standOff. When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote shown on the same row, so the borrowing can be checked against the thing borrowed from without leaving this page. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.| relation to other work | claim_relations | how this paper stands to another: agrees, contradicts, extends, qualifies |
| claim type | claim_type | the shape of the claim: existence, mechanism, method, critique, prediction, null |
| concept edge | concept_edges | an asserted relation between two concepts, with the sentence asserting it |
| concept | concepts | an idea the paper works with, quoted where the paper uses it |
| contribution | contribution | what the paper claims to add |
| limitation | limitations | what the paper says its evidence cannot support |
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| method | method | how the paper produced its evidence |
| object of study | object_of_study | what the paper is about — the thing it investigates |
| phenomenon | phenomenon | the effect or regularity under study |
| research question | research_question | the question the paper sets itself |
| sense | senses | a distinct use of a term, quoted where the paper fixes it |
| unrecordable relation | unhoused_relations | a relation the paper asserts that this vocabulary cannot express, with the reason |
MIMIC-III, a freely accessible critical care database
Abstract
object_of_studylimitationsconcepts↗concepts↗sensesMIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework.
Subject terms: Outcomes research, Health care, Prognosis, Diagnosis, Medical research
Background & Summary
concepts↗In recent years there has been a concerted move towards the adoption of digital health record systems in hospitals. In the US, for example, the number of non-federal acute care hospitals with basic digital systems increased from 9.4 to 75.5% over the 7 year period between 2008 and 2014 ( ref. 1 ).
phenomenonlimitationslimitationsconcepts↗concepts↗concepts↗unhoused_relationssensesDespite 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. In parallel, the scientific research community is increasingly coming under criticism for the lack of reproducibility of studies 2 .
research_questionmechanisms↗concepts↗concepts↗unhoused_relationssensesclaim_relationsclaim_relationsHere we report the release of the MIMIC-III database, an update to the widely-used MIMIC-II database (Data Citation 1). 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 ( Fig. 1 ). The open nature of the data allows clinical studies to be reproduced and improved in ways that would not otherwise be possible.
Overview of the MIMIC-III critical care database.
Based on our experience with the previous major release of MIMIC (MIMIC-II, released in 2010) we anticipate MIMIC-III to be widely used internationally in areas such as academic and industrial research, quality improvement initiatives, and higher education coursework.
concepts↗unhoused_relationssensesclaim_relationsTo recognize the increasingly broad usage of MIMIC, we have renamed the full title of the database from ‘Multiparameter Intelligent Monitoring in Intensive Care’ to ‘Medical Information Mart for Intensive Care’. The MIMIC-III critical care database is unique and notable for the following reasons:
it is the only freely accessible critical care database of its kind;
the dataset spans more than a decade, with detailed information about individual patient care;
analysis is unrestricted once a data use agreement is accepted, enabling clinical research and education around the world.
Patient characteristics
concepts↗concepts↗concepts↗MIMIC-III contains data associated with 53,423 distinct hospital admissions for adult patients (aged 16 years or above) admitted to critical care units between 2001 and 2012. In addition, it contains data for 7870 neonates admitted between 2001 and 2008. The data covers 38,597 distinct adult patients and 49,785 hospital admissions. The median age of adult patients is 65.8 years (Q1–Q3: 52.8–77.8), 55.9% patients are male, and in-hospital mortality is 11.5%. The median length of an ICU stay is 2.1 days (Q1–Q3: 1.2–4.6) and the median length of a hospital stay is 6.9 days (Q1-Q3: 4.1–11.9). A mean of 4579 charted observations (’chartevents’) and 380 laboratory measurements (’labevents’) are available for each hospital admission. Table 1 provides a breakdown of the adult population by care unit.
Details of the MIMIC-III patient population by first critical care unit on hospital admission for patients aged 16 years and above.
The primary International Classification of Diseases (ICD-9) codes from the patient discharges are listed in Table 2 . The top three codes across hospital admissions for patients aged 16 years and above were:
Distribution of primary International Classification of Diseases, 9th Edition (ICD-9) codes by care unit for patients aged 16 years and above.
414.01 (‘Coronary atherosclerosis of native coronary artery’), accounting for 7.1% of all hospital admissions;
038.9 (‘Unspecified septicemia’), accounting for 4.2% of all hospital admissions; and
410.71 (‘Subendocardial infarction, initial episode of care’), accounting for 3.6% of all hospital admissions.
Classes of data
concepts↗Data available in the MIMIC-III database ranges from time-stamped, nurse-verified physiological measurements made at the bedside to free-text interpretations of imaging studies provided by the radiology department. Table 3 gives an overview of the different classes of data available. Figure 2 shows sample data for a single patient stay in a medical intensive care unit. The patient, who was undergoing a course of chemotherapy at the time of admission, presented with febrile neutropenia, anemia, and thrombocytopenia.
Classes of data available in the MIMIC-III critical care database.
Sample data for a single patient stay in a medical intensive care unit. GCS is Glasgow Coma Scale; NIBP is non-invasive blood pressure; and O2 saturation is blood oxygen saturation.
Methods
concepts↗concepts↗claim_relationsconcept_edgesThe Laboratory for Computational Physiology at Massachusetts Institute of Technology is an interdisciplinary team of data scientists and practicing physicians. MIMIC-III is the third iteration of the MIMIC critical care database, enabling us to draw upon prior experience with regard to data management and integration 3 .
Database development
claim_typeThe 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. Data was downloaded from several sources, including:
archives from critical care information systems.
hospital electronic health record databases.
Social Security Administration Death Master File.
concepts↗concepts↗Two different critical care information systems were in place over the data collection period: Philips CareVue Clinical Information System (models M2331A and M1215A; Philips Health-care, Andover, MA) and iMDsoft MetaVision ICU (iMDsoft, Needham, MA). These systems were the source of clinical data such as:
time-stamped nurse-verified physiological measurements (for example, hourly documentation of heart rate, arterial blood pressure, or respiratory rate);
documented progress notes by care providers;
continuous intravenous drip medications and fluid balances.
mechanisms↗limitationsconcepts↗concepts↗concepts↗unhoused_relationsunhoused_relationsconcept_edgesWith exception to data relating to fluid intake, which differed significantly in structure between the CareVue and MetaVision systems, data was merged when building the database tables. Data which could not be merged is given a suffix to denote the data source. For example, inputs for patients monitored with the CareVue system are stored in INPUTEVENTS_CV, whereas inputs for patients monitored with the Metavision system are stored in INPUTEVENTS_MV. Additional information was collected from hospital and laboratory health record systems, including:
patient demographics and in-hospital mortality.
laboratory test results (for example, hematology, chemistry, and microbiology results).
discharge summaries and reports of electrocardiogram and imaging studies.
billing-related information such as International Classification of Disease, 9th Edition (ICD-9) codes, Diagnosis Related Group (DRG) codes, and Current Procedural Terminology (CPT) codes.
limitationsconcepts↗Out-of-hospital mortality dates were obtained using the Social Security Administration Death Master File. A more detailed description of the data is shown in Table 1 . Physiological waveforms obtained from bedside monitors (such as electrocardiograms, blood pressure waveforms, photoplethysmograms, impedance pneumograms) were obtained for a subset of patients.
mechanisms↗concepts↗concepts↗concepts↗concepts↗concepts↗unhoused_relationsunhoused_relationsSeveral projects are ongoing to map concepts within the MIMIC database to standardized dictionaries. For example, researchers at the National Library of Medicine National Institutes of Health have mapped laboratory tests and medications in MIMIC-II to LOINC and RxNorm, respectively 4 . Efforts are also underway to transform MIMIC to common data models, such as the Observational Medical Outcomes Partnership Common Data Model, to support the application of standardized tools and methods 5 . These developments are progressively incorporated into the MIMIC database where possible.
The 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.
Deidentification
method↗mechanisms↗limitationsconcepts↗concepts↗concepts↗unhoused_relationsunhoused_relationssensesBefore data was incorporated into the MIMIC-III database, it was first deidentified in accordance with Health Insurance Portability and Accountability Act (HIPAA) standards using structured data cleansing and date shifting. The deidentification process for structured data required the removal of all eighteen of the identifying data elements listed in HIPAA, including fields such as patient name, telephone number, address, and dates. In particular, dates were shifted into the future by a random offset for each individual patient in a consistent manner to preserve intervals, resulting in stays which occur sometime between the years 2100 and 2200. Time of day, day of the week, and approximate seasonality were conserved during date shifting. Dates of birth for patients aged over 89 were shifted to obscure their true age and comply with HIPAA regulations: these patients appear in the database with ages of over 300 years.
mechanisms↗limitationsconcepts↗concepts↗concepts↗concepts↗unhoused_relationsunhoused_relationsProtected health information was removed from free text fields, such as diagnostic reports and physician notes, using a rigorously evaluated deidentification system based on extensive dictionary look-ups and pattern-matching with regular expressions 6 . The components of this deidentification system are continually expanded as new data is acquired.
Code availability
The code that underpins the MIMIC-III website and documentation is openly available and contributions from the research community are encouraged: https://github.com/MIT-LCP/mimic-website
A Jupyter Notebook containing the code used to generate the tables and descriptive statistics included in this paper is available at: https://github.com/MIT-LCP/mimic-iii-paper/
Data Records
contributionmechanisms↗concepts↗concepts↗concepts↗concepts↗concepts↗unhoused_relationsunhoused_relationsunhoused_relationsunhoused_relationssensesconcept_edgesMIMIC-III is a relational database consisting of 26 tables (Data Citation 1). Tables are linked by identifiers which usually have the suffix ‘ID’. For example, SUBJECT_ID refers to a unique patient, HADM_ID refers to a unique admission to the hospital, and ICUSTAY_ID refers to a unique admission to an intensive care unit.
concepts↗concepts↗Charted events such as notes, laboratory tests, and fluid balance are stored in a series of ‘events’ tables. For example the OUTPUTEVENTS table contains all measurements related to output for a given patient, while the LABEVENTS table contains laboratory test results for a patient.
mechanisms↗concepts↗concepts↗concepts↗unhoused_relationssensesTables prefixed with ‘D_’ are dictionary tables and provide definitions for identifiers. For example, every row of CHARTEVENTS is associated with a single ITEMID which represents the concept measured, but it does not contain the actual name of the measurement. By joining CHARTEVENTS and D_ITEMS on ITEMID, it is possible to identify the concept represented by a given ITEMID. Further detail is provided below.
Data tables
concepts↗concepts↗concepts↗concepts↗unhoused_relationssensesDeveloping the MIMIC data model involved balancing simplicity of interpretation against closeness to ground truth. As such, the model is a reflection of underlying data sources, modified over iterations of the MIMIC database in response to user feedback. Table 4 describes how data is distributed across the data tables. Care has been taken to avoid making assumptions about the underlying data when carrying out transformations, so MIMIC-III closely represents the raw hospital data.
An overview of the data tables comprising the MIMIC-III (v1.3) critical care database.
concepts↗Broadly speaking, five tables are used to define and track patient stays: ADMISSIONS; PATIENTS; ICUSTAYS; SERVICES; and TRANSFERS. Another five tables are dictionaries for cross-referencing codes against their respective definitions: D_CPT; D_ICD_DIAGNOSES; D_ICD_PROCEDURES; D_ITEMS; and D_LABITEMS. The remaining tables contain data associated with patient care, such as physiological measurements, caregiver observations, and billing information.
concepts↗unhoused_relationsclaim_relationsIn some cases it would be possible to merge tables—for example, the D_ICD_PROCEDURES and CPTEVENTS tables both contain detail relating to procedures and could be combined—but our approach is to keep the tables independent for clarity, since the data sources are significantly different. Rather than combining the tables within MIMIC data model, we suggest researchers develop database views and transforms as appropriate.
Technical Validation
claim_relationsThe number of structural changes were minimized to achieve the desired level of deidentification and data schema, helping to ensure that MIMIC-III closely represents the raw data collected within the Beth Israel Deaconess Medical Center.
mechanisms↗concepts↗unhoused_relationsBest practice for scientific computing was followed where possible 7 . Code used to build MIMIC-III was version controlled and developed collaboratively within the laboratory. This approach encouraged and facilitated sharing of readable code and documentation, as well as frequent feedback from colleagues.
mechanisms↗concepts↗unhoused_relationsIssue tracking is used to ensure that limitations of the data and code are clearly documented and are dealt with as appropriate. The research community is encouraged to report and address issues as they are found, and a system for releasing minor database updates is in place.
Usage Notes
Data access
limitationslimitationsconcepts↗claim_relationsMIMIC-III is provided as a collection of comma separated value (CSV) files, along with scripts to help with importing the data into database systems including PostreSQL, MySQL, and MonetDB. As the database contains detailed information regarding the clinical care of patients, it must be treated with appropriate care and respect. Researchers are required to formally request access via a process documented on the MIMIC website 8 . There are two key steps that must be completed before access is granted:
the researcher must complete a recognized course in protecting human research participants that includes Health Insurance Portability and Accountability Act (HIPAA) requirements.
concepts↗the researcher must sign a data use agreement, which outlines appropriate data usage and security standards, and forbids efforts to identify individual patients.
limitationsconcepts↗Approval requires at least a week. Once an application has been approved the researcher will receive emails containing instructions for downloading the database from PhysioNetWorks, a restricted access component of PhysioNet 9 .
Example usage
MIMIC has been used as a basis for coursework in numerous educational institutions, for example in medical analytics courses at Stanford University (course BIOMEDIN215), Massachusetts Institute of Technology (courses HST953 and HST950J/6.872), Georgia Institute of Technology (course CSE8803), University of Texas at Austin (course EE381V), and Columbia University (course G4002), amongst others. MIMIC has also provided the data that underpins a broad range of research studies, which have explored topics such as machine learning approaches for prediction of patient outcomes, clinical implications of blood pressure monitoring techniques, and semantic analysis of unstructured patient notes 10–13 .
mechanisms↗concepts↗sensesA series of 'datathons' have been held alongside development of the MIMIC database. These events assemble caregivers, data scientists, and those with domain-specific knowledge with the aim of creating ideas and producing clinically relevant, reproducible research 14 . In parallel the events introduce new researchers to MIMIC and provide a platform for continuous review and development of code and research.
Documentation for the MIMIC database is available online 8 . The content is under continuous development and includes a list of studies that have been carried out using MIMIC. The website includes functionality that enables the research community to directly submit updates and improvements via GitHub.
Collaborative research
concepts↗concepts↗unhoused_relationssensesclaim_relationsOur experience is that many researchers work independently to produce code for data processing and analysis. We seek to move towards a more collaborative, iterative, and self-checking development process where researchers work together on a shared code base. To facilitate collaboration, a public code repository has been created to encourage researchers to develop and share code collectively: https://github.com/MIT-LCP/mimic-code .
concepts↗concepts↗concepts↗concepts↗The repository has been seeded with code to calculate commonly utilized variables in critical care research, including severity of illness scores, comorbidity scores, and duration of various treatments such as mechanical ventilation and vasopressor use. We encourage users to incorporate this code into their research, provide improvements, and add new contributions that have potential to benefit the research community as a whole. Over time, we expect the repository to become increasingly vital for researchers working with the MIMIC-III database.
concepts↗concepts↗concepts↗unhoused_relationsunhoused_relationsAlongside work on the centralized codebase, we support efforts to transform MIMIC into common data models such the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) 5 . Developing these common models may help to facilitate integration with complementary datasets and to enable the application of generalized analytic tools. Important efforts to map concepts to standardized clinical ontologies are also underway.
Additional Information
How to cite this article: Johnson, A. E. W. et al. MIMIC-III, a freely accessible critical care database. Sci. Data 3:160035 doi: 10.1038/sdata.2016.35 (2016).
Supplementary Material
Acknowledgments
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 Fig. 1.
Footnotes
Data Citations
References
Associated Data
Data Citations
Supplementary Materials
Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; 3 of 3 relations here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.
This record holds no controls. A layer is empty when the tagger recorded nothing in it — which is not the same as the paper having nothing to record.
| facet | tag | field | conf. | whose words |
|---|---|---|---|---|
| object_of_study | the structure, provenance, privacy processing, access model, and research uses of the MIMIC-III critical-care database | object_of_study | the paper's words | |
| research_question | how a comprehensive deidentified critical-care database can be assembled, validated, documented, and made broadly usable for reproducible research | research_question | the paper's words | |
| contribution | releases and documents MIMIC-III, a linked twenty-six-table clinical database with deidentification, controlled access, validation, and collaborative code infrastructure | contribution | the paper's words | |
| claim_type | method | claim_type | the paper's words | |
| phenomenon | the unrealized research value of fragmented hospital data and the reproducibility benefits of accessible integrated clinical data | phenomenon | the paper's words | |
| method | system-or-tool-building also in: system-or-tool-building | method | the paper's words | |
| mechanisms | mechanisms | mechanisms[0] | 0.99 | the tagger's words |
| mechanisms | mechanisms | mechanisms[1] | 0.99 | the tagger's words |
| mechanisms | mechanisms | mechanisms[2] | 0.98 | the tagger's words |
| mechanisms | mechanisms | mechanisms[3] | 0.99 | the tagger's words |
| mechanisms | mechanisms | mechanisms[4] | 0.99 | the tagger's words |
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| mechanisms | mechanisms | mechanisms[8] | 0.98 | the tagger's words |
| mechanisms | mechanisms | mechanisms[9] | 0.98 | the tagger's words |
| limitations | the database is single-center, so its institutional coverage is narrower than a multi-center resource | limitations[0] | 0.99 | the tagger's words |
| limitations | digital-system interoperability remains unresolved and impedes integration of hospital data | limitations[1] | 0.99 | the tagger's words |
| limitations | the potential of hospital data for understanding and improving care had not yet been fully realized | limitations[2] | 0.99 | the tagger's words |
| limitations | fluid-intake records from CareVue and MetaVision differ enough in structure that they remain source-specific | limitations[3] | 0.99 | the tagger's words |
| limitations | physiological waveforms are available for only a subset of patients | limitations[4] | 0.99 | the tagger's words |
| limitations | deidentification changes calendar dates and obscures ages over eighty-nine, requiring careful interpretation | limitations[5] | 0.99 | the tagger's words |
| limitations | the free-text deidentification system requires continuing expansion as new data arrives | limitations[6] | 0.98 | the tagger's words |
| limitations | access is restricted by required human-subjects training and a signed data-use agreement | limitations[7] | 0.99 | the tagger's words |
| limitations | approval takes at least a week and download occurs through a restricted PhysioNet component | limitations[8] | 0.99 | the tagger's words |
| limitations | MIMIC-III data must be handled with appropriate care because it contains detailed clinical information | limitations[9] | 0.99 | the tagger's words |
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| unhoused_relations | interoperability → data integration Unresolved interoperability creates data-integration challenges. | unhoused_relations[0] | 0.99 | the tagger's words |
| unhoused_relations | MIMIC-III → MIMIC-II MIMIC-III updates the widely used MIMIC-II database. | unhoused_relations[1] | 0.99 | the tagger's words |
| unhoused_relations | Medical Information Mart for Intensive Care → MIMIC-III The expanded title of MIMIC was changed to Medical Information Mart for Intensive Care. | unhoused_relations[2] | 0.99 | the tagger's words |
| unhoused_relations | simplicity of interpretation → closeness to ground truth The data model balances simplicity of interpretation against closeness to ground truth. | unhoused_relations[3] | 0.99 | the tagger's words |
| unhoused_relations | SUBJECT_ID → adult patients SUBJECT_ID refers to a unique patient represented in MIMIC-III. | unhoused_relations[4] | 0.99 | the tagger's words |
| unhoused_relations | HADM_ID → hospital admissions HADM_ID refers to a unique hospital admission. | unhoused_relations[5] | 0.99 | the tagger's words |
| unhoused_relations | ICUSTAY_ID → hospital admissions ICUSTAY_ID refers to a unique intensive-care-unit admission. | unhoused_relations[6] | 0.98 | the tagger's words |
| unhoused_relations | CareVue Clinical Information System → INPUTEVENTS_CV CareVue input events are stored in INPUTEVENTS_CV. | unhoused_relations[7] | 0.99 | the tagger's words |
| unhoused_relations | MetaVision ICU → INPUTEVENTS_MV MetaVision input events are stored in INPUTEVENTS_MV. | unhoused_relations[8] | 0.99 | the tagger's words |
| unhoused_relations | LOINC → laboratory measurements MIMIC laboratory tests were mapped to LOINC. | unhoused_relations[9] | 0.99 | the tagger's words |
| unhoused_relations | RxNorm → MIMIC-III MIMIC medications were mapped to RxNorm. | unhoused_relations[10] | 0.99 | the tagger's words |
| unhoused_relations | structured data cleansing → protected health information Structured cleansing removes HIPAA identifying elements from clinical data. | unhoused_relations[11] | 0.99 | the tagger's words |
| unhoused_relations | date shifting → ground truth Date shifting preserves intervals while intentionally changing calendar dates. | unhoused_relations[12] | 0.98 | the tagger's words |
| unhoused_relations | dictionary look-ups → protected health information Dictionary look-ups help remove protected health information from free text. | unhoused_relations[13] | 0.99 | the tagger's words |
| unhoused_relations | pattern-matching → protected health information Regular-expression pattern matching helps remove protected health information from free text. | unhoused_relations[14] | 0.99 | the tagger's words |
| unhoused_relations | SUBJECT_ID → 26 tables Identifiers such as SUBJECT_ID link tables in the relational database. | unhoused_relations[15] | 0.99 | the tagger's words |
| unhoused_relations | CHARTEVENTS → D_ITEMS Joining CHARTEVENTS to D_ITEMS on ITEMID reveals the name of the measured concept. | unhoused_relations[16] | 0.99 | the tagger's words |
| unhoused_relations | database views → INPUTEVENTS_CV Researchers can combine independent source tables through database views and transforms. | unhoused_relations[17] | 0.98 | the tagger's words |
| unhoused_relations | version controlled → reproducibility Version-controlled collaborative development facilitates readable shared code and feedback. | unhoused_relations[18] | 0.98 | the tagger's words |
| unhoused_relations | Issue tracking → data integration Issue tracking documents and manages limitations in the data and code. | unhoused_relations[19] | 0.98 | the tagger's words |
| unhoused_relations | public code repository → shared code base The public repository enables researchers to develop and share code collectively. | unhoused_relations[20] | 0.99 | the tagger's words |
| unhoused_relations | common data models → integration with complementary datasets Common models may facilitate integration with complementary datasets. | unhoused_relations[21] | 0.99 | the tagger's words |
| unhoused_relations | common data models → generalized analytic tools Common models may enable application of generalized analytic tools. | unhoused_relations[22] | 0.99 | the tagger's words |
| senses | MIMIC-III - a large single-center database integrating deidentified clinical data from critical-care admissions | senses[0] | 0.99 | the tagger's words |
| senses | Medical Information Mart for Intensive Care - the renamed full title chosen to reflect MIMIC's increasingly broad use | senses[1] | 0.99 | the tagger's words |
| senses | interoperability - the unresolved capacity of hospital digital systems to support integration across sources | senses[2] | 0.98 | the tagger's words |
| senses | reproducibility - the ability to repeat and improve clinical studies using openly available underlying data | senses[3] | 0.99 | the tagger's words |
| senses | date shifting - a consistent random offset per patient that obscures dates while preserving clinical intervals | senses[4] | 0.99 | the tagger's words |
| senses | relational database - twenty-six tables linked by patient, hospital-admission, and ICU-stay identifiers | senses[5] | 0.99 | the tagger's words |
| senses | dictionary tables - cross-reference tables that turn identifiers such as ITEMID into named clinical concepts | senses[6] | 0.99 | the tagger's words |
| senses | data model - a deliberately minimally transformed representation balancing interpretation against ground-truth fidelity | senses[7] | 0.99 | the tagger's words |
| senses | datathons - cross-disciplinary events for producing clinically relevant reproducible research and onboarding new users | senses[8] | 0.98 | the tagger's words |
| senses | shared code base - a collaborative, iterative, self-checking alternative to independent one-off analysis code | senses[9] | 0.99 | the tagger's words |
| claim_relations | introduces → the international release of the deidentified MIMIC-III clinical database note: The paper explicitly reports the release. | claim_relations[0] | 0.99 | the tagger's words |
| claim_relations | extends → MIMIC-II with a third, broader major database release note: MIMIC-III is stated to be the third iteration built from prior experience. | claim_relations[1] | 0.99 | the tagger's words |
| claim_relations | supports → reproducible and improvable clinical studies through accessible underlying data note: The stated rationale for open data. | claim_relations[2] | 0.99 | the tagger's words |
| claim_relations | qualifies → the claim that analysis is unrestricted note: Unrestricted analysis begins only once a data-use agreement is accepted. | claim_relations[3] | 0.99 | the tagger's words |
| claim_relations | validates → fidelity of MIMIC-III to raw hospital data note: Minimal structural change is the stated technical-validation strategy. | claim_relations[4] | 0.98 | the tagger's words |
| claim_relations | recommends → keeping structurally different source tables independent and constructing research-specific views note: The paper states this design recommendation directly. | claim_relations[5] | 0.99 | the tagger's words |
| claim_relations | supports → collaborative, iterative, and self-checking analysis-code development note: The public repository is presented as infrastructure for this process. | claim_relations[6] | 0.99 | the tagger's words |
| claim_relations | qualifies → freely accessible critical-care data note: Access requires training and a signed data-use agreement. | claim_relations[7] | 0.99 | the tagger's words |
| concept_edges | MIMIC-III —is-a→ relational database the relation itself is separately evidenced · strength of the relation: 1 · note: Direct categorical statement. · the relation stated: MIMIC-III is a relational database. also in: MIMIC-III · relational database | concept_edges[0] | 0.99 | the tagger's words |
| concept_edges | MIMIC-III —is-a→ MIMIC critical care database the relation itself is separately evidenced · strength of the relation: 1 · note: Direct lineage classification. · the relation stated: MIMIC-III is the third iteration of the MIMIC critical care database. also in: MIMIC-III · MIMIC critical care database | concept_edges[1] | 0.99 | the tagger's words |
| concept_edges | INPUTEVENTS_CV —contrasted-with→ INPUTEVENTS_MV the relation itself is separately evidenced · strength of the relation: 0.95 · note: Explicit whereas contrast tied to distinct source structures. · the relation stated: CareVue input events are stored in INPUTEVENTS_CV, whereas MetaVision input events are stored in INPUTEVENTS_MV. also in: INPUTEVENTS_CV · INPUTEVENTS_MV | concept_edges[2] | 0.99 | the tagger's words |
Tags that could not be placed in the text:
What was deliberately not tagged, and why, is recorded per paper by the tagger. Where a paper has no such record, this page says so rather than implying that nothing was declined.