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A Temporal Dataset for Respiratory Support in Critically Ill Patients

Mira Moukheiber Lama Moukheiber Dana Moukheiber Sicheng Hao Leo Anthony Celi Hyung-Chul Lee

Published: May 31, 2024. Version: 1.0.0

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Moukheiber, M., Moukheiber, L., Moukheiber, D., Hao, S., Celi, L. A., & Lee, H. (2024). A Temporal Dataset for Respiratory Support in Critically Ill Patients (version 1.0.0). PhysioNet.

Please include the standard citation for PhysioNet: (show more options)
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.


We present a temporal benchmark dataset for clinical respiratory intervention tasks in intensive care unit (ICU) patients, derived from the MIMIC v2.2 dataset. The data consists of 50,920 adult ICU patients and includes 90-day hourly ventilation data, laboratory results, vital signs, and details of treatment interventions such as respiratory support interventions, vasopressor administration, and continuous renal replacement therapy. Additionally, it encompasses static variables such as ICU length of stay, discharge outcome, and death outcome. While there are existing open ICU datasets, the limited availability of benchmark datasets designed for clinical treatments in respiratory support motivated the development of the data. The data facilitates various tasks including time-series analysis, survival analysis, sequential decision-making tasks, and reinforcement learning.


Critically-ill patients often find themselves in the intensive care unit (ICU) seeking specialized support for respiratory distress  [1]. Despite advances in supportive treatments, the in-hospital mortality rate remains 40% for conditions such as acute lung injury and acute respiratory distress syndrome. Managing respiratory distress involves intricate treatment measures, including invasive mechanical ventilation, non-invasive mechanical ventilation, and high-flow nasal cannula. However, existing recommendations and outcomes, especially regarding intubation and weaning procedures for ICU patients, remain controversial and poorly understood [2].

In complex ICU environments, well-curated observational data is crucial for evidence-based decision-making, especially given the challenges of conducting real-world trials for respiratory treatments that evolve over time. Observational health data derived from Electronic Health Records (EHRs) presents a valuable resource. While publicly available de-identified EHR ICU datasets exist, such as HiRID, Salzburg Intensive Care database, AmsterdamUMCdb, and the Medical Information Mart for Intensive Care (MIMIC) [3], existing benchmarks primarily focus on conventional clinical prediction tasks like mortality prediction and length-of-stay estimation [4-6].

To address this gap, we present a temporal benchmark dataset derived from MIMIC-IV (v2.2) for evaluating respiratory intervention tasks. The hourly dataset is enriched with ventilation data and a wide range of other covariates, including demographics, lab results, measurements, illness severity scores, treatment interventions, and outcome variables. It covers 50,920 patients admitted to the ICU, with records collected over 90 days. This dataset can help address weaning delays and failures, optimize strategies for respiratory support, identify efficiencies in clinical practices, provide decision support to attending physicians regarding intubation decisions in the ICU, and facilitate time-series and reinforcement learning applications. It can also be linked to social determinants of health to better understand societal implications and biases in complex healthcare settings [7-11] . 


We introduce  a temporal benchmark for clinical respiratory interventions, a 90-day hourly ventilation dataset derived from MIMIC-IV version 2.2. MIMIC-IV is an open-access, de-identified database, compiled from electronic health records of patients admitted to the ICU or Emergency Department at the Beth Israel Deaconess Medical Center in Boston between 2008 and 2019 [3]. Our temporal dataset includes confounding variables categorized into both time-fixed and time-varying variables, as well as outcome variables. All patient data was extracted using customized scripts from Structured Query Language (SQL) using an object-relational database system.

In the MIMIC-IV database, a patient can have multiple ICU stays over the years or experience transitions between different ICUs during the same hospital admission. To prevent data leakage and maintain data integrity, we choose the first ICU stay with respiratory support for each patient. This approach ensures that data used for modeling  is independent and not influenced by information from subsequent stays. Additionally, patients with a do not resuscitate or do not intubate directives and those who were on invasive ventilation 24 hours before ICU admission are excluded, resulting in a total of 50,920 patients.

The majority of timestamps for time-varying variables in the raw MIMIC data are presented in year, month, day, hour, minute, and second format, offering the potential to derive granular data for comprehensive medical analysis. The sporadic recording of multiple observations allows us to aggregate the data into hourly bins to improve the data density and analytical consistency. Our dataset spans the period of 0 to 2160 hours (equivalent to 90 days) following ICU admission for each subject. 

Data Description

The dataset presented comprises 50,920 distinct adult patients admitted to the ICU of Beth Israel Deaconess Medical Center (Boston, MA, USA) between 2008 and 2019. Static, time-varying, and outcome variables have been extracted from MIMIC-IV and organized into an hourly materialized view. The content for each patient is stored in a *.csv format, named after the patient's unique identifier (subject ID). The directory where the data is stored, is organized into multiple folders, each named according to the first three digits of the subject IDs. Within each folder, CSV files are stored for all subjects whose IDs begin with the corresponding three digits of the folder name.

Patient level static variables

Static parameters extracted for patients, as outlined in Table 1, encompass demographic variables, comorbidity scores that evaluate neurological function (including the Glasgow Coma Scale and its three components: eye opening, verbal and motor responses), along with an assessment of patient organ dysfunction (maximum Sequential Organ Failure Assessment score) performed 24 hours after ICU admission.

Table 1: Patient level static variables
Variable Description
intime ICU admission time
outtime ICU discharge time
gender Patient gender
anchor year Patient shifted year
anchor age Patient age in anchor year
insurance Patient insurance type
language English proficiency indicator
marital status Patient marital status
race Patient race
first_careunit ICU type during first admission
pbw_kg Patient predicted body weight (kg)
height_inch Patient height (inches)
elixhauser_vanwalraven Elixhauser-Van Walraven score
gcs Glasgow Coma Scale (GCS) score
gcs_motor GCS motor response component
gcs_verbal GCS verbal response component
gcs_eyes GCS eye-opening response component
gcs_unable Endotracheal tube indicator
sofa_24_hours Max 24-hour Sequential Organ Failure Assessment (SOFA) score


The time-varying measurements in the data encompass ventilation settings, laboratory results, and vital signs. Ventilation settings and vital signs are extracted from the MIMIC chartevents table, while labs data are obtained from the MIMIC labevents table, each identified by their respective ItemIDs. If there were multiple ItemIDs representing the same measurement with different units, we standardize the units before incorporating them into our data. To handle multiple values within a single hour for a subject, the results are aggregated by computing the median, given that the median exhibits reduced sensitivity to noisy data. The labs are sourced from arterial blood gas (ABG) specimens, as arterial blood measurements are deemed to have greater clinical relevance and precision when evaluating parameters such as respiratory function, acid-base balance, and oxygenation status. Two parameters derived from ventilation settings are also presented: set_pc_draeger (set pressure for pressure-controlled ventilation from the Draeger ventilator) and set_pc (set pressure for pressure-controlled ventilation). Set_pc_draeger is calculated as the difference between the inspiratory pressure from the Draeger ventilator (pinsp_draeger) and the set peak inspiratory pressure (ppeak). Based on clinical knowledge, set_pc is populated with pcv_level (pressure controlled ventilation level) if present, pinsp_hamilton (inspiratory pressure from Hamilton ventilator) if pcv_level is absent, and set_pc_draeger (inspiratory pressure from Draeger ventilator) if both are absent. All variables related to ventilation parameters, vital signs, and labs, and their corresponding descriptions, are described in Table 2 within their respective sections.

Table 2: Measurements Observations.
Variable Description
Ventilation parameters
ppeak Peak Inspiratory pressure (cmH2O)
set_peep1 Set peak inspiratory pressure (cmH2O)
total_peep Total peak inspiratory pressure (cmH2O)
rr Respiratory rate (insp/min)
set_rr1 Set respiratory rate (insp/min)
total_rr Total respiratory rate (insp/min)
set_tv1 Set tidal volume (mL)
total_tv Total tidal volume (mL)
set_fio21 Set fraction of inspired oxygen
set_ie_ratio1 Set inspiratory-to-expiratory ratio
set_pc1 Set pressure for pressure controlled ventilation (cmH2O)
_set_pc_draeger Set pressure from Draeger Ventilator (cmH2O)
_pinsp_draeger2 Inspiratory pressure from Draeger Ventilator (cmH2O)
_pinsp_hamilton2 Inspiratory pressure from Hamilton ventilator (cmH2O)
_pcv_level2 Pressure controlled ventilation level (cmH2O)
calculated_bicarbonate Calculated bicarbonate, whole blood (mEq/L)
so2 Oxygen saturation (%)
pCO2 Partial pressure of carbon dioxide (mmHg)
pO2 Partial pressure of oxygen (mmHg)
pH pH
Vital Signs
heart_rate Heart rate (bpm)
sbp Systolic arterial blood pressure (mmHg)
dbp Diastolic arterial blood pressure (mmHg)
mbp Mean arterial blood pressure (mmHg)
sbp_ni Systolic non-invasive blood pressure (mmHg)
dbp_ni Diastolic non-invasive blood pressure (mmHg)
mbp_ni Mean non-invasive blood pressure (mmHg)
temperature Temperature (°C)
spO2 Oxygen saturation pulse oximetry (%)
glucose Blood glucose

1 "set" in ventilation settings refers to values set by healthcare professionals on the ventilator to suit the patients’ respiratory needs.

2 "_" refers to intermediate variables.

Treatment interventions
Respiratory support interventions

Three respiratory support methods, namely invasive ventilation (INV), non-invasive ventilation (NIV), and high-flow nasal cannula (HFNC), are presented as binary indicators per hour. The curation of these respiratory support variables is verified by clinical experts to ensure accuracy and reliability. 

In  MIMIC, the procedureevents table identifies patients on INV or NIV during their ICU stay, while the chartevents table identifies patients on HFNC. INV and NIV in MIMIC have documented start and end times recorded by respiratory therapists, however, HFNC lacks a corresponding time interval; having only the time at which the measurement was observed.

Therefore, we pre-process the data to establish a start time and end time for each HFNC event per ICU stay. In addition, multiple HFNC events could occur during a single ICU stay. Therefore, if the time gap between two consecutive HFNC events exceeded 24 hours, we treat them as separate events. For each HFNC event, the minimum and maximum time at which the HFNC is applied is used to obtain the HFNC start time and the end time. HFNC events with identical start and end times are excluded.

In cases of overlapping mutually exclusive treatments, where patients were recorded to be on both non-invasive and invasive ventilation simultaneously, we prioritize the most invasive treatment strategy (INV > NIV > HFNC). This occurs due to the complexities involved in transitioning between ventilation therapies within the ICU, which often includes a series of procedures during the transition period. Furthermore, for short intervals (less than 6 hours) recorded between two different treatments, we attribute the gap to the less invasive treatment. This allows us to handle situations where the precise timing of treatments is unclear. 

Additional interventions

Additional binary indicators for interventions include vasopressor administration and continuous renal replacement therapy. Vasopressors are extracted from the MIMIC inputevents table and matched to the corresponding hour using their respective start and end times. A patient is classified as being on vasopressors if they received norepinephrine, epinephrine, dopamine, phenylephrine, or vasopressin. Information regarding continuous renal replacement therapy (CRRT) is extracted from the MIMIC chartevents table. Patients are identified as being on CRRT if they had a positive value for blood flow rate or fluid removal during dialysis.  A summary of all treatment interventions is presented in Table 3. 

Table 3: Treatment interventions
Variable Description
invasive Invasive ventilation indicator
noninvasive Non-invasive ventilation indicator
highflow High-flow nasal cannula indicator
vasopressor Vasopressor treatment indicator
crrt Continuous renal replacement therapy indicator
Outcome Variables

The majority of the outcome variables are recorded as binary markers at each hour, with one denoting the occurrence of the event. These include discharge outcome, ICU out-time outcome, death outcome, sepsis. 

Discharge outcome and ICU out-time outcome indicate if a patient was discharged from the hospital or ICU respectively. The death outcome variable denotes whether a patient died at a specific hour. The date of death in MIMIC was derived from hospital and state records. In cases where both data sources are available, in-hospital mortality is preferentially used over state-linked data. The state-derived date of death included only the date component, so a default time of midnight is used when converting the date to a timestamp.

The data also includes a sepsis outcome variable that identifies whether a patient is septic according to the Sepsis-3 diagnostic criteria. Additionally, it contains the length of stay variable, which indicates the duration of a patient's ICU stay in fractional days.  A summary the outcome variables is presented in Table 4.

Table 4: Outcome variables
Variable Description
discharge_outcome Hospital discharge indicator
icuouttime_outcome ICU discharge indicator
death_outcome Death indicator
sepsis Presence of sepsis using sepsis 3 criteria
los ICU length of stay (fractional days)

Usage Notes

The dataset can be utilized in various studies focusing on respiratory support and ICU practices, addressing issues such as weaning delays and optimizing intubation decisions. This dataset offers considerable reuse potential in multiple areas. Researchers can use it to address weaning delays and failures, optimize respiratory support strategies, identify efficiencies in clinical practices, and provide decision support to attending physicians regarding intubation decisions in the ICU. Additionally, it facilitates time-series analysis and reinforcement learning approaches, aiding in predictive modeling and decision-making processes.

One limitation to consider is the generalizability of the dataset, as the dataset may not fully represent all ICU settings, particularly those with differing protocols or patient populations. Temporal bias is another limitation, as changes in clinical practices over time could impact the relevance of older data within the dataset.


The dataset originates from the MIMIC-IV v2.2 database, which is a de-identified dataset accessed through the PhysioNet Credentialed Health Data Use Agreement (v1.5.0) that we have been granted permission to use.The dataset is a derivative dataset and thus no new patient data was collected. The ethics approval of the dataset follows from that of the parent MIMIC dataset.


L.C., D.M., and L.M. are supported by grant NIH-R01-EB017205 from the National Institute of Health. D.M. and L.M. are supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH-R01-EB030362. D.M. is also supported by NIH National Library of Medicine under 75N97020C00013, and Massachusetts Life Sciences Center. H.C. is supported by the Korea Health Technology Research and Development Project through the Korea Health Industry Development Institute, funded by the Ministry of Health and Welfare, Republic of Korea (grant number: HI21C107409). The authors would like to thank Dr. Kerollos Wanis, for his valuable feedback.

Conflicts of Interest

The authors have no conflicts of interests to declare.


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