An approach for modular database architecture design in the intensive care unit

   This article presents the design of a database intended to optimize the storage and processing of medical data, with a focus on decision support in intensive care and resuscitation.

   The aim of the study is to develop a logical database model based on advanced principles and methods used in international open database projects, capable of minimizing human error and enhancing the accuracy of real-time patient prognosis.

   The methodology is founded on a comparative analysis of existing international medical databases, such as MIMIC-IV and eICU. An innovative modular approach was applied in designing the new database, ensuring system flexibility and scalability. The primary outcome is the creation of a logical database model that can be effectively utilized within the Russian healthcare system, including remote and low-resource regions. The logical model was developed taking into account the specifics of medical data, including modules for storing information on hospitalizations, patient condition indicators, laboratory tests, medication prescriptions and other aspects of clinical practice. An important part of the study is the integration of the database with Russian medical information systems and adaptation to national standards and regulatory requirements. The developed architecture of the logical model minimizes the influence of the human factor, automates data analysis and can be used in the development of medical decision support systems. The practical significance lies in improving the quality of medical care and reducing the burden on the staff. The system is applicable in Russian institutions, including remote regions, and contributes to the digitalization of healthcare.

1. Ukaz Prezidenta Rossijskoj Federacii ot 07. 05. 2024 g. №309 «O nacional'nyh celyah razvitiya Rossijskoj Federacii na period do 2030 goda i na perspektivu do 2036 goda». (In Russ.)

2. Edinyj plan po dostizheniyu nacional'nyh celej razvitiya Rossijskoj Federacii do 2030 goda i na perspektivu do 2036 goda (utv. Pravitel'stvom RF). (In Russ.)

3. Pokida AN, Zybunovskaya NV. Health in the perception of Russians and real medical practices. Zdorov’e Naseleniya i Sreda Obitaniya. 2021; 29(7): 19-27. (In Russ.) doi: 10.35627/2219-5238/2021-29-7-19-27.

4. Sychev EV, Esaulenko IE, Petrova TN, Petrov IS. Personnel Policy of Rural Healthcare and Ways to Improve its Effectiveness. Science of the young (Eruditio Juvenium). 2023; 11(4): 535-544. (In Russ.) doi: 10.23888/HMJ2023114535-544.

5. Ramaniuk TI, Pozdnyakov DYu, Mushenok FB. Use of the opportunities of machine learning and artificial intelligence in the intensive care unit. Medical doctor and information technology. 2021; 2: 60-71. (In Russ.) doi: 1025881/18110193_2021_2_60.

6. Ramaniuk TI, Pozdnyakov DYU, Mushenok FB. Use of the opportunities of machine learning and artificial intelligence in the intensive care unit. Medical doctor and information technology. 2021; 2: 60-71. (In Russ.) doi: 1025881/18110193_2021_2_60.

7. Gorban VI, Shchegolev AV, Protsenko DN, et al. Digitalization of anesthesiology and resuscitation services: multicenter questionnaire study. Annals of Critical Care. 2024; 2: 43-53. (In Russ.) doi: 10.21320/1818-474X-2024-2-43-53.

8. Gusarov VG, Zamyatin MN, Gorohovatskij YUI, et al. Patient safety as the foundation for the development strategy of the Department of Anesthesiology and Intensive Care of the Pirogov Center. Bulletin of Pirogov National Medical & Surgical Center. 2022; 17(4-2): 4-12. (In Russ.) doi: 10.25881/20728255_2022_17_4_2_4.

9. Karpov OE, Gusarov VG, Zamyatin MN, et al. Digital Solutions Integration into the Anesthesiology Service of a Multidisciplinary Clinic. Bulletin of Pirogov National Medical & Surgical Center. 2020; 15(3-2): 106-113. (In Russ.) doi: 10.25881/BPNMSC.2020.33.66.020.

10. Klykov AI, Frolov NS. Features of priority project «Improvement of medical care through the introduction of information technologies». Smolensk Medical Almanac. 2019; 3: 78-81. (In Russ.)

11. Korchagin EE, Gordeeva NV, Demko IV, et al. Use of information systems in healthcare. Siberian Medical Review. 2019; 3: 106-111. (In Russ.) doi: 10.20333/2500136-2019-3-106-111.

12. Kleymenova E B, Yashina L P. The role of health information technology in promoting patient safety. Medical doctor and information technology. 2020; 3: 13-24. (In Russ.) doi: 10.37690/1811-0193-2020-3-13-24.

13. Mikheev AE. Opportunities, problems and prospects of information technologies in the field of clinical safety. Manager Zdravoohranenia. 2023; 1: 5-20. (In Russ.) doi: 10.21045/1811-0185-2023-S-5-20.

14. Koshechkin KA. Regulation of artificial intelligence in medicine. Patient-Oriented Medicine and Pharmacy. 2023; 1(1): 32-40. (In Russ.) doi: 10.37489/2949-1924-0005.

15. Mirskikh I, Mingaleva Z, Kuranov V, Matseeva S. Digitization of Medicine in Russia: Mainstream Development and Potential. In: Antipova, T. (eds) Integrated Science in Digital Age 2020. ICIS 2020. Lecture Notes in Networks and Systems. 2021; 136. Springer, Cham. doi: 10.1007/978-3-030-49264-9_30.

16. Kononova O, Prokudin D, Timofeeva A, Matrosova E. The Digital Era of Healthcare in Russia: Case Study. Lecture Notes in Information Systems and Organization, in: Zaramenskikh E, Fedorova A. (ed.), Digital Transformation and New Challenges. 2021. Р.265-286. Springer.

17. Yanovskaya O, Kulagina N, Logacheva N. Digital inequality of Russian regions. Sustainable Development and Engineering Economics 1. 2022; 5: 77-98. (In Russ.) doi: 10.48554/SDEE.2022.1.5.

18. Krom IL, Erugina MV, Yeremina MG, et al. Optimization of medical care in regional health care: perspectives and barriers. Sociology of Medicine. 2023; 22(1): 19-27. (In Russ.) doi: 10.17816/socm252064.

19. Grechko AV, Yadgarov MY, Yakovlev AA, et al. RICD: Russian Intensive Care Dataset. General Reanimatology. 2024; 20(3): 22-31. (In Russ.) doi: 10.15360/1813-9779-2024-3-22-31.

20. Chen C, Ma S, Liao L, Xiao Y, Dai H. Effects of mesenchymal stem cells on postresuscitation renal and intestinal injuries in a porcine cardiac arrest model. Shock. 2023; 59(5): 803-809. doi: 10.1097/SHK.0000000000002107.

21. Kakadiaris A. Evaluating the Fairness of the MIMIC-IV Dataset and a Baseline Algorithm: Application to the ICU Length of Stay Prediction. License: arXiv.org perpetual non-exclusive license arXiv:2401.00902v1 [cs.LG] 31 Dec 2023. doi: 10.48550/arXiv.2401.00902.

22. González-Nóvoa JA, Busto L, Rodríguez-Andina JJ, et al. Using Explainable Machine Learning to Improve Intensive Care Unit Alarm Systems. Sensors. 2021; 21: 7125. doi: 10.3390/s21217125.

23. Alabdulhafith M, Saleh H, Elmannai H, et al. A Clinical Decision Support Systemfor Edge/Cloud ICU Readmission Model Based on Particle Swarm Optimization, Ensemble Machine Learning, and Explainable Artificial Intelligence. IEEE Access. 2023; 11: 100604–100621. doi: 10.1109/ACCESS.2023.3312343.

24. Amico B. Explainable Temporal Data Mining Techniques to Support the Prediction Task in Medicine. S.S.D. INF/01 — Informatics. 2023. Р. 165.

25. Thorsen-Meyer HC, Nielsen AB, Nielsen AP, et al. Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records. Lancet Digit Health. 2020; 2(4): e179-e191. doi: 10.1016/S2589-7500(20)30018-2.

26. Shishkin S, Sheiman I, Vlassov V, Potapchik E, Sazhina S. Structural changes in the Russian health care system: do they match European trends? Health Econ Rev. 2022; 12(1): 29. doi: 10.1186/s13561-022-00373-z.

27. Ye Z, An S, Gao Y, et al. Association between the triglyceride glucose index and in-hospital and 1-year mortality in patients with chronic kidney disease and coronary artery disease in the intensive care unit. Cardiovasc Diabetol. 2022; 110(2023). doi: 10.1186/s12933-023-01843-2.

28. Xu J, Cai H, Zheng, X. Timing of vasopressin initiation and mortality in patients with septic shock: analysis of the MIMIC-III and MIMIC-IV databases. BMC Infect Dis. 2023; 199(2023). doi: 10.1186/s12879-023-08147-6.

29. Di Martino F, Delmastro F. Explainable AI for clinical and remote health applications: a survey on tabular and time series data. Artif Intell Rev. 2023; 56: 5261-5315. doi: 10.1007/s10462-022-10304-3.

30. 30 Morid AM, Sheng O, Dunbar J. Time series prediction using deep learning methods in healthcare. ACM Trans Manage Info Systs. 2023; 14: 1-29. doi: 10.1145/3531326.

31. Idowu EAA, Teo J, Salih S, Valverde J, Yeung JA. Streams, rivers and data lakes: an introduction to understanding modern electronic healthcare records. Clin Med (Lond). 2023; 23(4): 409. doi: 10.7861/clinmed.2022-0325.

32. Stephany ND, Nan K, Douglas C, et al. HL7 FHIR-based tools and initiatives to support clinical research: a scoping review, Journal of the American Medical Informatics Association. 2022; 29(9): 1642-1653. doi: 10.1093/jamia/ocac105.

33. Dhayne H, Haque R, Kilany R, Taher Y. In Search of Big Medical Data Integration Solutions - A Comprehensive Survey. In IEEE Access. 2019; 7: 91265-91290. doi: 10.1109/ACCESS.2019.2927491.

34. Parciak M, Suhr M, Schmidt C, et al. FAIRness through automation: development of an automated medical data integration infrastructure for FAIR health data in a maximum care university hospital. BMC Med Inform Decis Mak 23. 2023; 94. doi: 10.1186/s12911-023-02195-3.

35. Bernonille S, Nies J, Pedersen HG, et al. Three different cases of exploiting decision support services for adverse drug event prevention. Stud Health Technol Inform. 2011; 166: 180-8.

36. Imran S, Mahmood T, Morshed A, ad Sellis T. Big data analytics in healthcare − A systematic literature review and roadmap for practical implementation, in IEEE/CAA Journal of Automatica Sinica. 2021; 8(1): 1-22. doi: 10.1109/JAS.2020.1003384.

37. Ciampi M, Sicuranza M, Silvestri S. A Privacy-Preserving and Standard-Based Architecture for Secondary Use of Clinical Data. Information 2022; 13(2): 87. doi: 10.3390/info13020087.

38. Jiang J, Hewner S, Chandola V. Explainable Deep Learning for Readmission Prediction with Tree-GloVe Embedding, 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI). 09-12 August 2021. Р. 138-147. doi: 10.1109/ICHI52183.2021.00031.

39. Blinov P, Avetisian M, Kokh V, Umerenkov D, Tuzhilin A. Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-Based Neural Networks. In: Michalowski M, Moskovitch R. (eds) Artificial Intelligence in Medicine. AIME 2020. Lecture Notes in Computer Science,2020; 12299. Springer. Cham. doi: 10.1007/978-3-030-59137-3_11.

40. Kontsevaya A, Bobrova N, Barbarash O, et al. The management of acute myocardial infarction in the Russian Federation: protocol for a study of patient pathways. Wellcome Open Res. 2017; 2: 89. doi: 10.12688/wellcomeopenres.12478.2.

41. Brankovic A, Hassanzadeh H, Good N, et al. Explainable machine learning for real-time deterioration alert prediction to guide pre-emptive treatment. Sci Rep. 2022; 12: 11734. doi: 10.1038/s41598-022-15877-1.

42. Pang K, Li L, Ouyang W, Liu X, Tang Y. Establishment of ICU Mortality Risk Prediction Models with Machine Learning Algorithm Using MIMIC-IV Database. Diagnostics. 2022; 12(5): 1068. doi: 10.3390/diagnostics12051068.

43. Sun Y, He Z, Ren, J, et al. Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC-IV database based on machine learning. BMC Anesthesiol. 2023; 23: 178. doi: 10.1186/s12871-023-02138-5.

44. Liu W, Tao G, Zhang Y, et al. A Simple Weaning Model Based on Interpretable Machine Learning Algorithm for Patients With Sepsis: A Research of MIMIC-IV and eICU Databases. Front. Med. 2022; 8: 814566. doi: 10.3389/fmed.2021.814566.

45. Kovalchuk SV, Funkner AA, Metsker OG, Yakovlev AN. Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification. Journal of biomedical informatics, 2018; 82: 128-142. doi: 10.1016/j.jbi.2018.05.004.

46. Cao Y, Li Y, Wang M, et al. Interpretable machine learning for predicting risk of invasive fungal infection in critically ill patients in the intensive care unit: a retrospective cohort study based on MIMIC-IV database. Shock. 2024; 61(6): 817-827. doi: 10.1097/SHK.0000000000002312.

47. Sun Y, He Z, Ren J, et al. Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC -IV database based on machine learning. BMC Anesthesiol. 2023; 23: 178. doi: 10.1186/s12871-023-02138-5.

48. Pang K, Liang L, Wen O, Xing L, Yongzhong T. Establishment of ICU Mortality Risk Prediction Models with Machine Learning Algorithm Using MIMIC-IV Database. Diagnostics,2022; 12(5): 1068. doi: 10.3390/diagnostics12051068.

49. Shu T, Huang J, Deng J, et al. Development and assessment of scoring model for ICU stay and mortality prediction after emergency admissions in ischemic heart disease: a retrospective study of MIMIC-IV databases. Intern Emerg Med. 2023; 18: 487-497. doi: 10.1007/s11739-023-03199-7.

50. Johnson AEW, Bulgarelli L, Shen L, et al. MIMIC-IV, a freely accessible electronic health record dataset. Sci Data. 2023; 10(1). doi: 10.1038/s41597-022-01899-x.

51. Medical Information Mart for Intensive Care. MIMIC Online Documentation: https://mimic.mit.edu.