В последнее время новая коронавирусная инфекция или COVID 19, вызванная возбудителем SARS-CoV 2, продолжает быстрое распространение по всему миру. По мнению Всемирной организации здравоохранения (ВОЗ), объявившей эту вспышку пандемией, COVID 19 является серьезной проблемой для общественного здравоохранения, имеющей международное значение. Из-за отсутствия доказанного эффективного лечения и вакцинации против COVID 19 меры предосторожности считаются ВОЗ стратегическими целями и основным способом противодействия пандемии. Руководствам стран рекомендовано принять национальные программы медицинского обслуживания, направленные на оценку и снижение риска распространения инфекции. На этом фоне технологии прогнозной аналитики стали активно использоваться для составления популяционных и персональных прогнозов развития заболеваемости, смертности, оценки тяжести течения болезни и т. д. В данной статье представлен обзор имеющихся разработок и публикаций по теме применения прогнозной аналитики для борьбы с пандемией COVID 19.
Литература
1. World Health Organization. Coronavirus disease (COVID 19) pandemic, https://www.who.int/emergencies/ diseases/novel-coronavirus 2019
2. Hamed M.A. An overview on COVID 19: reality and expectation. Bull Natl Res Cent. 2020;44(1):86. doi: 10.1186/s42269 020 00341 9. Epub 2020 Jun 1. PMID: 32514228; PMCID: PMC7266424, https://doi. org/10.1186/s42269 020 00341 9
3. Khalifa M. Health Analytics Types, Functions and Levels: A Review of Literature. Stud Health Technol Inform. 2018;251:137–140. PMID: 29968621, https://pubmed.ncbi.nlm.nih.gov/29968621/
4. Healthcare Analytics Market Size By Product (Descriptive Analytics, Predictive Analytics, Prescriptive Analytics), By Application (Operations Management, Financial Management, Population Health Management, Clinical Management) By End-Use (Hospitals, Clinics, Others), Industry Analysis Report, Regional Outlook, Application Potential, Price Trends, Competitive Market Share & Forecast, 2019–2025, https://www.gminsights.com/industry-analysis/healthcare-analytics-market
5. Global Healthcare Analytics Markets, 2019–2024 by Type, Solution, Deployment Model, Application, End-user, and Geography – ResearchAndMarkets.com, https://www.researchandmarkets.com/reports/4858254/ healthcare-analytics-market-forecasts-from 2019
6. Healthcare Analytics Market Size, Share & Industry Analysis, By Product (Descriptive, Predic-tive, and Prescriptive), By Application (Financial Analytics, Population Health Analytics, Clinical Analytics, and Operations and Administrative Analytics), By End User (Payers, Providers, and Others) and Regional Forecast, 2019– 2026, https://www.fortunebusinessinsights.com/healthcare-analytics-market 102641
7. Healthcare Analytics Market by Type (Predictive, Prescriptive), Component (Hardware, Software, and Services), Delivery Mode (Cloud), Application (Clinical, RCM, Claims, Fraud, Risk, PHM), End user (Payer, Provider) and Geography- Global Forecast to 2027, https://www.meticulousresearch.com/product/healthcare-analytics-market/
8. Coronavirus ‘could infect 60% of global population if unchecked’, https://www.theguardian.com/ world/2020/feb/11/coronavirus-expert-warns-infection-could-reach 60-of-worlds-population
9. Harvard Professor Sounds Alarm on ‘Likely’ Coronavirus Pandemic: 40% to 70% of World Could Be Infected This Year, https://www.mediaite.com/news/harvard-professor-sounds-alarm-on-likely-coronavirus-pandemic 40-to 70-of-world-could-be-infected-this-year/
10. Coronavirus: Up to 70% of Germany could become infected – Merkel, https://www.bbc.com/news/ world-us-canada 51835856
11. The Cognitive Bias That Makes Us Panic About Coronavirus, https://www.bloomberg.com/opinion/articles/2020–02–28/coronavirus-panic-caused-by-probability-neglect
12. Коронавирус: опасная иллюзия смертности, https://habr.com/ru/post/494896/
13. Путин о коронавирусе: «Нам нужен профессиональный прогноз», https://vostokmedia.com/news/society/13–04–2020/putin-o-koronaviruse-nam-nuzhen-professionalnyy-prognoz
14. Jewell N.P., Lewnard J.A., Jewell B.L. Predictive Mathematical Models of the COVID 19 Pandemic: Underlying Principles and Value of Projections. JAMA. 2020;323(19):1893–1894. doi:10.1001/jama.2020.6585, https://jamanetwork.com/journals/jama/fullarticle/2764824
15. He S., Peng Y. & Sun K. SEIR modeling of the COVID 19 and its dynamics. Nonlinear Dyn 101, 1667–1680 (2020). https://doi.org/10.1007/s11071-020-05743-y
16. Tolles J., Luong T. Modeling Epidemics With Compartmental Models. JAMA. 2020;323(24):2515–2516. doi:10.1001/jama.2020.8420, https://jamanetwork.com/journals/jama/fullarticle/2766672
17. Zhao Y.-F., Shou M.-H., Wang Z.-X. Prediction of the Number of Patients Infected with COVID 19 Based on Rolling Grey Verhulst Models. Int. J. Environ. Res. Public Health 2020, 17, 4582, https://doi.org/10.3390/ ijerph17124582
18. Why It’s So Freaking Hard To Make A Good COVID 19 Model, https://fivethirtyeight.com/features/why-its-so-freaking-hard-to-make-a-good-covid 19-model/
19. Google Cloud AI and Harvard Global Health Institute Collaborate on new COVID 19 forecasting model, https://cloud.google.com/blog/products/ai-machine-learning/google-cloud-is-releasing-the-covid 19-public-forecasts
20. Cleveland Clinic’s COVID 19 strategy driven by data modeling, https://searchbusinessanalytics.techtarget. com/feature/Cleveland-Clinics-COVID 19-strategy-driven-by-data-modeling
21. Harmon S.A., Sanford T.H., Xu S. et al. Artificial intelligence for the detection of COVID 19 pneumonia on chest CT using multinational datasets. Nat Commun 11, 4080 (2020). https://doi.org/10.1038/ s41467 020 17971 2
22. Морозов С. П., Гомболевский В. А., Чернина В. Ю., Блохин И. А., Мокиенко О. А., Владзимирский А. В., Белевский А. С., Проценко Д. Н., Лысенко М. А., Зайратьянц О. В., Никонов Е. Л. Прогнозирование летальных исходов при covid 19 по данным компьютерной томографии органов грудной клетки. Туберкулез и болезни легких. – 2020. – Т. 98. – № 6. – С. 7–14.
23. Лучевая диагностика коронавирусной болезни (COVID 19): организация, методология, интерпретация результатов: препринт № ЦДТ – 2020 – I. Версия 2 от 17.04.2020 / сост. С. П. Морозов, Д. Н. Проценко, С. В. Сметанина [и др.] // Серия «Лучшие практики лучевой и инструментальной диагностики». – Вып. 65. – М.: ГБУЗ «НПКЦ ДиТ ДЗМ», 2020. – 78 с.
24. Bayat V., Phelps S., Ryono R., Lee C., Parekh H., Mewton J., Sedghi F., Etminani P., Holodniy M. A SARS-CoV 2 Prediction Model from Standard Laboratory Tests. Clin Infect Dis. 2020 Aug 12: ciaa1175. doi: 10.1093/cid/ciaa1175. Epub ahead of print. PMID: 32785701; PMCID: PMC7454351, https://pubmed.ncbi.nlm.nih.gov/32785701/
25. Yan, L., Zhang, H., Goncalves, J. et al. An interpretable mortality prediction model for COVID 19 patients. Nat Mach Intell 2, 283–288 (2020). https://doi.org/10.1038/s42256 020 0180 7
26. Liang W., Yao J., Chen A. et al. Early triage of critically ill COVID 19 patients using deep learning. Nat Commun 11, 3543 (2020). https://doi.org/10.1038/s41467 020 17280 8
27. Schalekamp S., Huisman M., van Dijk R.A., Boomsma M.F., Freire Jorge P.J., de Boer W.S., Herder G.J.M., Bonarius M., Groot O.A., Jong E., Schreuder A., Schaefer-Prokop C.M. Model-based Prediction of Critical Illness in Hospitalized Patients with COVID 19. Radiology. 2020 Aug 13:202723. doi: 10.1148/radiol.2020202723. Epub ahead of print. PMID: 32787701; PMCID: PMC7427120, https://pubmed.ncbi.nlm.nih.gov/32787701/
28. Jehi L., Ji X., Milinovich A., Erzurum S., Merlino A., Gordon S., Young J.B., Kattan M.W. Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID 19. PLoS One. 2020 Aug 11;15(8): e0237419. doi: 10.1371/journal.pone.0237419. PMID: 32780765; PMCID: PMC7418996, https://pubmed.ncbi.nlm.nih.gov/32780765/
29. FDA greenlights ICU AI for predicting fatal COVID 19 complications, https://www.fiercebiotech.com/medtech/fda-greenlights-icu-ai-for-predicting-fatal-covid 19-complications
30. Bartkowiak B., Snyder A.M., Benjamin A., Schneider A., Twu N.M., Churpek M.M., Roggin K.K., Edelson D.P. Validating the Electronic Cardiac Arrest Risk Triage (eCART) Score for Risk Stratification of Surgical Inpatients in the Postoperative Setting: Retrospective Cohort Study. Ann Surg. 2019 Jun;269(6):1059–1063. doi: 10.1097/ SLA.0000000000002665. PMID: 31082902; PMCID: PMC6610875, https://pubmed.ncbi.nlm.nih.gov/31082902/
31. Covid 19: NHS datasets ‘not sophisticated enough’ to flag high risk patients, https://www.digitalhealth. net/2020/03/covid 19-nhs-datasets-not-sophisticated-enough-to-flag-high-risk-patients/
32. Israel is using AI to flag high-risk covid 19 patients, https://www.technologyreview.com/2020/04/24/1000543/ israel-ai-prediction-medical-testing-data-high-risk-covid 19-patients/
33. Israel Aerospace разработала AI-модель для прогнозирования прогрессирования болезни у пациентов с COVID 19, https://evercare.ru/news/israel-aerospace-razrabotala-ai-model-dlya-prognozirovaniya-progressirovaniya-bolezni-u
34. How AI can determine which coronavirus patients require hospitalization, https://thenextweb.com/ neural/2020/04/02/ai-can-help-manage-hospital-resources-during-the-coronavirus-crisis-syndication/
2. Hamed M.A. An overview on COVID 19: reality and expectation. Bull Natl Res Cent. 2020;44(1):86. doi: 10.1186/s42269 020 00341 9. Epub 2020 Jun 1. PMID: 32514228; PMCID: PMC7266424, https://doi. org/10.1186/s42269 020 00341 9
3. Khalifa M. Health Analytics Types, Functions and Levels: A Review of Literature. Stud Health Technol Inform. 2018;251:137–140. PMID: 29968621, https://pubmed.ncbi.nlm.nih.gov/29968621/
4. Healthcare Analytics Market Size By Product (Descriptive Analytics, Predictive Analytics, Prescriptive Analytics), By Application (Operations Management, Financial Management, Population Health Management, Clinical Management) By End-Use (Hospitals, Clinics, Others), Industry Analysis Report, Regional Outlook, Application Potential, Price Trends, Competitive Market Share & Forecast, 2019–2025, https://www.gminsights.com/industry-analysis/healthcare-analytics-market
5. Global Healthcare Analytics Markets, 2019–2024 by Type, Solution, Deployment Model, Application, End-user, and Geography – ResearchAndMarkets.com, https://www.researchandmarkets.com/reports/4858254/ healthcare-analytics-market-forecasts-from 2019
6. Healthcare Analytics Market Size, Share & Industry Analysis, By Product (Descriptive, Predic-tive, and Prescriptive), By Application (Financial Analytics, Population Health Analytics, Clinical Analytics, and Operations and Administrative Analytics), By End User (Payers, Providers, and Others) and Regional Forecast, 2019– 2026, https://www.fortunebusinessinsights.com/healthcare-analytics-market 102641
7. Healthcare Analytics Market by Type (Predictive, Prescriptive), Component (Hardware, Software, and Services), Delivery Mode (Cloud), Application (Clinical, RCM, Claims, Fraud, Risk, PHM), End user (Payer, Provider) and Geography- Global Forecast to 2027, https://www.meticulousresearch.com/product/healthcare-analytics-market/
8. Coronavirus ‘could infect 60% of global population if unchecked’, https://www.theguardian.com/ world/2020/feb/11/coronavirus-expert-warns-infection-could-reach 60-of-worlds-population
9. Harvard Professor Sounds Alarm on ‘Likely’ Coronavirus Pandemic: 40% to 70% of World Could Be Infected This Year, https://www.mediaite.com/news/harvard-professor-sounds-alarm-on-likely-coronavirus-pandemic 40-to 70-of-world-could-be-infected-this-year/
10. Coronavirus: Up to 70% of Germany could become infected – Merkel, https://www.bbc.com/news/ world-us-canada 51835856
11. The Cognitive Bias That Makes Us Panic About Coronavirus, https://www.bloomberg.com/opinion/articles/2020–02–28/coronavirus-panic-caused-by-probability-neglect
12. Коронавирус: опасная иллюзия смертности, https://habr.com/ru/post/494896/
13. Путин о коронавирусе: «Нам нужен профессиональный прогноз», https://vostokmedia.com/news/society/13–04–2020/putin-o-koronaviruse-nam-nuzhen-professionalnyy-prognoz
14. Jewell N.P., Lewnard J.A., Jewell B.L. Predictive Mathematical Models of the COVID 19 Pandemic: Underlying Principles and Value of Projections. JAMA. 2020;323(19):1893–1894. doi:10.1001/jama.2020.6585, https://jamanetwork.com/journals/jama/fullarticle/2764824
15. He S., Peng Y. & Sun K. SEIR modeling of the COVID 19 and its dynamics. Nonlinear Dyn 101, 1667–1680 (2020). https://doi.org/10.1007/s11071-020-05743-y
16. Tolles J., Luong T. Modeling Epidemics With Compartmental Models. JAMA. 2020;323(24):2515–2516. doi:10.1001/jama.2020.8420, https://jamanetwork.com/journals/jama/fullarticle/2766672
17. Zhao Y.-F., Shou M.-H., Wang Z.-X. Prediction of the Number of Patients Infected with COVID 19 Based on Rolling Grey Verhulst Models. Int. J. Environ. Res. Public Health 2020, 17, 4582, https://doi.org/10.3390/ ijerph17124582
18. Why It’s So Freaking Hard To Make A Good COVID 19 Model, https://fivethirtyeight.com/features/why-its-so-freaking-hard-to-make-a-good-covid 19-model/
19. Google Cloud AI and Harvard Global Health Institute Collaborate on new COVID 19 forecasting model, https://cloud.google.com/blog/products/ai-machine-learning/google-cloud-is-releasing-the-covid 19-public-forecasts
20. Cleveland Clinic’s COVID 19 strategy driven by data modeling, https://searchbusinessanalytics.techtarget. com/feature/Cleveland-Clinics-COVID 19-strategy-driven-by-data-modeling
21. Harmon S.A., Sanford T.H., Xu S. et al. Artificial intelligence for the detection of COVID 19 pneumonia on chest CT using multinational datasets. Nat Commun 11, 4080 (2020). https://doi.org/10.1038/ s41467 020 17971 2
22. Морозов С. П., Гомболевский В. А., Чернина В. Ю., Блохин И. А., Мокиенко О. А., Владзимирский А. В., Белевский А. С., Проценко Д. Н., Лысенко М. А., Зайратьянц О. В., Никонов Е. Л. Прогнозирование летальных исходов при covid 19 по данным компьютерной томографии органов грудной клетки. Туберкулез и болезни легких. – 2020. – Т. 98. – № 6. – С. 7–14.
23. Лучевая диагностика коронавирусной болезни (COVID 19): организация, методология, интерпретация результатов: препринт № ЦДТ – 2020 – I. Версия 2 от 17.04.2020 / сост. С. П. Морозов, Д. Н. Проценко, С. В. Сметанина [и др.] // Серия «Лучшие практики лучевой и инструментальной диагностики». – Вып. 65. – М.: ГБУЗ «НПКЦ ДиТ ДЗМ», 2020. – 78 с.
24. Bayat V., Phelps S., Ryono R., Lee C., Parekh H., Mewton J., Sedghi F., Etminani P., Holodniy M. A SARS-CoV 2 Prediction Model from Standard Laboratory Tests. Clin Infect Dis. 2020 Aug 12: ciaa1175. doi: 10.1093/cid/ciaa1175. Epub ahead of print. PMID: 32785701; PMCID: PMC7454351, https://pubmed.ncbi.nlm.nih.gov/32785701/
25. Yan, L., Zhang, H., Goncalves, J. et al. An interpretable mortality prediction model for COVID 19 patients. Nat Mach Intell 2, 283–288 (2020). https://doi.org/10.1038/s42256 020 0180 7
26. Liang W., Yao J., Chen A. et al. Early triage of critically ill COVID 19 patients using deep learning. Nat Commun 11, 3543 (2020). https://doi.org/10.1038/s41467 020 17280 8
27. Schalekamp S., Huisman M., van Dijk R.A., Boomsma M.F., Freire Jorge P.J., de Boer W.S., Herder G.J.M., Bonarius M., Groot O.A., Jong E., Schreuder A., Schaefer-Prokop C.M. Model-based Prediction of Critical Illness in Hospitalized Patients with COVID 19. Radiology. 2020 Aug 13:202723. doi: 10.1148/radiol.2020202723. Epub ahead of print. PMID: 32787701; PMCID: PMC7427120, https://pubmed.ncbi.nlm.nih.gov/32787701/
28. Jehi L., Ji X., Milinovich A., Erzurum S., Merlino A., Gordon S., Young J.B., Kattan M.W. Development and validation of a model for individualized prediction of hospitalization risk in 4,536 patients with COVID 19. PLoS One. 2020 Aug 11;15(8): e0237419. doi: 10.1371/journal.pone.0237419. PMID: 32780765; PMCID: PMC7418996, https://pubmed.ncbi.nlm.nih.gov/32780765/
29. FDA greenlights ICU AI for predicting fatal COVID 19 complications, https://www.fiercebiotech.com/medtech/fda-greenlights-icu-ai-for-predicting-fatal-covid 19-complications
30. Bartkowiak B., Snyder A.M., Benjamin A., Schneider A., Twu N.M., Churpek M.M., Roggin K.K., Edelson D.P. Validating the Electronic Cardiac Arrest Risk Triage (eCART) Score for Risk Stratification of Surgical Inpatients in the Postoperative Setting: Retrospective Cohort Study. Ann Surg. 2019 Jun;269(6):1059–1063. doi: 10.1097/ SLA.0000000000002665. PMID: 31082902; PMCID: PMC6610875, https://pubmed.ncbi.nlm.nih.gov/31082902/
31. Covid 19: NHS datasets ‘not sophisticated enough’ to flag high risk patients, https://www.digitalhealth. net/2020/03/covid 19-nhs-datasets-not-sophisticated-enough-to-flag-high-risk-patients/
32. Israel is using AI to flag high-risk covid 19 patients, https://www.technologyreview.com/2020/04/24/1000543/ israel-ai-prediction-medical-testing-data-high-risk-covid 19-patients/
33. Israel Aerospace разработала AI-модель для прогнозирования прогрессирования болезни у пациентов с COVID 19, https://evercare.ru/news/israel-aerospace-razrabotala-ai-model-dlya-prognozirovaniya-progressirovaniya-bolezni-u
34. How AI can determine which coronavirus patients require hospitalization, https://thenextweb.com/ neural/2020/04/02/ai-can-help-manage-hospital-resources-during-the-coronavirus-crisis-syndication/
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