В статье описаны основные аспекты технологий машинного обучения и искусственного интеллекта. Отражены возможности их применения для отделений анестезиологии и реаниматологии. Представлен современный опыт применения цифровых систем для анализа, прогнозирования и управления данными пациента с точки зрения клинической и экономической эффективности. Описана функциональность информационной системы поддержки принятия клинических решений IntelliSpace Critical Care and Anesthesia (ICCA) для отделений анестезиологии, реанимации и интенсивной терапии, основанная на управлении данными пациента, производства компании «Филипс».
Литература
1. 500 Person Gender-Height-Weight-Body Mass Index. Available at: https://www.kaggle.com/ yersever/500-person-gender-height-weight-bodymassindex.
2. Snow-cholera-map-1.jpg. Available at: https://en.wikipedia.org/wiki/File:Snow-cholera-map-1.jpg.
3. Shearer C. The CRISP-DM model: the new blueprint for data mining. Journal of Data Warehousing. 2000; 5: 13-22.
4. Lizotte DJ, Laber EB. Multi-objective Markov decision processes for data-driven decision support. Journal of Machine Learning Research: JMLR. 2016; 17: 211.
5. Ren O, Johnson A, Lehman E, Komorowski M, Aboab J, Fengyi Tang, Shahn Z, Sow D, Mark R, LiWei Lehman. Predicting and Understanding Unexpected Respiratory Decompensation in Critical Care Using Sparse and Heterogeneous Clinical Data. IEEE International Conference on Healthcare Informatics (ICHI). New York; 2018; 144-151. doi: 10.1109/ICHI.2018.00024.
6. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM, Hotchkiss RS, Levy MM, Marshall JC, Martin GS, Opal SM, Rubenfeld GD, van der Poll T, Vincent JL, Angus DC. The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA. 2016; 315(8): 801-810. doi: 10.1001/jama.2016.0287.
7. Fleischmann-Struzek C, Goldfarb DM, Schlattmann P, Schlapbach LJ, Reinhart K, Kissoon N. The global burden of pediatric and neonatal sepsis: a systematic review. The Lancet. Respiratory Medicine. 2018; 6(3): 223-230. doi: 10.1016/S2213-2600(18)30063-8.
8. Cheng L-F, Darnell G, Chivers C, Draugelis M, Li K, Engelhardt B. Sparse multi-output Gaussian processes for medical time series prediction. BMC Medical Informatics and Decision Making. 2020; 20(1): 152. doi: 10.1186/s12911-020-1069-4.
9. Rivers E, Nguyen B, Havstad S, Ressler J, Muzzin A, Knoblich B, Peterson E, Tomlanovich M; Early GoalDirected Therapy Collaborative Group. Early goal-directed therapy in the treatment of severe sepsis and septic shock. The New England Journal of Medicine. 2001; 345(19): 1368-1377. doi: 10.1056/ NEJMoa010307.
10. Mao Q, Jay M, Hoffman JL, Calvert J, Barton C, Shimabukuro D, Shieh L, Chettipally U, Fletcher G, Kerem Y, Zhou Y, Das R. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open. 2018; 8(1): e017833. doi: 10.1136/bmjopen-2017-017833.
11. Meyer A, Zverinski D, Pfahringer B, Kempfert J, Kuehne T, Sündermann SH, Stamm C, Hofmann T, Falk V, Eickhoff C. Machine learning for ral-time prediction of complications in critical care: a retrospective study. The Lancet. Respiratory Medicine. 2018; 6(12): 905-914. doi: 10.1016/S2213-2600(18)30300-X.
12. Subbe CP, Duller B, Bellomo R. Effect of an automated notification system for deteriorating ward patients on clinical outcomes. Critical Care. 2017; 21(1): 52. doi: 10.1186/s13054-017-1635-z.
13. Johnson AE, Pollard T. J, Shen L, Lehman L-W. H, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA. Mark RG. Mimic-III, a freely accessible critical care database. Scientific Data. 2016; 3: 160035. doi: 10.1038/sdata.2016.35.
14. Vincent JL, Martin GS, Levy MM. QSOFA does not replace SIRS in the definition of sepsis. Critical Care. 2016; 20(1): 210. doi: 10.1186/s13054-016-1389-z.
15. De Backer D, Donadello K, Sakr Y, Ospina-Tascon G, Salgado D, Scolletta S, Vincent JL. Microcirculatory alterations in patients with severe sepsis: impact of time of assessment and relationship with outcome. Critical Care Medicine. 2013; 41(3): 791-799. doi: 10.1097/CCM.0b013e3182742e8b.
16. Cheng L-F, Prasad N, Engelhardt BE. An Optimal Policy for Patient Laboratory Tests in Intensive Care Units. Pacific Symposium on Biocomputing. 2019; 24: 320-331.
2. Snow-cholera-map-1.jpg. Available at: https://en.wikipedia.org/wiki/File:Snow-cholera-map-1.jpg.
3. Shearer C. The CRISP-DM model: the new blueprint for data mining. Journal of Data Warehousing. 2000; 5: 13-22.
4. Lizotte DJ, Laber EB. Multi-objective Markov decision processes for data-driven decision support. Journal of Machine Learning Research: JMLR. 2016; 17: 211.
5. Ren O, Johnson A, Lehman E, Komorowski M, Aboab J, Fengyi Tang, Shahn Z, Sow D, Mark R, LiWei Lehman. Predicting and Understanding Unexpected Respiratory Decompensation in Critical Care Using Sparse and Heterogeneous Clinical Data. IEEE International Conference on Healthcare Informatics (ICHI). New York; 2018; 144-151. doi: 10.1109/ICHI.2018.00024.
6. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM, Hotchkiss RS, Levy MM, Marshall JC, Martin GS, Opal SM, Rubenfeld GD, van der Poll T, Vincent JL, Angus DC. The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA. 2016; 315(8): 801-810. doi: 10.1001/jama.2016.0287.
7. Fleischmann-Struzek C, Goldfarb DM, Schlattmann P, Schlapbach LJ, Reinhart K, Kissoon N. The global burden of pediatric and neonatal sepsis: a systematic review. The Lancet. Respiratory Medicine. 2018; 6(3): 223-230. doi: 10.1016/S2213-2600(18)30063-8.
8. Cheng L-F, Darnell G, Chivers C, Draugelis M, Li K, Engelhardt B. Sparse multi-output Gaussian processes for medical time series prediction. BMC Medical Informatics and Decision Making. 2020; 20(1): 152. doi: 10.1186/s12911-020-1069-4.
9. Rivers E, Nguyen B, Havstad S, Ressler J, Muzzin A, Knoblich B, Peterson E, Tomlanovich M; Early GoalDirected Therapy Collaborative Group. Early goal-directed therapy in the treatment of severe sepsis and septic shock. The New England Journal of Medicine. 2001; 345(19): 1368-1377. doi: 10.1056/ NEJMoa010307.
10. Mao Q, Jay M, Hoffman JL, Calvert J, Barton C, Shimabukuro D, Shieh L, Chettipally U, Fletcher G, Kerem Y, Zhou Y, Das R. Multicentre validation of a sepsis prediction algorithm using only vital sign data in the emergency department, general ward and ICU. BMJ Open. 2018; 8(1): e017833. doi: 10.1136/bmjopen-2017-017833.
11. Meyer A, Zverinski D, Pfahringer B, Kempfert J, Kuehne T, Sündermann SH, Stamm C, Hofmann T, Falk V, Eickhoff C. Machine learning for ral-time prediction of complications in critical care: a retrospective study. The Lancet. Respiratory Medicine. 2018; 6(12): 905-914. doi: 10.1016/S2213-2600(18)30300-X.
12. Subbe CP, Duller B, Bellomo R. Effect of an automated notification system for deteriorating ward patients on clinical outcomes. Critical Care. 2017; 21(1): 52. doi: 10.1186/s13054-017-1635-z.
13. Johnson AE, Pollard T. J, Shen L, Lehman L-W. H, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA. Mark RG. Mimic-III, a freely accessible critical care database. Scientific Data. 2016; 3: 160035. doi: 10.1038/sdata.2016.35.
14. Vincent JL, Martin GS, Levy MM. QSOFA does not replace SIRS in the definition of sepsis. Critical Care. 2016; 20(1): 210. doi: 10.1186/s13054-016-1389-z.
15. De Backer D, Donadello K, Sakr Y, Ospina-Tascon G, Salgado D, Scolletta S, Vincent JL. Microcirculatory alterations in patients with severe sepsis: impact of time of assessment and relationship with outcome. Critical Care Medicine. 2013; 41(3): 791-799. doi: 10.1097/CCM.0b013e3182742e8b.
16. Cheng L-F, Prasad N, Engelhardt BE. An Optimal Policy for Patient Laboratory Tests in Intensive Care Units. Pacific Symposium on Biocomputing. 2019; 24: 320-331.
Для цитирования
Романюк Т.И., Поздняков Д.Ю., Мушенок Ф.Б. Использование возможностей машинного обучения и искусственного интеллекта в отделениях анестезиологии и реанимации. Врач и информационные технологии. 2021; 2: 60-71. doi: 10.25881/18110193_2021_2_60.
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