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.

This dataset is created from MIMIC-III (Medical Information Mart for Intensive Care III) and contains simulated patient admission notes. The clinical notes contain information about a patient at admission time to the ICU and are labelled for four outcome prediction tasks: Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay. To obtain the data one first has to gain access to the MIMIC-III dataset and then run the scripts introduced in the linked repository.

Retrospectively collected medical data has the opportunity to improve patient care through knowledge discovery and algorithm development. Broad reuse of medical data is desirable for the greatest public good, but data sharing must be done in a manner which protects patient privacy. The Medical Information Mart for Intensive Care (MIMIC)-III database provided critical care data for over 40,000 patients admitted to intensive care units at the Beth Israel Deaconess Medical Center (BIDMC). Importantly, MIMIC-III was deidentified, and patient identifiers were removed according to the Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor provision. MIMIC-III has been integral in driving large amounts of research in clinical informatics, epidemiology, and machine learning. Here we present MIMIC-IV, an update to MIMIC-III, which incorporates contemporary data and improves on numerous aspects of MIMIC-III. MIMIC-IV adopts a modular approach to data organization, highlighting data provenance and facilitating both individual and combined use of disparate data sources. MIMIC-IV is intended to carry on the success of MIMIC-III and support a broad set of applications within healthcare.

Retrospectively collected medical data has the opportunity to improve patient care through knowledge discovery and algorithm development. Broad reuse of medical data is desirable for the greatest public good, but data sharing must be done in a manner which protects patient privacy. Here we present Medical Information Mart for Intensive Care (MIMIC)-IV, a large deidentified dataset of patients admitted to the emergency department or an intensive care unit at the Beth Israel Deaconess Medical Center in Boston, MA. MIMIC-IV contains data for over 65,000 patients admitted to an ICU and over 200,000 patients admitted to the emergency department. MIMIC-IV incorporates contemporary data and adopts a modular approach to data organization, highlighting data provenance and facilitating both individual and combined use of disparate data sources. MIMIC-IV is intended to carry on the success of MIMIC-III and support a broad set of applications within healthcare.

Clinical question answering (QA) (or reading comprehension) aims to automatically answer questions from medical professionals based on clinical texts. We release this dataset, which contains 1287 annotated QA pairs on 36 sampled discharge summaries from MIMIC-III Clinical Notes, to facilitate the clinical question answering task. Questions in our dataset are either verified or directly generated by clinical experts.

Note that the primary purpose of this dataset is to test the generalizability of a QA model, i.e., whether a QA model that is trained on other datasets can answer questions on this dataset (which may have a different distribution compared with the training data), rather than to train a QA model. Hence the scale of our annotations is relatively small compared to some existing QA datasets.

MIMIC_III_IPI - Discharge Summaries from Medical Information Mart for Intensive Care-III with Indirect Personal Identifiers Annotations

The discharge summaries we use for demonstrating our Indirect Personal Identifiers (IPI) schema are randomly sampled from the Medical Information Mart for Intensive Care (MIMIC-III) dataset. MIMIC-III comprises health-related data from over 40,000 patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. Among other types of data, such as patient demographics, the database also includes various types of textual data, such as diagnostic reports and discharge summaries. We chose discharge summaries for our study, since these are richer in information than other notes in MIMIC-III. Details:

• Johnson, A., Pollard, T., & Mark, R. (2016). MIMIC-III Clinical Database (version 1.4). PhysioNet. https://doi.org/10.13026/C2XW26.
• Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L. W., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific data, 3, 160035. https://doi.org/10.1038/sdata.2016.35

This is the Discharge Summaries from MIMIC-III with Indirect Personal Identifiers Annotations as an external source of the paper accepted at the PrivateNLP workshop at NAACL 2025, a preprint can be found in:

• Baroud, I., Raithel, L., Möller, S., & Roller, R. (2025). Beyond De-Identification: A Structured Approach for Defining and Detecting Indirect Identifiers in Medical Texts. arXiv preprint arXiv:2502.13342.

This repository contains the annotations in a CSV file and the annotation guidelines document. Inspecting the exact annotation texts requires access to the MIMIC-III Clinical Database, see https://physionet.org/content/mimiciii/1.4/. Each row in the CSV file has an ID together with a list of the IPI annotated spans, each in the format {"start": ,"end": ,"label": }. The ID in the ipi_annotations.csv table corresponds to the same ROW_ID in the MIMIC-III NOTEEVENTS.csv table and can be used for merging the tables to inspect the original documents and reconstruct the annotations using the offsets.

Please note that only authenticated users can request access to review and download the annotations and guidelines. If you encounter any issues, feel free to reach out to the contact person.

This is a preprocessed dataset derived from patient records in MIMIC-III and eICU, two large-scale electronic health record (EHR) databases. It contains features and labels for 5 prediction tasks involving 3 adverse outcomes (prediction times listed in parentheses): in-hospital mortality (48h), acute respiratory failure (4h and 12h), and shock (4h and 12h). We extracted comprehensive, high-dimensional feature representations (up to ~8,000 features) using FIDDLE (FlexIble Data-Driven pipeLinE), an open-source preprocessing pipeline for structured clinical data. These 5 prediction tasks were designed in consultation with a critical care physician for their clinical importance, and were used as part of the proof-of-concept experiments in the original paper to demonstrate FIDDLE's utility in aiding the feature engineering step of machine learning model development. The intent of this release is to share preprocessed MIMIC-III and eICU datasets used in the experiments to support and enable reproducible machine learning research on EHR data.

Background: Mechanically ventilated patients in the intensive care unit (ICU) have high mortality rates. There are multiple prediction scores, such as the Simplified Acute Physiology Score II (SAPS II), Oxford Acute Severity of Illness Score (OASIS), and Sequential Organ Failure Assessment (SOFA), widely used in the general ICU population. We aimed to establish prediction scores on mechanically ventilated patients with the combination of these disease severity scores and other features available on the first day of admission.Methods: A retrospective administrative database study from the Medical Information Mart for Intensive Care (MIMIC-III) database was conducted. The exposures of interest consisted of the demographics, pre-ICU comorbidity, ICU diagnosis, disease severity scores, vital signs, and laboratory test results on the first day of ICU admission. Hospital mortality was used as the outcome. We used the machine learning methods of k-nearest neighbors (KNN), logistic regression, bagging, decision tree, random forest, Extreme Gradient Boosting (XGBoost), and neural network for model establishment. A sample of 70% of the cohort was used for the training set; the remaining 30% was applied for testing. Areas under the receiver operating characteristic curves (AUCs) and calibration plots would be constructed for the evaluation and comparison of the models' performance. The significance of the risk factors was identified through models and the top factors were reported.Results: A total of 28,530 subjects were enrolled through the screening of the MIMIC-III database. After data preprocessing, 25,659 adult patients with 66 predictors were included in the model analyses. With the training set, the models of KNN, logistic regression, decision tree, random forest, neural network, bagging, and XGBoost were established and the testing set obtained AUCs of 0.806, 0.818, 0.743, 0.819, 0.780, 0.803, and 0.821, respectively. The calibration curves of all the models, except for the neural network, performed well. The XGBoost model performed best among the seven models. The top five predictors were age, respiratory dysfunction, SAPS II score, maximum hemoglobin, and minimum lactate.Conclusion: The current study indicates that models with the risk of factors on the first day could be successfully established for predicting mortality in ventilated patients. The XGBoost model performs best among the seven machine learning models.

Additional file 1: Raw data of relevant clinical data of stroke patients.

Overview

The MIMIC PERform datasets contain physiological signals recorded from critically-ill patients during routine clinical care. Specifically, the datasets contain the following signals:

electrocardiogram (ECG)

photoplethysmogram (PPG)

impedance pneumography (imp), also known as respiratory (resp)

The datasets were extracted from the MIMIC III Waveform Database. Further details of the datasets are provided in the documentation accompanying the ppg-beats project, which is available at: https://ppg-beats.readthedocs.io/en/latest/ .

Datasets

The following datasets are available:

MIMIC PERform AF Dataset: Recordings from 35 critically-ill adults during routine clinical care, categorised as either AF (atrial fibrillation, 19 subjects) or non-AF (16 subjects).

Matlab format (AF subjects, non-AF subjects)

WFDB format (AF subjects, non-AF subjects)

CSV format (AF subjects, non-AF subjects)

MIMIC PERform Training Dataset: Recordings from 200 patients during routine clinical care, who are categorised as either adults (100 subjects) or neonates (100 subjects).

Matlab format (all data, adults, neonates)

WFDB format (all data, adults, neonates)

CSV format (all data, adults, neonates)

MIMIC PERform Testing Dataset: Recordings from 200 patients during routine clinical care, who are categorised as either adults (100 subjects) or neonates (100 subjects).

Matlab format (all data, adults, neonates)

WFDB format (all data, adults, neonates)

CSV format (all data, adults, neonates)

Citation

When using these datasets, please cite the following publication:

Charlton PH et al. Detecting beats in the photoplethysmogram: benchmarking open-source algorithms. Physiological Measurement 2022. DOI: 10.1088/1361-6579/ac826d

Acknowledgments

Each dataset is accompanied by a licence which acknowledges the source(s) of the data - please see the individual licenses for these acknowledgements.

This dataset is a curated subset of MIMIC-III (v1.4), specifically formatted to facilitate reproducibility of the experiments in the work t-PatchGNN. It serves as part of a benchmark designed for forecasting irregular multivariate clinical time series, that is, given a set of historical Irregular Multivariate Time Series (IMTS) observations and forecasting queries, the forecasting problem aims to accurately forecast the values in correspondence to these queries. This requires addressing key challenges such as missing data, variable sampling rates, and complex temporal dependencies. The dataset includes patient records with diverse physiological measurements, each sampled at irregular intervals, reflecting real-world clinical scenarios. It is structured to capture both short-term and long-term temporal patterns, making it well-suited for evaluating machine learning models in medical time series forecasting. By providing a standardized benchmark, this dataset aims to advance research in predictive modeling for healthcare, enabling the development of robust algorithms that can handle irregular and sparse clinical data. The dataset’s applications extend to critical areas such as early disease detection, patient risk stratification, and treatment outcome prediction, making it a valuable resource for the medical AI and machine learning communities.

This dataset contains TensorFlow SequenceExamples derived from patient records in MIMIC-III, a freely available set of deidentified medical records from critical care patients at Beth Israel Deaconess Medical Center. Each SequenceExample converts data from an individual patient encounter and any previous encounters into a set of timestamped “feature lists” describing the patient history up to a certain time, beyond which predictions can be made. These data are suitable for direct input into TensorFlow modeling pipelines, and include labels for inpatient mortality and discharge diagnosis codes for each encounter. The intent of this release is to provide a preprocessed, ready-to-use version of MIMIC-III to support and enable reproducible machine learning research for electronic health records.

The objective of this Bioengineering Research Partnership is to focus the resources of a powerful interdisciplinary team from academia (MIT), industry (Philips Medical Systems) and clinical medicine (Beth Israel Deaconess Medical Center) to develop and evaluate advanced ICU patient monitoring systems that will substantially improve the efficiency, accuracy and timeliness of clinical decision making in intensive care.

The code is about how to extract data from the MIMIC-III. (7Z)

MIMIC-IV ICD-9 contains 209,326 discharge summaries—free-text medical documents—annotated with ICD-9 diagnosis and procedure codes. It contains data for patients admitted to the Beth Israel Deaconess Medical Center emergency department or ICU between 2008-2019. All codes with fewer than ten examples have been removed, and the train-val-test split was created using multi-label stratified sampling. The dataset is described further in Automated Medical Coding on MIMIC-III and MIMIC-IV: A Critical Review and Replicability Study, and the code to use the dataset is found here.

The dataset is intended for medical code prediction and was created using MIMIC-IV v2.2 and MIMIC-IV-NOTE v2.2. Using the two datasets requires a license obtained in Physionet; this can take a couple of days.

The VeriFact-BHC dataset is designed to verify the factuality of long-form text written about a patient against their own electronic health record. There is increasing interest in using large language models (LLMs) to generate clinical text in patient care applications, yet this text needs to be evaluated for factual errors and hallucinations prior to committing text to a patient’s permanent medical record. Text written about a patient should be internally consistent with information already known about the patient, such as that stored in their medical records. VeriFact-BHC contains long-form Brief Hospital Course (BHC) clinical narratives typically found in a discharge summary that have been decomposed into text proposition statements. From 100 patients in the MIMIC-III Clinical Database v1.4, we consider two types of BHC text: a human-written BHC and a LLM-generated BHC. The original human clinician-written BHC is extracted from the discharge summary note. The LLM-generated BHC is composed by a LLM using the patient’s longitudinal clinical notes from the hospital admission. Each BHC is decomposed in two ways: sentence propositions and atomic claim propositions. The remaining electronic health record (EHR) notes for each patient serves as a patient-specific reference of facts that is used by clinicians and VeriFact to assign labels. A total of 13,070 propositions are annotated by multiple clinicians with a ground truth established via majority voting and manual adjudication. Also provided are labels assigned by the VeriFact artificial intelligence system and labels assessing whether propositions are valid from a first-order logic standpoint. The reference EHR for each patient is provided in both machine-readable and PDF formats. By offering this dataset, we hope to spur further investigation and creation of computational systems for automatic chart review and patient-specific fact verification. We invite the research community to utilize this dataset to develop better methods to guardrail patient-specific LLM-generated clinical text.

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.

Online

Website

Github

DataHub

SPARQL endpoint

You can query some of the data online there. There is also the download link. Of course you can download it here.

Context

Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Facing with patients symptoms, experienced caregivers make right medical decisions based on their professional knowledge that accurately grasps relationships between symptoms, diagnosis, and treatments. We aim to capture these relationships by constructing a large and high-quality heterogeneous graph linking patients, diseases, and drugs (PDD) in EMRs.

Content

Specifically, we extract important medical entities from MIMIC-III (Medical Information Mart for Intensive Care III) and automatically link them with the existing biomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDD graph presented is accessible on the Web via the SPARQL endpoint, and provides a pathway for medical discovery and applications, such as effective treatment recommendations.

A subgraph of PDD is illustrated in the followng figure to betterunderstand the PDD graph.

https://github.com/wangmengsd/pdd-graph/raw/master/example.png" alt="enter image description here">

Acknowledgements

Author

Data set belongs to Meng Wang, Jiaheng Zhang, Jun Liu,Wei Hu, Sen Wang, , Wenqiang Liu and Lei Shi

They come from： 1. MOEKLINNS lab, Xi’an Jiaotong University, Xi’an, China 2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 3. Griffith Universtiy, Gold Coast Campus, Australia

Some Email： - Meng Wang：wangmengsd@stu.xjtu.edu.cn - Lei Shi：xjtushilei@foxmail.com - Jun Liu：liukeen@xjtu.edu.cn

Research

The paper is being reviewed and is not easily disclosed.So it can't be linked here.

Inspiration

If you have any questions, please contact the email address above.

Do you have any suggestions ? And send them to an e-mail address above.

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

### If your article needs to be reference our work , you can reference our github.

Physicians record their detailed thought-processes about diagnoses and treatments as unstructured text in a section of a clinical note called the "assessment and plan". This information is more clinically rich than structured billing codes assigned for an encounter but harder to reliably extract given the complexity of clinical language and documentation habits. To structure these sections we collected a dataset of annotations over assessment and plan sections from the publicly available and de-identified MIMIC-III dataset, and developed deep-learning based models to perform this task, described in the associated paper available as a pre-print at: https://www.medrxiv.org/content/10.1101/2022.04.13.22273438v1

When using this data please cite our paper:

@article {Stupp2022.04.13.22273438, author = {Stupp, Doron and Barequet, Ronnie and Lee, I-Ching and Oren, Eyal and Feder, Amir and Benjamini, Ayelet and Hassidim, Avinatan and Matias, Yossi and Ofek, Eran and Rajkomar, Alvin}, title = {Structured Understanding of Assessment and Plans in Clinical Documentation}, year = {2022}, doi = {10.1101/2022.04.13.22273438}, publisher = {Cold Spring Harbor Laboratory Press}, URL = {https://www.medrxiv.org/content/early/2022/04/17/2022.04.13.22273438}, journal = {medRxiv} }

The dataset, presented here, contains annotations of assessment and plan sections of notes from the publicly available and de-identified MIMIC-III dataset, marking the active problems, their assessment description, and plan action items. Action items are additionally marked as one of 8 categories (listed below). The dataset contains over 30,000 annotations of 579 notes from distinct patients, annotated by 6 medical residents and students.

The dataset is divided into 4 partitions - a training set (481 notes), validation set (50 notes), test set (48 notes) and an inter-rater set. The inter-rater set contains the annotations of each of the raters over the test set. Rater 1 in the inter-rater set should be regarded as an intra-rater comparison (details in the paper). The labels underwent automatic normalization to capture entire word boundaries and remove flanking non-alphanumeric characters.

Code for transforming labels into TensorFlow examples and training models as described in the paper will be made available at GitHub: https://github.com/google-research/google-research/tree/master/assessment_plan_modeling

In order to use these annotations, the user additionally needs to obtain the text of the notes which is found in the NOTE_EVENTS table from MIMIC-III, access to which is to be acquired independently (https://mimic.mit.edu/)

Annotations are given as character spans in a CSV file with the following schema:

    Field
    Type
    Semantics


    partition
    categorical (one of [train, val, test, interrater]
    The set of ratings the span belongs to.


    rater_id
    int
    Unique id for each the raters


    note_id
    int
    The note’s unique note_id, links to the MIMIC-III notes table (as ROW-ID).


    span_type
    categorical (one of [PROBLEM_TITLE,
    PROBLEM_DESCRIPTION, ACTION_ITEM]
    Type of the span as annotated by raters.


    char_start
    int
    Character offsets from note start


    char_end
    int


    action_item_type
    categorical (one of [MEDICATIONS, IMAGING, OBSERVATIONS_LABS, CONSULTS, NUTRITION, THERAPEUTIC_PROCEDURES, OTHER_DIAGNOSTIC_PROCEDURES, OTHER])
    Type of action item if the span is an action item (empty otherwise) as annotated by raters.

Collection of comprising deidentified health related data associated with patients who stayed in critical care units of Beth Israel Deaconess Medical Center between 2001 and 2012. Database includes information such as demographics, vital sign measurements made at bedside (~1 data point per hour), laboratory test results, procedures, medications, caregiver notes, imaging reports, and mortality (both in and out of hospital).