Research progress of patient intelligent monitoring and risk warning_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

LIANG Shiqi ¹ , LIU Yuwei ² , HE Yuhua ² ,  GONG Renrong ³ ,  LI Ka ¹

1. West China School of Nursing, Sichuan University/West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
2. West China School of Nursing, Sichuan University/Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
3. West China School of Nursing, Sichuan University/Department of Surgery, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;

Corresponding author：

GONG Renrong, Email: gongrenrong@163.com; LI Ka, Email: likalika127127@163.com

Keywords：

patient monitoring; vital signs; risk warning; artificial intelligence; structure database

DOI：

10.7507/1007-9424.202011092

Video：

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Objective To summarize the research status of intelligent patient monitoring and risk warning, and provide reference and enlightenment for promoting the construction of intelligent monitoring and management platform of clinical patient risk.Method The literatures about patient monitoring and risk warning at home and abroad in recent years were reviewed.Results At present, the research at home and abroad mainly focused on the retrospective construction of the prediction model of severe complications of inpatients by using the electronic medical record database. The clinical decision support system based on real-time vital signs and dynamic electronic medical record data was still in the early development stage, and there was no mature product with high market share.Conclusions The construction process of structured electronic medical record system should be further strengthened, and the fully integrated clinical decision support system and artificial intelligence self-learning system should be the key research and development direction in the future, so as to promote the deep integration of big data and artificial intelligence technology with clinical scenes.

Citation： LIANG Shiqi, LIU Yuwei, HE Yuhua, GONG Renrong, LI Ka. Research progress of patient intelligent monitoring and risk warning. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2021, 28(9): 1232-1236. doi: 10.7507/1007-9424.202011092 Copy

1.	Andersen LW, Kim WY, Chase M, et al. The prevalence and significance of abnormal vital signs prior to in-hospital cardiac arrest. Resuscitation, 2016, 98: 112-117.
2.	Cherry PG, Jones CP. Attitudes of nursing staff towards a modified early warning system. Br J Nurs, 2015, 24(16): 812-818.
3.	Nightgale-EU. Nightingale: Levels of sophistication in early warning. Available from: https://www.nightingale-h2020.eu/industry/presentations-our-omc-meetings.
4.	Dursun Ergezen F, Kol E. Nurses’ responses to monitor alarms in an intensive care unit: An observational study. Intensive Crit Care Nurs, 2020, 59: 102845.
5.	刘凤春, 孟茜, 梁宝娟, 等. 应用“应力环”降低心电监护仪误报警率的效果评价. 中国实用护理杂志, 2020, 36(17): 1319-1322.
6.	王娜, 李龙倜, 徐圣康, 等. 重症监护室护士医疗设备报警疲劳的现状及其影响因素分析. 现代临床护理, 2018, 17(12): 11-16.
7.	Nix M. Combating alarm fatigue. Am J Nur, 2015, 115(2): 16.
8.	ECRI. Top 10 technology health hazards for 2020: Solutions Kit. 2019-10-29. https://www.ecri.org/search-results/member-preview/hdjournal/pages/2020_top_10_hazards/.
9.	Arnold J, Davis A, Fischhoff B, et al. Comparing the predictive ability of a commercial artificial intelligence early warning system with physician judgement for clinical deterioration in hospitalised general internal medicine patients: a prospective observational study. BMJ Open, 2019, 9(10): e032187.
10.	Uppanisakorn S, Bhurayanontachai R, Boonyarat J, et al. National Early Warning Score (NEWS) at ICU discharge can predict early clinical deterioration after ICU transfer. J Crit Care, 2018, 43: 225-229.
11.	Bian Y, Xu F, Lv RJ, et al. An early warning scoring system for the prevention of acute heart failure. Int J Cardiol, 2015, 183: 111-116.
12.	李红, 吴婷婷, 穆艳. 急性冠状动脉综合征患者心搏骤停前24 h早期预警评分变化趋势的研究. 中华护理杂志, 2019, 54(1): 14-18.
13.	Alam N, Hobbelink EL, van Tienhoven AJ, et al. The impact of the use of the Early Warning Score (EWS) on patient outcomes: a systematic review. Resuscitation, 2014, 85(5): 587-594.
14.	Kolic I, Crane S, McCartney S, et al. Factors affecting response to National Early Warning Score (NEWS). Resuscitation, 2015, 90: 85-90.
15.	林少虹, 林月双, 李春容, 等. MEWS病情自动预警系统的开发与应用. 护理学报, 2019, 26(16): 76-78.
16.	Zimlichman E, Szyper-Kravitz M, Shinar Z, et al. Early recognition of acutely deteriorating patients in non-intensive care units: assessment of an innovative monitoring technology. J Hosp Med, 2012, 7(8): 628-633.
17.	Churpek MM, Adhikari R, Edelson DP. The value of vital sign trends for detecting clinical deterioration on the wards. Resuscitation, 2016, 102: 1-5.
18.	Dezman ZD, Hu E, Hu PF, et al. Computer modelling using prehospital vitals predicts transfusion and mortality. Prehosp Emerg Care, 2016, 20(5): 609-614.
19.	Elvekjaer M, Aasvang EK, Olsen RM, et al. Physiological abnormalities in patients admitted with acute exacerbation of COPD: an observational study with continuous monitoring. J Clin Monit Comput, 2020, 34(5): 1051-1060.
20.	Bleyer AJ, Vidya S, Russell GB, et al. Longitudinal analysis of one million vital signs in patients in an academic medical center. Resuscitation, 2011, 82(11): 1387-1392.
21.	Churpek MM, Yuen TC, Winslow C, et al. Multicenter development and validation of a risk stratification tool for ward patients. Am J Respir Crit Care Med, 2014, 190(6): 649-655.
22.	刘艺聪, 张婷婷, 张紫君, 等. 标准化护理语言在电子健康记录中的应用现状. 中国实用护理杂志, 2019, 35(9): 717-721.
23.	Al-Jaghbeer M, Dealmeida D, Bilderback A, et al. Clinical decision support for in-hospital AKI. J Am Soc Nephrol, 2018, 29(2): 654-660.
24.	Taylor RA, Pare JR, Venkatesh AK, et al. Prediction of in-hospital mortality in emergency department patients with sepsis: a local big data-driven, machine learning approach. Acad Emerg Med, 2016, 23(3): 269-278.
25.	Ong ME, Lee Ng CH, Goh K, et al. Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability compared with the modified early warning score. Crit Care, 2012, 16(3): R108.
26.	Dziadzko MA, Novotny PJ, Sloan J, et al. Multicenter derivation and validation of an early warning score for acute respiratory failure or death in the hospital. Crit Care, 2018, 22(1): 286.
27.	严律南, 杨家印. 人工智能肝癌临床决策支持系统的开放、验证和应用价值. 中国普外基础与临床杂志, 2020, 27(9): 1052-1056.
28.	刘志华, 曹晓均, 李庆丰, 等. 基于电子病历应用模型标准的医院信息化建设实践. 中国医疗设备, 2017, 32(6): 148-151.
29.	Castillo RS, Kelemen A. Considerations for a successful clinical decision support system. Comput Inform Nurs, 2013, 31(7): 319-326.
30.	洪建, 张洁, 陶敏, 等. 新型临床决策支持系统在智慧医院建设中的应用研究. 中国数字医学, 2019, 14(9): 22-24.
31.	林深, 郑哲. 分析面部图片预测冠心病:人工智能开启疾病筛查新时代. 中国胸心血管外科临床杂志, 2020, 27(11): 1262-1264.
32.	李芊, 周逸菲, 李峥艳, 等. 基于CT的直肠癌新辅助化疗后病理完全缓解预测模型的初步探索—DACCA数据库的联合研究. 中国普外基础与临床杂志, 2020, 27(5): 606-611.
33.	Nemati S, Holder A, Razmi F, et al. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med, 2018, 46(4): 547-553.
34.	胥婷, 崔文彬, 于广军. 我国智慧医院建设现状及发展路径. 中国医院, 2020, 24(3): 1-3.
35.	王修来, 曹阳. 移动数字医院研究. 北京: 中国人事出版社, 2016: 203-213.
36.	Yao Q, Wang M, Chen Y, et al. Taking human out of learning applications: a survey on automated machine learning. arXiv: 1810.13306v4[cs.AI] 16 Dec 2019. https://arxiv.org/pdf/1810.13306.pdf.
37.	Waring J, Lindvall C, Umeton R. Automated machine learning: review of the state-of-the-art and opportunities for healthcare. Artif Intell Med, 2020, 104: 101822.
38.	Alaa AM, Bolton T, Di Angelantonio E, et al. Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423 604 UK Biobank participants. PLoS One, 2019, 14(5): e0213653.
39.	Alaa AM, van der Schaar M. Prognostication and risk factors for cystic fibrosis via automated machine learning. Sci Rep, 2018, 8(1): 11242.
40.	Luo G. MLBCD: a machine learning tool for big clinical data. Health Inf Sci Syst, 2015, 3: 3.
41.	Luo G. PredicT-ML: a tool for automating machine learning model building with big clinical data. Health Inf Sci Syst, 2016, 4: 5.

1. Andersen LW, Kim WY, Chase M, et al. The prevalence and significance of abnormal vital signs prior to in-hospital cardiac arrest. Resuscitation, 2016, 98: 112-117.
2. Cherry PG, Jones CP. Attitudes of nursing staff towards a modified early warning system. Br J Nurs, 2015, 24(16): 812-818.
3. Nightgale-EU. Nightingale: Levels of sophistication in early warning. Available from: https://www.nightingale-h2020.eu/industry/presentations-our-omc-meetings.
4. Dursun Ergezen F, Kol E. Nurses’ responses to monitor alarms in an intensive care unit: An observational study. Intensive Crit Care Nurs, 2020, 59: 102845.
5. 刘凤春, 孟茜, 梁宝娟, 等. 应用“应力环”降低心电监护仪误报警率的效果评价. 中国实用护理杂志, 2020, 36(17): 1319-1322.
6. 王娜, 李龙倜, 徐圣康, 等. 重症监护室护士医疗设备报警疲劳的现状及其影响因素分析. 现代临床护理, 2018, 17(12): 11-16.
7. Nix M. Combating alarm fatigue. Am J Nur, 2015, 115(2): 16.
8. ECRI. Top 10 technology health hazards for 2020: Solutions Kit. 2019-10-29. https://www.ecri.org/search-results/member-preview/hdjournal/pages/2020_top_10_hazards/.
9. Arnold J, Davis A, Fischhoff B, et al. Comparing the predictive ability of a commercial artificial intelligence early warning system with physician judgement for clinical deterioration in hospitalised general internal medicine patients: a prospective observational study. BMJ Open, 2019, 9(10): e032187.
10. Uppanisakorn S, Bhurayanontachai R, Boonyarat J, et al. National Early Warning Score (NEWS) at ICU discharge can predict early clinical deterioration after ICU transfer. J Crit Care, 2018, 43: 225-229.
11. Bian Y, Xu F, Lv RJ, et al. An early warning scoring system for the prevention of acute heart failure. Int J Cardiol, 2015, 183: 111-116.
12. 李红, 吴婷婷, 穆艳. 急性冠状动脉综合征患者心搏骤停前24 h早期预警评分变化趋势的研究. 中华护理杂志, 2019, 54(1): 14-18.
13. Alam N, Hobbelink EL, van Tienhoven AJ, et al. The impact of the use of the Early Warning Score (EWS) on patient outcomes: a systematic review. Resuscitation, 2014, 85(5): 587-594.
14. Kolic I, Crane S, McCartney S, et al. Factors affecting response to National Early Warning Score (NEWS). Resuscitation, 2015, 90: 85-90.
15. 林少虹, 林月双, 李春容, 等. MEWS病情自动预警系统的开发与应用. 护理学报, 2019, 26(16): 76-78.
16. Zimlichman E, Szyper-Kravitz M, Shinar Z, et al. Early recognition of acutely deteriorating patients in non-intensive care units: assessment of an innovative monitoring technology. J Hosp Med, 2012, 7(8): 628-633.
17. Churpek MM, Adhikari R, Edelson DP. The value of vital sign trends for detecting clinical deterioration on the wards. Resuscitation, 2016, 102: 1-5.
18. Dezman ZD, Hu E, Hu PF, et al. Computer modelling using prehospital vitals predicts transfusion and mortality. Prehosp Emerg Care, 2016, 20(5): 609-614.
19. Elvekjaer M, Aasvang EK, Olsen RM, et al. Physiological abnormalities in patients admitted with acute exacerbation of COPD: an observational study with continuous monitoring. J Clin Monit Comput, 2020, 34(5): 1051-1060.
20. Bleyer AJ, Vidya S, Russell GB, et al. Longitudinal analysis of one million vital signs in patients in an academic medical center. Resuscitation, 2011, 82(11): 1387-1392.
21. Churpek MM, Yuen TC, Winslow C, et al. Multicenter development and validation of a risk stratification tool for ward patients. Am J Respir Crit Care Med, 2014, 190(6): 649-655.
22. 刘艺聪, 张婷婷, 张紫君, 等. 标准化护理语言在电子健康记录中的应用现状. 中国实用护理杂志, 2019, 35(9): 717-721.
23. Al-Jaghbeer M, Dealmeida D, Bilderback A, et al. Clinical decision support for in-hospital AKI. J Am Soc Nephrol, 2018, 29(2): 654-660.
24. Taylor RA, Pare JR, Venkatesh AK, et al. Prediction of in-hospital mortality in emergency department patients with sepsis: a local big data-driven, machine learning approach. Acad Emerg Med, 2016, 23(3): 269-278.
25. Ong ME, Lee Ng CH, Goh K, et al. Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability compared with the modified early warning score. Crit Care, 2012, 16(3): R108.
26. Dziadzko MA, Novotny PJ, Sloan J, et al. Multicenter derivation and validation of an early warning score for acute respiratory failure or death in the hospital. Crit Care, 2018, 22(1): 286.
27. 严律南, 杨家印. 人工智能肝癌临床决策支持系统的开放、验证和应用价值. 中国普外基础与临床杂志, 2020, 27(9): 1052-1056.
28. 刘志华, 曹晓均, 李庆丰, 等. 基于电子病历应用模型标准的医院信息化建设实践. 中国医疗设备, 2017, 32(6): 148-151.
29. Castillo RS, Kelemen A. Considerations for a successful clinical decision support system. Comput Inform Nurs, 2013, 31(7): 319-326.
30. 洪建, 张洁, 陶敏, 等. 新型临床决策支持系统在智慧医院建设中的应用研究. 中国数字医学, 2019, 14(9): 22-24.
31. 林深, 郑哲. 分析面部图片预测冠心病:人工智能开启疾病筛查新时代. 中国胸心血管外科临床杂志, 2020, 27(11): 1262-1264.
32. 李芊, 周逸菲, 李峥艳, 等. 基于CT的直肠癌新辅助化疗后病理完全缓解预测模型的初步探索—DACCA数据库的联合研究. 中国普外基础与临床杂志, 2020, 27(5): 606-611.
33. Nemati S, Holder A, Razmi F, et al. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med, 2018, 46(4): 547-553.
34. 胥婷, 崔文彬, 于广军. 我国智慧医院建设现状及发展路径. 中国医院, 2020, 24(3): 1-3.
35. 王修来, 曹阳. 移动数字医院研究. 北京: 中国人事出版社, 2016: 203-213.
36. Yao Q, Wang M, Chen Y, et al. Taking human out of learning applications: a survey on automated machine learning. arXiv: 1810.13306v4[cs.AI] 16 Dec 2019. https://arxiv.org/pdf/1810.13306.pdf.
37. Waring J, Lindvall C, Umeton R. Automated machine learning: review of the state-of-the-art and opportunities for healthcare. Artif Intell Med, 2020, 104: 101822.
38. Alaa AM, Bolton T, Di Angelantonio E, et al. Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423 604 UK Biobank participants. PLoS One, 2019, 14(5): e0213653.
39. Alaa AM, van der Schaar M. Prognostication and risk factors for cystic fibrosis via automated machine learning. Sci Rep, 2018, 8(1): 11242.
40. Luo G. MLBCD: a machine learning tool for big clinical data. Health Inf Sci Syst, 2015, 3: 3.
41. Luo G. PredicT-ML: a tool for automating machine learning model building with big clinical data. Health Inf Sci Syst, 2016, 4: 5.

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Research progress of patient intelligent monitoring and risk warning

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