MIMIC-III is 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 database includes information such as demographics, vital sign measurements made at the bedside (~1 data point per hour), laboratory test results, procedures, medications, caregiver notes, imaging reports, and mortality (including post-hospital discharge).MIMIC supports a diverse range of analytic studies spanning epidemiology, clinical decision-rule improvement, and electronic tool development. It is notable for three factors: it is freely available to researchers worldwide; it encompasses a diverse and very large population of ICU patients; and it contains highly granular data, including vital signs, laboratory results, and medications.

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.

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.

croyer/MIMIC-III-split dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

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.

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.

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/

Dataset Card for "mimiciii-hospitalcourse-meta"

More Information needed

The MIMIC PERform datasets are a series of datasets extracted from the MIMIC III Waveform Database. Each dataset contains recordings of physiological signals 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)

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/ . In particular, documentation is provided on the following datasets:

• 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 (all data, adults, neonates)
  • WFDB format (all data, adults, neonates)
  • CSV format (all data, adults, neonates)
• 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 (AF subjects, non-AF subjects)
  • WFDB format (AF subjects, non-AF subjects)
  • CSV format (AF subjects, non-AF subjects)

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

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.

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.

Dataset

This dataset was created by chan hainguyen

Released under MIT

Contents

and the eICU

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

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

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).

The MIMIC-III-full and MIMIC-III-top 50 datasets are used for training and testing the proposed model. The MIMIC-III-full dataset contains all the records, while the MIMIC-III-top 50 dataset contains only the top 50 most frequent ICD codes.

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.

Additional file 1. Extracted raw data from the MIMIC-III.