the National Institutes of Health. The authors would also like to thank Philips Healthcare and staff at the Beth Israel Deaconess Medical Center, Boston, for supporting database development, and Ken Pierce for providing ongoing support for the MIMIC research community.

Conflicts of Interest The authors declare no competing financial interests.

References Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L. H., Feng, M., Ghassemi, M., Mo...

Digitization of healthcare data along with algorithmic breakthroughts in AI will have a major impact on healthcare delivery in coming years. Its intresting to see application of AI to assist clinicians during patient treatment in a privacy preserving way. While scientific knowledge can help guide interventions, there remains a key need to quickly cut through the space of decision policies to find effective strategies to support patients during the care process.

Offline Reinforcement learning (also referred to as safe or batch reinforcement learning) is a promising sub-field of RL which provides us with a mechanism for solving real world sequential decision making problems where access to simulator is not available. Here we assume that learn a policy from fixed dataset of trajectories with further interaction with the environment(agent doesn't receive reward or punishment signal from the environment). It has shown that such an approach can leverage vast amount of existing logged data (in the form of previous interactions with the environment) and can outperform supervised learning approaches or heuristic based policies for solving real world - decision making problems. Offline RL algorithms when trained on sufficiently large and diverse offline datasets can produce close to optimal policies(ability to generalize beyond training data).

As Part of my PhD, research, I investigated the problem of developing a Clinical Decision Support System for Sepsis Management using Offline Deep Reinforcement Learning.

MIMIC-III ('Medical Information Mart for Intensive Care') is a large open-access anonymized single-center database which consists of comprehensive clinical data of 61,532 critical care admissions from 2001–2012 collected at a Boston teaching hospital. Dataset consists of 47 features (including demographics, vitals, and lab test results) on a cohort of sepsis patients who meet the sepsis-3 definition criteria.

we try to answer the following question:

Given a particular patient’s characteristics and physiological information at each time step as input, can our DeepRL approach, learn an optimal treatment policy that can prescribe the right intervention(e.g use of ventilator) to the patient each stage of the treatment process, in order to improve the final outcome(e.g patient mortality)?

we can use popular state-of-the-art algorithms such as Deep Q Learning(DQN), Double Deep Q Learning (DDQN), DDQN combined with BNC, Mixed Monte Carlo(MMC) and Persistent Advantage Learning (PAL). Using these methods we can train an RL policy to recommend optimum treatment path for a given patient.

Data acquisition, standard pre-processing and modelling details can be found here in Github repo: https://github.com/asjad99/MIMIC_RL_COACH

The MIMIC-III Waveform Database contains 67,830 record sets for approximately 30,000 ICU patients. Almost all record sets include a waveform record containing digitized signals (typically including ECG, ABP, respiration, and PPG, and frequently other signals) and a “numerics” record containing time series of periodic measurements, each presenting a quasi-continuous recording of vital signs of a single patient throughout an ICU stay (typically a few days, but many are several weeks in duration). A subset of this database contains waveform and numerics records that have been matched and time-aligned with MIMIC-III Clinical Database records.

MIMIC-IV-ED is a large, freely available database of emergency department (ED) admissions at the Beth Israel Deaconess Medical Center between 2011 and 2019. The database contains ~425,000 ED stays. Vital signs, triage information, medication reconciliation, medication administration, and discharge diagnoses are available. All data are deidentified to comply with the Health Information Portability and Accountability Act (HIPAA) Safe Harbor provision. MIMIC-IV-ED is intended to support a diverse range of education initiatives and research studies.

MIMIC-III is a dataset comprising health-related data associated with over 40,000 patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012

ntphuc149/MIMIC-III-Clinical-Database dataset hosted on Hugging Face and contributed by the HF Datasets community

MIMIC-III is a database of critically ill patients admitted to an intensive care unit (ICU) at the Beth Israel Deaconess Medical Center (BIDMC) in Boston, MA. MIMIC-III has seen broad use, and was updated with the release of MIMIC-IV. MIMIC-IV contains more contemporaneous stays, higher granularity data, and expanded domains of information. To maximize the sample size of MIMIC-IV, the database overlaps with MIMIC-III, and specifically both databases contain the same admissions which occurred between 2008 - 2012. This overlap complicates analyses of the two databases simultaneously. Here we provide a subset of MIMIC-III containing patients who are not in MIMIC-IV. The goal of this project is to simplify the combination of MIMIC-III with MIMIC-IV.

The MIMIC-IV-ECG module contains approximately 800,000 diagnostic electrocardiograms across nearly 160,000 unique patients. These diagnostic ECGs use 12 leads and are 10 seconds in length. They are sampled at 500 Hz. This subset contains all of the ECGs for patients who appear in the MIMIC-IV Clinical Database. When a cardiologist report is available for a given ECG, we provide the needed information to link the waveform to the report. The patients in MIMIC-IV-ECG have been matched against the MIMIC-IV Clinical Database, making it possible to link to information across the MIMIC-IV modules.

EHR downtime impacts an estimated 13.2% of the U.S. population, disrupting access to patient health records and creating delays in clinical decision-making. Raw medical transcripts are often unstructured, inconsistent, and sensitive, making them difficult to use directly for research or AI applications. This leads to wasted time on preprocessing and limits the potential for advanced analytics.

This dataset provides cleaned and de-identified medical transcripts from MIMIC-IV, allowing researchers to focus on NLP, predictive modeling, and knowledge graph applications without the burden of raw data cleaning. By reducing barriers to analysis, it supports the development of tools that can improve healthcare efficiency and patient outcomes.

Applications: - Healthcare NLP (Named Entity Recognition, text classification) - Predictive modeling for admission/discharge outcomes - Analysis of patient demographics and clinical severity - AI-driven knowledge graph construction from structured + unstructured hospital data

Notes Data is de-identified to ensure HIPAA compliance Intended for research and educational purposes only Source: MIMIC-IV, MIT Laboratory for Computational Physiology

BackgroundExisting research suggests that using statins may reduce the incidence of enteritis caused by C. difficile and improve the prognosis of patients. This study aimed to explore the relation between Clostridium difficile-induced enteritis (CDE) and statin use.MethodsData were collected from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Multivariate logistic regression analysis was employed to assess the impact of statin use on CDE incidence in patients in intensive care units (ICUs) and its effect on in-hospital mortality among them. The research findings were validated by performing propensity score matching (PSM), inverse probability of treatment weighting (IPTW), and subgroup analyses.ResultsThe study enrolled the data of 51,978 individuals to assess the effect of statin usage on the occurrence of CDE in patients admitted to the ICU. The results indicate that statins can decrease the prevalence of CDE in patients in ICU (odds ratio (OR): 0.758, 95% confidence interval (CI): 0.666–0.873, P < 0.05), which was further confirmed through PSM (OR: 0.760, 95% CI: 0.661–0.873, P < 0.05) and IPTW (OR: 0.818, 95% CI: 0.754–0.888, P < 0.05) analyses. For most subgroups, statins’ favorable effect in reducing CDE remained constant. A total of 1,208 patients were included in the study to evaluate whether statins could lower the risk of death in patients in ICU with enteritis caused by C. difficile. Statins did not reduce in-hospital mortality of patients in ICU with CDE (OR: 0.911, 95% CI: 0.667–1.235, P = 0.553). The results were validated following PSM (OR: 0.877, 95% CI: 0.599–1.282, P = 0.499) and IPTW (OR: 0.781, 95% CI: 0.632–1.062, P = 0.071) analyses, and all subgroups demonstrated consistent results.ConclusionStatin administration can reduce the incidence of CDE in patients in the ICU; however, it does not decrease the in-hospital mortality rate for individuals with CDE.

This new dataset was established according to the MIMIC III dataset, an openly available database developed by The Laboratory of Computational Physiology at Massachusetts Institute of Technology (MIT), which consists of data from more than 25,000 patients who were admitted to the Beth Israel Deaconess Medical Center (BIDMC) since 2003 and who have been de-identified for information safety. Here, we identified patients who were diagnosed as pelvic, acetabular, or combined pelvic and acetabular fractures according to ICD-9 code and who survived at least 72 hours after the ICU admission. All the data within the first 72 hours following ICU admission were collected and extracted from the MIMIC-III clinical database version 1.4.

Published studies using the MIMIC database

We conducted our experiments on de-identified EHR data from MIMIC-III. This data set contains various clinical data relating to patient admission to ICU, such as disease diagnoses in the form of International Classification of Diseases (ICD)-9 codes, and lab test results as detailed in Supplementary Materials. We collected data for 5,956 patients, extracting lab tests every hour from admission. There are a total of 409 unique lab tests and 3,387 unique disease diagnoses observed. The diagnoses were obtained as ICD-9 codes and they were represented using one-hot encoding where one represents patients with disease and zero indicates those without. We binned the lab test events into 6, 12, 24, and 48 hours prior to patient death or discharge from ICU. From these data, we performed mortality predictions that are 10-fold, cross validated.

The advent of large, open access text databases has driven advances in state-of-the-art model performance in natural language processing (NLP). The relatively limited amount of clinical data available for NLP has been cited as a significant barrier to the field's progress. Here we describe MIMIC-IV-Note: a collection of deidentified free-text clinical notes for patients included in the MIMIC-IV clinical database. MIMIC-IV-Note contains 331,794 deidentified discharge summaries from 145,915 patients admitted to the hospital and emergency department at the Beth Israel Deaconess Medical Center in Boston, MA, USA. The database also contains 2,321,355 deidentified radiology reports for 237,427 patients. All notes have had protected health information removed in accordance with the Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor provision. All notes are linkable to MIMIC-IV providing important context to the clinical data therein. The database is intended to stimulate research in clinical natural language processing and associated areas.

We provide some annotations of the Medical Information Mart for Intensive Care (MIMIC) III waveform database matched Subset. The annotations are for the electrocardiogram recordings and denote atrial fibrillation status.More annotations will be added in future.Details about MIMIC III matched subset can be found at Physionet.https://archive.physionet.org/physiobank/database/mimic3wdb/matched/If you use the annotations, please cite the following paper:Bashar, S.K., Ding, E., Walkey, A.J., McManus, D.D. and Chon, K.H., 2019. Noise Detection in Electrocardiogram Signals for Intensive Care Unit Patients. IEEE Access, 7, pp.88357-88368

MIMIC II (Multiparameter Intelligent Monitoring in Intensive Care) Database contains comprehensive clinical data from tens of thousands of Intensive Care Unit (ICU) patients. Data were collected between 2001 and 2008 from a variety of ICUs (medical, surgical, coronary care, and neonatal) in a single tertiary teaching hospital. The database contains clinical data from bedside workstations as well as hospital archives. The database also includes thousands of records of continuous high-resolution physiologic waveforms and minute-by-minute numeric time series (trends) of physiologic measurements.

Database of MRI quantitative measurements gathered from literature and experimental studies, for tissues and synthetic materials.Additionally, a code base is provided to aid in finding MRI tissue relaxation times for a target field strength, and to provide functionality to solve for tissue mimic composition given target tissue relaxation times.

This database is created to enable community-based sepsis detection research. It is a subset of MIMIC-III Waveform Database Matched Subset. Sepsis onset is calculated based on Sepsis-3 criteria. Total of 447 patients are included. Further details can be found in our research paper or description file.If you use the annotations, please cite the following paper:..Details about MIMIC III matched subset can be found at Physionet.https://physionet.org/content/mimic3wdb-matched/1.0/