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Electronic Health Records: Towards Digital Twins in Healthcare

T0 review · 2 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This chapter argues that MIMIC-III made critical-care data broadly accessible, and that the database's identifier structure means query choices materially change research results.

desk verdict Useful MIMIC-III tutorial for newcomers, but the central counting error needs fixing before I'd trust it with students. read the letter →

arxiv 2501.09640 v2 pith:IZTC4BFW submitted 2025-01-16 cs.AI

Muhammet Alkan , Hester Huijsdens , Yola Jones , Fani Deligianni This is my paper · ORCID
classification cs.AI
keywords EHRICDMIMIC-IIIdescriptivestatisticspredictiveanalyticsdigitaltwinsrelationaldatabasecriticalcare
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Electronic health records moved from paper to searchable databases, and this chapter argues that the MIMIC-III intensive-care database is the clearest proof of what that shift makes possible: a rich, real-world clinical dataset any qualified researcher can use. The chapter's central practical claim is that MIMIC-III is not one flat table but a relational database in which patient identifiers, hospital admission identifiers, and ICU-stay identifiers each answer a different question, so different querying approaches produce subtly different patient counts and outcome estimates. It demonstrates this with worked SQL queries for descriptive statistics, mortality, length of stay, vital signs, and ICD-9 coding, and uses a catheterization study to show how careful cohort selection changes conclusions. The importance is that if the authors are right, schema literacy is a prerequisite for trustworthy EHR research and for the eventual move toward predictive analytics and digital twins.

What carries the argument

The carrying mechanism is the MIMIC-III relational schema, centered on three identifiers: `subject_id` for a unique patient, `hadm_id` for a unique hospital admission, and `icustay_id` for a unique ICU stay. The chapter's argument is that these identifiers sit at different levels of granularity—one patient can have several admissions, and one admission can have several ICU stays—so counts and outcome measures computed from one identifier do not automatically transfer to another. Around this core sit the `patients`, `admissions`, and `icustays` tables, with dictionary tables that decode chart, input, output, and lab events, and with the ICD-9 code tables that attach diagnoses to admissions.

What would settle it

Re-run the chapter's counting queries on the same MIMIC-III release by selecting distinct values from `subject_id`, `hadm_id`, and `icustay_id` respectively; if the three counts match, the chapter's central warning about query-sensitive results is empty rather than instructive, and if the age-filtered admission count in Section 3.3 exceeds the total in Figure 9, that specific statistic is false.

Watch

Extended reading notes

Core claim

The central claim is that MIMIC-III has opened up access to high-quality critical-care data, and that this access comes with a catch: the database's architecture determines the answer. Counting distinct patients, distinct hospital admissions, and distinct ICU stays yields different totals, and mortality estimates differ depending on whether death is measured in the ICU, in the hospital, or over a longer window after admission. The chapter shows these differences with reproducible queries and charts, presenting the MIMIC-III schema as the key instrument that links raw clinical events to research-grade conclusions. Along the way it treats ICD-9 codes as the clinical vocabulary that lets researchers group patients by disease, and it presents the progression from descriptive statistics through predictive models to digital twins as the direction in which EHR research is moving.

Load-bearing premise

The chapter's usefulness rests on the factual accuracy of its MIMIC-III statistics, and that accuracy is internally inconsistent: Section 3.3 reports over 53,000 hospital admissions for patients aged 60 and above, while Figures 9 and 10 report 49,785 hospital admissions and 53,423 ICU stays.

Editorial extensions

If this is right

  • Researchers should report which identifier they counted and which mortality window they used whenever they publish MIMIC-III results, because the same database can support multiple defensible answers.
  • Reproducible cohort definitions, like the catheterization study's stepwise SQL pipeline, become as important as statistical models for making EHR research trustworthy.
  • Mortality and length-of-stay models built on MIMIC-III should be evaluated against the specific outcome definition they target, since ICU, hospital, and longer-term mortality behave differently.
  • The path to predictive analytics and digital twins in healthcare depends on solving the data-linkage and schema-understanding problems demonstrated here, not just on adding more data.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same identifier-hierarchy problem is likely present in other large EHR databases, so the chapter's tutorial could be read as a general checklist for schema-aware reporting rather than a MIMIC-III-specific guide.
  • If published MIMIC-III studies rarely specify their counting semantics, a portion of apparent disagreements in the literature may be artifacts of query choice rather than genuine clinical differences.
  • A simple testable extension would be a benchmark task where researchers are given the chapter's clinical questions and asked to report their query definitions; the spread of answers would quantify how much architecture literacy matters.
  • The chapter's own statistical inconsistency (Section 3.3 versus Figures 9 and 10) illustrates the same point it argues: numbers from EHR databases need to be traced back to the exact query that produced them.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 6 minor

Summary. This preprint is an expository chapter on electronic health records and digital twins, with a detailed tutorial on the MIMIC-III critical care database. It reviews the transition to EHRs in the UK and USA, the ICD-9/ICD-10 coding systems, MIMIC-III's relational schema, descriptive statistics queries, mortality and length-of-stay outcomes, vital sign extraction, ICD-9 code distribution, clinical concepts, and a catheterization mortality case study, before discussing the path from descriptive to prescriptive analytics and digital twins. The central pedagogical claim is that correct analysis of MIMIC-III requires understanding the schema because different identifiers (patients, hospital admissions, ICU stays) yield different counts.

Significance. If the factual issues are corrected, this chapter would be a useful pedagogical resource for researchers entering critical-care data analytics. Its strengths are the clear entity-relationship exposition, the emphasis on identifier semantics and the consequences of query choices, the practical SQL query examples (presented as figures), and the accessible treatment of ICD coding and clinical concepts. The chapter does not present a new method or derivation, and it does not ship code or machine-checked proofs; its value is tutorial rather than research.

major comments (2)
  1. [3.3] The sentence 'The MIMIC-III database contains over 53,000 distinct hospital admissions for patients aged 60 years and above' is internally inconsistent with the chapter's own Figures 9 and 10, which report 49,785 hospital admissions and 53,423 ICU stays, and it conflicts with published MIMIC-III v1.4 counts. The number 53,423 is the total count of ICU stays, not hospital admissions, and the age restriction is unsupported by the cited figures. Because the chapter's central lesson is that conflating hospital admission and ICU stay identifiers produces different results, this error reproduces the very mistake the text warns against and should be corrected (e.g., to 'the database contains 49,785 hospital admissions and 53,423 ICU stays; the later case studies often restrict to patients aged 60 and above').
  2. [3.5.1] The ICU mortality paragraph states that the query 'focuses on patients older than 60 years' but then reports 'total ICU mortality is about 8.5%, corresponding to approximately 4,565 patients.' Since 8.5% of 53,423 is about 4,541, the reported mortality count matches the all-adult ICU population rather than a >60-year-old subset. The authors should either provide the cohort size for the age-restricted query or clarify that the 8.5% figure is for all adults; as written, the age filter and the reported numbers are inconsistent and undermine the schema-semantics lesson of the chapter.
minor comments (6)
  1. [2.3.1] The statement that the ICD-9 coding system 'consists of a five-digit structure' is an oversimplification; ICD-9-CM codes have 3 to 5 digits, and the text should acknowledge this variability.
  2. [3.2] The citation 'cite [Moody, 2009]' appears as a raw LaTeX command; it should be formatted as a proper citation.
  3. [Figure 3] The figure contains typos: 'HIPPA' should be 'HIPAA' and 'MyHeathED' should be 'MyHealthED'.
  4. [References] The reference list contains 'V oigt' (with a space) for 'Voigt'; this should be corrected.
  5. [3.3] The phrase 'The MIMIC-III is a relational database comprises 26 tables' is ungrammatical; it should be 'The MIMIC-III relational database comprises 26 tables'.
  6. [3.4] The table name 'Patients' is sometimes written as 'Patient' (e.g., 'the Patient and Icustays tables'); for consistency, use 'patients' throughout.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the chapter is expository and contains no derivation or prediction that reduces to its inputs.

full rationale

This manuscript is a survey/tutorial chapter. It does not derive a new result, fit a model, or make a prediction that is then compared with data. Its central claims are descriptive: that EHR systems evolved, that ICD coding changed from ICD-9 to ICD-10, that MIMIC-III is a valuable public database, and that querying it requires understanding its schema. None of these claims is established by a derivation chain from assumptions, and none is justified by a self-citation that carries the argument. The chapter's factual account of MIMIC-III can be checked against the database itself and against publicly documented counts, and the apparent mismatch between the Section 3.3 statement about 'over 53,000 distinct hospital admissions for patients aged 60 years and above' and the chapter's own Figures 9 and 10 (49,785 hospital admissions and 53,423 ICU stays) is a factual consistency issue, not a circularity issue. A factual error does not make an argument circular because there is no argument that reduces to its own conclusion. The chapter also relies on external sources for historical facts and for the MIMIC-III study example, but those citations are ordinary scholarly references rather than load-bearing self-citations that define the outcome. Therefore the circularity burden is not applicable and the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The chapter is a review, so it introduces no free parameters or invented entities. Its claims rest on the accuracy of cited database statistics and quoted SQL code, and two of those premises are not independently verified: the uniqueness of MIMIC-III and the internal consistency of the patient-count claims.

assumptions (3)
  • domain assumption MIMIC-III is the most comprehensive critical care database freely available to researchers worldwide.
    Stated in the abstract and Sections 3.1 and 3.2 without a comparative evaluation against other freely available critical care databases such as eICU and MIMIC-IV, which weakens the uniqueness claim.
  • domain assumption The database statistics reported in the chapter are accurate, including the claim in Section 3.3 of over 53,000 distinct hospital admissions for patients aged 60 and above.
    The chapter's own Figures 9 and 10 report 49,785 hospital admissions and 53,423 ICU stays, so this assertion is internally inconsistent and cannot be treated as a verified premise.
  • domain assumption The SQL queries shown in Figures 23 to 30 faithfully reproduce the patient selection pipeline of Hsu et al. 2015.
    The queries are presented as screenshots with no executable code or output verification, so a reader must trust the transcription, and no error checks are provided.

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Cite this review

Pith. "Pith review of Electronic Health Records: Towards Digital Twins in Healthcare." pith.science (2026). https://pith.science/paper/IZTC4BFW

@misc{pith2026250109640,
  author       = {Pith},
  title        = {Pith review of: Electronic Health Records: Towards Digital Twins in Healthcare},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IZTC4BFW}},
  note         = {Machine review of arXiv:2501.09640}
}
read the original abstract

The pivotal shift from traditional paper-based records to sophisticated Electronic Health Records (EHR), enabled systematic collection and analysis of patient data through descriptive statistics, providing insight into patterns and trends across patient populations. This evolution continued toward predictive analytics, allowing healthcare providers to anticipate patient outcomes and potential complications before they occur. This progression from basic digital record-keeping to sophisticated predictive modelling and digital twins reflects healthcare's broader evolution toward more integrated, patient-centred approaches that combine data-driven insights with personalized care delivery. This chapter explores the evolution and significance of healthcare information systems, beginning with an examination of the implementation of EHR in the UK and the USA. It provides a comprehensive overview of the International Classification of Diseases (ICD) system, tracing its development from ICD-9 to ICD-10. Central to this discussion is the MIMIC-III database, a landmark achievement in healthcare data sharing and arguably the most comprehensive critical care database freely available to researchers worldwide. MIMIC-III has democratized access to high-quality healthcare data, enabling unprecedented opportunities for research and analysis. The chapter examines its structure, clinical outcome analysis capabilities, and practical applications through case studies, with a particular focus on mortality and length of stay metrics, vital signs extraction, and ICD coding. Through detailed entity-relationship diagrams and practical examples, the text illustrates MIMIC's complex data structure and demonstrates how different querying approaches can lead to subtly different results, emphasizing the critical importance of understanding the database's architecture for accurate data extraction.

Figures

Figures reproduced from arXiv: 2501.09640 by the authors.

Figure 1
Figure 1. Patient’s Timeline 1.2 The transition to Electronic Health Records in the UK and USA The shift from paper-based to Electronic Health Records (EHRs) has profoundly influenced healthcare data management, enhanced the quality of clinical decision making and opened new avenues for biomedical research. It also signaled a transition from independent records to integrated databases. An overview of the adoption of EHRs in t… view at source ↗
Figure 2
Figure 2. The transition to EHRs in UK The Patient Protection and Affordable Care Act in 2010 and the US Food and Drug Administration Safety and Innovation Act in 2012 further advanced public health, stakeholder engagement, and the safety of the drug supply chain. On the other hand, the UK initiated the National Program for IT in 2002, aiming for digitization of health records and paperless operations by 2020, but faced chall… view at source ↗
Figure 3
Figure 3. The transition to EHRs in USA 1.3 Clinical Registries and Databases in Healthcare Clinical registries play a crucial role in modern healthcare systems by serving as comprehensive repositories of patient data focused on specific conditions or diseases. These sophisticated systems are designed to collect, store, and analyze 3 [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (31 more)
Figure 4
Figure 4. Figure 4: Evolution of the International Classification of Diseases System [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: ICD09 and ICD10 Chapters The ICD system’s evolution reflects the ongoing effort to create a comprehensive, flexible, and internationally accepted classification of diseases. Its widespread adoption has made it an indispensable tool for comparing health statistics acros…
Figure 6
Figure 6. Figure 6: ICD-9 VS ICD-10 One crucial aspect to understand is that there is not always a one-to-one mapping between ICD-9 and ICD-10 codes, [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: MIMIC III as a relational database - Tables [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: MIMIC-III: Distinct Patients’ Hospitalisations [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]
Figure 9
Figure 9. Figure 9: MIMIC-III: Hospital Admissions 11 [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]
Figure 10
Figure 10. Figure 10: MIMIC-III: Distinct ICU Admissions [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]
Figure 11
Figure 11. Figure 11: MIMIC-III: ICU Mortality as a clinical outcome [PITH_FULL_IMAGE:figures/full_fig_p012_11.png]
Figure 12
Figure 12. Figure 12: MIMIC-III: Hospital Mortality as a clinical outcome [PITH_FULL_IMAGE:figures/full_fig_p013_12.png]
Figure 13
Figure 13. Figure 13: MIMIC-III: Hospitalisation length as a clinical outcome [PITH_FULL_IMAGE:figures/full_fig_p013_13.png]
Figure 18
Figure 18. Figure 18: These visualizations allow clinicians to understand a patient’s progress in the intensive care unit over time. They provide a holistic view of the patient’s condition, combining various data points to create a comprehensive picture of the patient’s health status and t…
Figure 14
Figure 14. Figure 14: MIMIC-III: Charted Events for a Single Patient [PITH_FULL_IMAGE:figures/full_fig_p015_14.png]
Figure 15
Figure 15. Figure 15: MIMIC-III: Output Events of ICU Stay for a Single Patient [PITH_FULL_IMAGE:figures/full_fig_p015_15.png]
Figure 16
Figure 16. Figure 16: MIMIC-III: Medication intakes during an ICU stay for a single patient [PITH_FULL_IMAGE:figures/full_fig_p016_16.png]
Figure 17
Figure 17. Figure 17: MIMIC-III: Lab events during an ICU stay for a single patient [PITH_FULL_IMAGE:figures/full_fig_p016_17.png]
Figure 18
Figure 18. Figure 18: MIMIC-III: Vital signs during an ICU stay for a single patient [PITH_FULL_IMAGE:figures/full_fig_p017_18.png]
Figure 20
Figure 20. Figure 20: Visualizing these results across different ICU types reveals interesting patterns, [PITH_FULL_IMAGE:figures/full_fig_p017_20.png]
Figure 19
Figure 19. Figure 19: MIMIC-III: ICD-9 Code Queries in MIMIC-III [PITH_FULL_IMAGE:figures/full_fig_p018_19.png]
Figure 20
Figure 20. Figure 20: MIMIC-III: Extracting the distribution of ICD-9 Codes across ICUs [PITH_FULL_IMAGE:figures/full_fig_p018_20.png]
Figure 21
Figure 21. Figure 21: MIMIC-III: The distribution of ICD-9 Codes across ICUs [PITH_FULL_IMAGE:figures/full_fig_p019_21.png]
Figure 22
Figure 22. Figure 22: The study focused on patients requiring mechanical ventilation within 24 hours of ICU admission. To [PITH_FULL_IMAGE:figures/full_fig_p020_22.png]
Figure 22
Figure 22. Figure 22: MIMIC-III: Flowchart of Patient Inclusion and Exclusion Criteria [PITH_FULL_IMAGE:figures/full_fig_p021_22.png]
Figure 23
Figure 23. Figure 23: MIMIC-III: Query to Extract All Patients at ICU and all administration records [PITH_FULL_IMAGE:figures/full_fig_p021_23.png]
Figure 24
Figure 24. Figure 24: MIMIC-III: Query to exclude re-admissions [PITH_FULL_IMAGE:figures/full_fig_p022_24.png]
Figure 25
Figure 25. Figure 25: MIMIC-III: Query to include all patients with ventilation [PITH_FULL_IMAGE:figures/full_fig_p023_25.png]
Figure 26
Figure 26. Figure 26: MIMIC-III: Exclude patients with Sepsis 4 From Descriptive Analytics to Digital Twins: Advancing Healthcare Information Systems 4.1 From Descriptive Analytics to Prescriptive Analytics We can conceptualize healthcare information retrieval processes as a pathway from d…
Figure 27
Figure 27. Figure 27: MIMIC-III: Exclude patients with Vasopressors [PITH_FULL_IMAGE:figures/full_fig_p024_27.png]
Figure 28
Figure 28. Figure 28: MIMIC-III: Extract patients with IAC placed [PITH_FULL_IMAGE:figures/full_fig_p024_28.png]
Figure 29
Figure 29. Figure 29: MIMIC-III: Exclude patients in CSRU or CCU [PITH_FULL_IMAGE:figures/full_fig_p025_29.png]
Figure 30
Figure 30. Figure 30: MIMIC-III: Divide Cohort 4.2 Digital Twins Patients increasingly expect seamless management of their information across their healthcare providers. Currently, traditional healthcare models rely on disconnected systems, multiple sources of information. The new digital …
Figure 31
Figure 31. Figure 31: Digital Twins in Healthcare of patient centred disease preventive systems along with clinical decision support without increasing the burden on healthcare providers [Voigt et al., 2021, Drummond et al., 2023]. For example, telemonitoring devices and sensing technologi…

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Works this paper leans on

28 extracted references · 26 canonical work pages

  1. [1]

    Migrating to electronic health record systems: a comparative study between the united states and the united kingdom

    Karen Wilson and Lara Khansa. Migrating to electronic health record systems: a comparative study between the united states and the united kingdom. Health Policy, 122 0 (11): 0 1232--1239, 2018

    work page 2018

  2. [2]

    Ethical issues in electronic health records: A general overview

    Fouzia F Ozair, Nayer Jamshed, Amit Sharma, and Praveen Aggarwal. Ethical issues in electronic health records: A general overview. Perspectives in clinical research, 6 0 (2): 0 73--76, 2015

    work page 2015

  3. [3]

    Standardized electronic health record data modeling and persistence: A comparative review

    Aya Gamal, Sherif Barakat, and Amira Rezk. Standardized electronic health record data modeling and persistence: A comparative review. Journal of biomedical informatics, 114: 0 103670, 2021

    work page 2021

  4. [4]

    Concor, an initiative towards a national registry and dna-bank of patients with congenital heart disease in the netherlands: rationale, design, and first results

    ET Vander Velde, JWJ Vriend, MMAM Mannens, CSPM Uiterwaal, R Brand, and Barbara JM Mulder. Concor, an initiative towards a national registry and dna-bank of patients with congenital heart disease in the netherlands: rationale, design, and first results. European journal of epidemiology, 20: 0 549--557, 2005

    work page 2005

  5. [5]

    National quality register of congenital heart diseases--can we trust the data? Journal of Congenital Cardiology, 1: 0 1--8, 2017

    Amanda Bodell, Gudrun Bj \"o rkhem, Ulf Thil \'e n, and Estelle Naumburg. National quality register of congenital heart diseases--can we trust the data? Journal of Congenital Cardiology, 1: 0 1--8, 2017

    work page 2017

  6. [6]

    Creating the belgian congenital heart disease database combining administrative and clinical data (belcodac): rationale, design and methodology

    Fouke Ombelet, Eva Goossens, Ruben Willems, Lieven Annemans, Werner Budts, Julie De Backer, Katya De Groote, St \'e phane Moniotte, Liesbet Van Bulck, Ariane Marelli, et al. Creating the belgian congenital heart disease database combining administrative and clinical data (belcodac): rationale, design and methodology. International journal of cardiology, 3...

    work page 2020

  7. [7]

    Terminologies in the world health organisation family of international classifications (who-fic)

    Nicholas R Hardiker, Ann-Helene Almborg, Lynn Bracewell, Christopher Chute, Coen van Gool, and Cassandra Linton. Terminologies in the world health organisation family of international classifications (who-fic)

    work page

  8. [8]

    Classification of diseases, functioning, and disability, 2024

    Centers for Disease Control and Prevention . Classification of diseases, functioning, and disability, 2024. URL https://www.cdc.gov/nchs/icd/index.html. [Online; accessed 9-September-2024]

    work page 2024

Show all 28 references
  1. [9]

    International classification of diseases

    Sudha R Raminani. International classification of diseases. Encyclopaedia Britannica, 2015

    2015

  2. [10]

    Bills of mortality: tracking disease in early modern london

    Niall Boyce. Bills of mortality: tracking disease in early modern london. The Lancet, 395 0 (10231): 0 1186--1187, 2020

    2020

  3. [11]

    History of the statistical classification of diseases and causes of death

    Iwao Milton Moriyama, Ruth M Loy, Alastair Hamish Tearloch Robb-Smith, Harry Michael Rosenberg, and Donna L Hoyert. History of the statistical classification of diseases and causes of death. 2011

    2011

  4. [12]

    Icd-11, 2019

    The Lancet. Icd-11, 2019

    2019

  5. [13]

    International Statistical Classification of Diseases and related health problems: Alphabetical index, volume 3

    World Health Organization. International Statistical Classification of Diseases and related health problems: Alphabetical index, volume 3. World Health Organization, 2004

    2004

  6. [14]

    The mimic code repository: enabling reproducibility in critical care research

    Alistair EW Johnson, David J Stone, Leo A Celi, and Tom J Pollard. The mimic code repository: enabling reproducibility in critical care research. Journal of the American Medical Informatics Association, 25 0 (1): 0 32--39, 2018

    2018

  7. [15]

    big data

    Lucas Bulgarelli, Rodrigo Oct \'a vio Deliberato, and Alistair EW Johnson. Prediction on critically ill patients: the role of “big data”. Journal of critical care, 60: 0 64--68, 2020

    2020

  8. [16]

    Mortality prediction and acuity assessment in critical care

    A Johnson. Mortality prediction and acuity assessment in critical care. PhD thesis, University of Oxford, 2014

    2014

  9. [17]

    Mimic ii: a massive temporal icu patient database to support research in intelligent patient monitoring

    Mohammed Saeed, Christine Lieu, Greg Raber, and Roger G Mark. Mimic ii: a massive temporal icu patient database to support research in intelligent patient monitoring. In Computers in cardiology, pages 641--644. IEEE, 2002

    2002

  10. [18]

    Multiparameter intelligent monitoring in intensive care ii: a public-access intensive care unit database

    Mohammed Saeed, Mauricio Villarroel, Andrew T Reisner, Gari Clifford, Li-Wei Lehman, George Moody, Thomas Heldt, Tin H Kyaw, Benjamin Moody, and Roger G Mark. Multiparameter intelligent monitoring in intensive care ii: a public-access intensive care unit database. Critical car...

    2011

  11. [19]

    Mimic-iii, a freely accessible critical care database

    Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. Mimic-iii, a freely accessible critical care database. Scientific data, 3 0 (1): 0 1--9, 2016

    2016

  12. [20]

    Physionet: Research resource for complex physiologic signals

    George B Moody. Physionet: Research resource for complex physiologic signals. Jpn. J. Electrocardiol, 29: 0 1--3, 2009

    2009

  13. [21]

    The association between indwelling arterial catheters and mortality in hemodynamically stable patients with respiratory failure: a propensity score analysis

    Douglas J Hsu, Mengling Feng, Rishi Kothari, Hufeng Zhou, Kenneth P Chen, and Leo A Celi. The association between indwelling arterial catheters and mortality in hemodynamically stable patients with respiratory failure: a propensity score analysis. Chest, 148 0 (6): 0 1470--1476, 2015

    2015

  14. [22]

    Machine learning and ai for healthcare: Big data for improved health outcomes

    Panesar Arjun. Machine learning and ai for healthcare: Big data for improved health outcomes. Apress: London, UK, 2019

    2019

  15. [23]

    Digital twin: Values, challenges and enablers

    Adil Rasheed, Omer San, and Trond Kvamsdal. Digital twin: Values, challenges and enablers. arXiv preprint arXiv:1910.01719, 2019

    1910 arXiv

  16. [24]

    Make more digital twins

    Fei Tao and Qinglin Qi. Make more digital twins. Nature, 573 0 (7775): 0 490--491, 2019

    2019

  17. [25]

    Digital twin driven prognostics and health management for complex equipment

    Fei Tao, Meng Zhang, Yushan Liu, and Andrew YC Nee. Digital twin driven prognostics and health management for complex equipment. Cirp Annals, 67 0 (1): 0 169--172, 2018

    2018

  18. [26]

    Digital twins for multiple sclerosis

    Isabel Voigt, Hernan Inojosa, Anja Dillenseger, Rocco Haase, Katja Akg \"u n, and Tjalf Ziemssen. Digital twins for multiple sclerosis. Frontiers in immunology, 12: 0 669811, 2021

    2021

  19. [27]

    Home monitoring in asthma: towards digital twins

    David Drummond, Jolt Roukema, and Mari \"e lle Pijnenburg. Home monitoring in asthma: towards digital twins. Current Opinion in Pulmonary Medicine, 29 0 (4): 0 270--276, 2023

    2023

  20. [28]

    Towards a digital twin in human brain: Brain tumor detection using k-means

    Anastasios Loukas Sarris, Efstathios Sidiropoulos, Evangelos Paraskevopoulos, and Panagiotis Bamidis. Towards a digital twin in human brain: Brain tumor detection using k-means. In Caring is Sharing--Exploiting the Value in Data for Health and Innovation, pages 1052--1056. IOS...

    2023

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