Skip to main content

2021 | OriginalPaper | Buchkapitel

2AI&7D Model of Resistomics to Counter the Accelerating Antibiotic Resistance and the Medical Climate Crisis

verfasst von : Asoke K. Talukder, Prantar Chakrabarti, Bhaskar Narayan Chaudhuri, Tavpritesh Sethi, Rakesh Lodha, Roland E. Haas

Erschienen in: Big Data Analytics

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The antimicrobial resistance (AMR) crisis is referred to as ‘Medical Climate Crisis’. Inappropriate use of antimicrobial drugs is driving the resistance evolution in pathogenic microorganisms. In 2014 it was estimated that by 2050 more people will die due to antimicrobial resistance compared to cancer. It will cause a reduction of 2% to 3.5% in Gross Domestic Product (GDP) and cost the world up to 100 trillion USD. The indiscriminate use of antibiotics for COVID-19 patients has accelerated the resistance rate. COVID-19 reduced the window of opportunity for the fight against AMR. This man-made crisis can only be averted through accurate actionable antibiotic knowledge, usage, and a knowledge driven Resistomics. In this paper, we present the 2AI (Artificial Intelligence and Augmented Intelligence) and 7D (right Diagnosis, right Disease-causing-agent, right Drug, right Dose, right Duration, right Documentation, and De-escalation) model of antibiotic stewardship. The resistance related integrated knowledge of resistomics is stored as a knowledge graph in a Neo4j properties graph database for 24 × 7 access. This actionable knowledge is made available through smartphones and the Web as a Progressive Web Applications (PWA). The 2AI&7D Model delivers the right knowledge at the right time to the specialists and non-specialist alike at the point-of-action (Stewardship committee, Smart Clinic, and Smart Hospital) and then delivers the actionable accurate knowledge to the healthcare provider at the point-of-care in realtime.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
4.
Zurück zum Zitat Talukder, A.K., Schriml, L., Ghosh, A., Biswas, R., Chakrabarti, P., Haas, R.E.: Diseasomics: Actionable Machine Interpretable Disease Knowledge at the Point-of-Care, Submitted (2021) Talukder, A.K., Schriml, L., Ghosh, A., Biswas, R., Chakrabarti, P., Haas, R.E.: Diseasomics: Actionable Machine Interpretable Disease Knowledge at the Point-of-Care, Submitted (2021)
5.
Zurück zum Zitat Talukder, A.K., Haas, R.E.: AIoT: AI meets IoT and web in smart healthcare. In: 13th ACM Web Science Conference 2021 (WebSci ’21 Companion), June 21–25, 2021, Virtual Event, United Kingdom (2021) Talukder, A.K., Haas, R.E.: AIoT: AI meets IoT and web in smart healthcare. In: 13th ACM Web Science Conference 2021 (WebSci ’21 Companion), June 21–25, 2021, Virtual Event, United Kingdom (2021)
8.
Zurück zum Zitat Kuper, K.M., Nagel, J.L., Kile, J.W., May, L.S., Lee, F.M.: The role of electronic health record and “add-on” clinical decision support systems to enhance antimicrobial stewardship programs. Infect. Control Hosp. Epidemiol. 40(5), 501–511 (2019). https://doi.org/10.1017/ice.2019.51. Epub 2019 Apr 25. PMID: 31020944 Kuper, K.M., Nagel, J.L., Kile, J.W., May, L.S., Lee, F.M.: The role of electronic health record and “add-on” clinical decision support systems to enhance antimicrobial stewardship programs. Infect. Control Hosp. Epidemiol. 40(5), 501–511 (2019). https://​doi.​org/​10.​1017/​ice.​2019.​51. Epub 2019 Apr 25. PMID: 31020944
9.
Zurück zum Zitat Dengb, J.L.S., Zhang, L.: A review of artificial intelligence applications for antimicrobial resistance. Biosafety and Health (Available online 11 August 2020) (2020) Dengb, J.L.S., Zhang, L.: A review of artificial intelligence applications for antimicrobial resistance. Biosafety and Health (Available online 11 August 2020) (2020)
13.
Zurück zum Zitat Institute of Medicine: Crossing the Quality Chasm: A New Health System for the 21st Century. National Academy Press, Washington, D.C. (2001) Institute of Medicine: Crossing the Quality Chasm: A New Health System for the 21st Century. National Academy Press, Washington, D.C. (2001)
15.
Zurück zum Zitat Timo, J.T. Koski, J.N.: A review of bayesian networks and structure learning. 40(1), 51–103 (2012) Timo, J.T. Koski, J.N.: A review of bayesian networks and structure learning. 40(1), 51–103 (2012)
16.
Zurück zum Zitat Sethi, T., Maheshwari, S., Nagori, A., Lodha, R.: Stewarding antibiotic stewardship in intensive care units with Bayesian artificial intelligence [version 1; referees: awaiting peer review], Welcome Open Research 2018, 3:73 Last updated: 18 JUN 2018 (2018) Sethi, T., Maheshwari, S., Nagori, A., Lodha, R.: Stewarding antibiotic stewardship in intensive care units with Bayesian artificial intelligence [version 1; referees: awaiting peer review], Welcome Open Research 2018, 3:73 Last updated: 18 JUN 2018 (2018)
18.
Zurück zum Zitat Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. 2016. Knowledge Discovery and Data Mining (2016) Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. 2016. Knowledge Discovery and Data Mining (2016)
19.
Zurück zum Zitat Scutari, M.: Learning Bayesian networks with the bnlearn R package. J. Stat. Softw. 35(3), 1–22 (2010)CrossRef Scutari, M.: Learning Bayesian networks with the bnlearn R package. J. Stat. Softw. 35(3), 1–22 (2010)CrossRef
20.
Zurück zum Zitat Su, C., Andrew, A., Karagas M.R., Borsuk, M.E.: Using Bayesian networks to discover relations between genes, environment, and disease. BioData Mining 6, 6. (2013) Su, C., Andrew, A., Karagas M.R., Borsuk, M.E.: Using Bayesian networks to discover relations between genes, environment, and disease. BioData Mining 6, 6. (2013)
21.
Zurück zum Zitat Pearl, J.: The Do-Calculus revisited. In: de Freitas, N., Murphy, K. (eds.), Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, Corvallis, OR, AUAI Press, 4–11 (2012) Pearl, J.: The Do-Calculus revisited. In: de Freitas, N., Murphy, K. (eds.), Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, Corvallis, OR, AUAI Press, 4–11 (2012)
22.
Zurück zum Zitat Bakhit, M., Hoffmann, T., Scott, A.M., et al.: Resistance decay in individuals after antibiotic exposure in primary care: a systematic review and meta-analysis. BMC Med 16, 126 (2018)CrossRef Bakhit, M., Hoffmann, T., Scott, A.M., et al.: Resistance decay in individuals after antibiotic exposure in primary care: a systematic review and meta-analysis. BMC Med 16, 126 (2018)CrossRef
25.
Zurück zum Zitat Talukder, A.K., Sanz, J.B., Samajpati, J.: ‘Precision health’: balancing reactive care and proactive care through the evidence based knowledge graph constructed from real-world electronic health records, disease trajectories, diseasome, and patholome. In: Bellatreche, L., Goyal, V., Fujita, H., Mondal, A., Reddy, P.K. (eds.) BDA 2020. LNCS, vol. 12581, pp. 113–133. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66665-1_9CrossRef Talukder, A.K., Sanz, J.B., Samajpati, J.: ‘Precision health’: balancing reactive care and proactive care through the evidence based knowledge graph constructed from real-world electronic health records, disease trajectories, diseasome, and patholome. In: Bellatreche, L., Goyal, V., Fujita, H., Mondal, A., Reddy, P.K. (eds.) BDA 2020. LNCS, vol. 12581, pp. 113–133. Springer, Cham (2020). https://​doi.​org/​10.​1007/​978-3-030-66665-1_​9CrossRef
Metadaten
Titel
2AI&7D Model of Resistomics to Counter the Accelerating Antibiotic Resistance and the Medical Climate Crisis
verfasst von
Asoke K. Talukder
Prantar Chakrabarti
Bhaskar Narayan Chaudhuri
Tavpritesh Sethi
Rakesh Lodha
Roland E. Haas
Copyright-Jahr
2021
DOI
https://doi.org/10.1007/978-3-030-93620-4_4

Premium Partner