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.	劉鳳春, 孟茜, 梁寶娟, 等. 應用“應力環”降低心電監護儀誤報警率的效果評價. 中國實用護理雜志, 2020, 36(17): 1319-1322.
6.	王娜, 李龍倜, 徐圣康, 等. 重癥監護室護士醫療設備報警疲勞的現狀及其影響因素分析. 現代臨床護理, 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.	李紅, 吳婷婷, 穆艷. 急性冠狀動脈綜合征患者心搏驟停前24 h早期預警評分變化趨勢的研究. 中華護理雜志, 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, 林月雙, 李春容, 等. MEWS病情自動預警系統的開發與應用. 護理學報, 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.	劉藝聰, 張婷婷, 張紫君, 等. 標準化護理語言在電子健康記錄中的應用現狀. 中國實用護理雜志, 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.	嚴律南, 楊家印. 人工智能肝癌臨床決策支持系統的開放、驗證和應用價值. 中國普外基礎與臨床雜志, 2020, 27(9): 1052-1056.
28.	劉志華, 曹曉均, 李慶豐, 等. 基于電子病歷應用模型標準的醫院信息化建設實踐. 中國醫療設備, 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, 張潔, 陶敏, 等. 新型臨床決策支持系統在智慧醫院建設中的應用研究. 中國數字醫學, 2019, 14(9): 22-24.
31.	林深, 鄭哲. 分析面部圖片預測冠心病:人工智能開啟疾病篩查新時代. 中國胸心血管外科臨床雜志, 2020, 27(11): 1262-1264.
32.	李芊, 周逸菲, 李崢艷, 等. 基于CT的直腸癌新輔助化療后病理完全緩解預測模型的初步探索—DACCA數據庫的聯合研究. 中國普外基礎與臨床雜志, 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, 于廣軍. 我國智慧醫院建設現狀及發展路徑. 中國醫院, 2020, 24(3): 1-3.
35.	王修來, 曹陽. 移動數字醫院研究. 北京: 中國人事出版社, 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. 劉鳳春, 孟茜, 梁寶娟, 等. 應用“應力環”降低心電監護儀誤報警率的效果評價. 中國實用護理雜志, 2020, 36(17): 1319-1322.
6. 王娜, 李龍倜, 徐圣康, 等. 重癥監護室護士醫療設備報警疲勞的現狀及其影響因素分析. 現代臨床護理, 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. 李紅, 吳婷婷, 穆艷. 急性冠狀動脈綜合征患者心搏驟停前24 h早期預警評分變化趨勢的研究. 中華護理雜志, 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, 林月雙, 李春容, 等. MEWS病情自動預警系統的開發與應用. 護理學報, 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. 劉藝聰, 張婷婷, 張紫君, 等. 標準化護理語言在電子健康記錄中的應用現狀. 中國實用護理雜志, 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. 嚴律南, 楊家印. 人工智能肝癌臨床決策支持系統的開放、驗證和應用價值. 中國普外基礎與臨床雜志, 2020, 27(9): 1052-1056.
28. 劉志華, 曹曉均, 李慶豐, 等. 基于電子病歷應用模型標準的醫院信息化建設實踐. 中國醫療設備, 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, 張潔, 陶敏, 等. 新型臨床決策支持系統在智慧醫院建設中的應用研究. 中國數字醫學, 2019, 14(9): 22-24.
31. 林深, 鄭哲. 分析面部圖片預測冠心病:人工智能開啟疾病篩查新時代. 中國胸心血管外科臨床雜志, 2020, 27(11): 1262-1264.
32. 李芊, 周逸菲, 李崢艷, 等. 基于CT的直腸癌新輔助化療后病理完全緩解預測模型的初步探索—DACCA數據庫的聯合研究. 中國普外基礎與臨床雜志, 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, 于廣軍. 我國智慧醫院建設現狀及發展路徑. 中國醫院, 2020, 24(3): 1-3.
35. 王修來, 曹陽. 移動數字醫院研究. 北京: 中國人事出版社, 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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