Skip to main content
SpringerLink
Log in

A hybrid computational approach to process real-time streaming multi-sources data and improve classification for emergency patients triage services: moving forward to an efficient IoMT-based real-time telemedicine systems

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In the Internet of Medical Things (IoMT)-based real-time telemedicine systems, patients can utilize a wide range of medical devices and sensors, which leads to the continuous generation of massive amounts of data. The high speed of data generation poses challenges in collecting, organizing, processing, and making decisions about patients’ emergency levels. Existing methods for classifying (triaging) patients in such environments often yield inaccurate triage levels, necessitating a computational approach to enhance accuracy. This research aims to handle data from multiple heterogeneous sources in IoMT-based real-time telemedicine systems, analyze the data to accurately triage patients with the most urgent cases, and provide swift healthcare services. The proposed solution, the Data Processing with Triaging Model (DPTM), employs a hybrid approach that combines principal component analysis and decision tree algorithms. The model was tested using 55,680 patients with two chronic diseases: heart disease and hypertension. The computational results demonstrate the promising effectiveness of DPTM in accommodating and managing the requests of 55,680 patients. The proposed system achieves an impressive accuracy of 93%, surpassing four other algorithms. In conclusion, DPTM enhances medical services, reduces hospital overcrowding, and ensures accurate services for all patients, with a 93% accuracy rate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Algorithm 1
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

The data used in this paper are available from the corresponding author upon request.

References

  1. Galvan P et al (2018) PP155 telemedicine enhance universal coverage of diagnostic services. Int J Technol Assess Health Care 34(S1):127

    Article  Google Scholar 

  2. França RP, Iano Y, Monteiro ACB,Arthur R (2020) Potential proposal to improve data transmission in healthcare systems. in Deep Learning Techniques for Biomedical and Health Informatics, Elsevier, pp. 267–283

  3. Abdalkareem ZA, Amir A, Al-Betar MA, Ekhan P, Hammouri AI (2021) Healthcare scheduling in optimization context: a review. Health Technol (Berl) 11(3):445–469. https://doi.org/10.1007/s12553-021-00547-5

    Article  Google Scholar 

  4. Albahri AHAOS, Alsattar AAZHA, Albahri BBZAS (2023) Hospital selection framework for remote MCD patients based on fuzzy q-rung orthopair environment. Neural Comput Appl 35(8):6185–6196. https://doi.org/10.1007/s00521-022-07998-5

    Article  Google Scholar 

  5. Salman OH, Rasid MFA, Saripan MI, Subramaniam SK (2014) Multi-sources data fusion framework for remote triage prioritization in telehealth. J Med Syst 38(9):1–23. https://doi.org/10.1007/s10916-014-0103-4

    Article  Google Scholar 

  6. Campbell NRC et al (2021) WHO HEARTS: a global program to reduce cardiovascular disease burden: experience implementing in the Americas and opportunities in Canada. Can J Cardiol 37(5):744–755

    Article  Google Scholar 

  7. Nowbar AN, Gitto M, Howard JP, Francis DP, Al-Lamee R (2019) Mortality from ischemic heart disease: analysis of data from the World Health Organization and coronary artery disease risk factors From NCD Risk Factor Collaboration. Circ Cardiovasc Qual outcomes 12(6):e005375

    Article  Google Scholar 

  8. Ullah SMA, Islam MM, Mahmud S, Nooruddin S, Raju SMTU, Haque MR (2021) Scalable telehealth services to combat novel coronavirus (COVID-19) pandemic. SN Comput Sci 2:1–8

    Article  Google Scholar 

  9. Jamil F, Ahmad S, Iqbal N, Kim D-H (2020) Towards a remote monitoring of patient vital signs based on IoT-based blockchain integrity management platforms in smart hospitals. Sensors 20(8):2195

    Article  Google Scholar 

  10. Salman OH, Aal-Nouman MI, Taha ZK (2020) Reducing waiting time for remote patients in telemedicine with considering treated patients in emergency department based on body sensors technologies and hybrid computational algorithms: toward scalable and efficient real time healthcare monitoring syste. J Biomed Inform. https://doi.org/10.1016/j.jbi.2020.103592

    Article  Google Scholar 

  11. Kalid N et al (2018) Based on real time remote health monitoring systems: a new approach for prioritization ‘large scales data’ patients with chronic heart diseases using body sensors and communication technology. J Med Syst 42(4):1–37

    Article  Google Scholar 

  12. Salman OH, Zaidan AA, Zaidan BB, Naserkalid, Hashim M (2017) Novel methodology for triage and prioritizing using ‘big data’ patients with chronic heart diseases through telemedicine environmental. Int J Inf Technol Decis Mak 16(5):1211–1245. https://doi.org/10.1142/S0219622017500225

    Article  Google Scholar 

  13. Rubí JNS, Gondim PRL (2019) IoMT platform for pervasive healthcare data aggregation, processing, and sharing based on oneM2M and openEHR. Sensors (Switzerland) 19(19):1–25. https://doi.org/10.3390/s19194283

    Article  Google Scholar 

  14. Kalid N, Zaidan AA, Zaidan BB, Salman OH, Hashim M, Muzammil H (2018) Based real time remote health monitoring systems: a review on patients prioritization and related" big data" using body sensors information and communication technology. J Med Syst 42:1–30. https://doi.org/10.1007/s10916-017-0883-4

    Article  Google Scholar 

  15. Mohammed KI et al (2020) A uniform intelligent prioritisation for solving diverse and big data generated from multiple chronic diseases patients based on hybrid decision-making and voting method. IEEE Access 8:91521–91530. https://doi.org/10.1109/ACCESS.2020.2994746

    Article  Google Scholar 

  16. Mohammed KI et al (2020) Novel technique for reorganisation of opinion order to interval levels for solving several instances representing prioritisation in patients with multiple chronic diseases. Comput Methods Programs Biomed 185:105151

    Article  Google Scholar 

  17. Hamid RA, Albahri AS, Albahri OS, Zaidan AA (2021) Dempster–Shafer theory for classification and hybridised models of multi-criteria decision analysis for prioritisation: a telemedicine framework for patients with heart diseases. J Ambient Intell Humaniz Comput 13:4333

    Article  Google Scholar 

  18. Chen A, Zhang X, Zhou Z (2020) Machine learning: Accelerating materials development for energy storage and conversion. InfoMat 2(3):553–576. https://doi.org/10.1002/inf2.12094

    Article  Google Scholar 

  19. Sullivan E (2022) Understanding from machine learning models. Br J Philos Sci 73:1–34

    Article  MathSciNet  Google Scholar 

  20. Salman OH, Taha Z, Alsabah MQ, Hussein YS, Mohammed AS, Aal-Nouman M (2021) A review on utilizing machine learning technology in the fields of electronic emergency triage and patient priority systems in telemedicine: coherent taxonomy, motivations, open research challenges and recommendations for intelligent future work. Comput Methods Programs Biomed. https://doi.org/10.1016/j.cmpb.2021.106357

    Article  Google Scholar 

  21. Mendo IR, Marques G, de la Torre Díez I, López-Coronado M, Martín-Rodríguez F (2021) Machine learning in medical emergencies: a systematic review and analysis. J Med Syst 45(10):88

    Article  Google Scholar 

  22. Astill J, Dara RA, Fraser EDG, Roberts B, Sharif S (2020) Smart poultry management: smart sensors, big data, and the internet of things. Comput Electron Agric 170:105291

    Article  Google Scholar 

  23. Horng S, Sontag DA, Halpern Y, Jernite Y, Shapiro NI, Nathanson LA (2018) Creating an automated trigger for sepsis clinical decision support at emergency department triage using machine learning. PLoS ONE 12(4):e0174708

    Article  Google Scholar 

  24. Chatrati SP et al (2022) Smart home health monitoring system for predicting type 2 diabetes and hypertension. J King Saud Univ Comput Inf Sci 34(3):862–870. https://doi.org/10.1016/j.jksuci.2020.01.010

    Article  Google Scholar 

  25. Bora Ş, Kantarcı A, Erdoğan A, Beynek B, Kheibari B, Evren V (2022) Machine learning for E-triage. Int J Multidiscip Stud Innov Technol. https://doi.org/10.36287/ijmsit.6.1.86

    Article  Google Scholar 

  26. Hassan CAU, Iqbal J, Irfan R, Hussain S, Algarni AD, Bukhari SSH, Alturki N, Ullah SS (2022) Effectively predicting the presence of coronary heart disease using machine learning classifiers. Sensors 22(19):7227

    Article  Google Scholar 

  27. Liu N et al (2014) Prediction of adverse cardiac events in emergency department patients with chest pain using machine learning for variable selection. BMC Med Inform Decis Mak 14:1–9

    Article  Google Scholar 

  28. Patel SJ, Chamberlain DB, Chamberlain JM (2018) A machine learning approach to predicting need for hospitalization for pediatric asthma exacerbation at the time of emergency department triage. Acad Emerg Med 25(12):1463–1470

    Article  Google Scholar 

  29. Kadum SY et al (2023) Machine learning-based telemedicine framework to prioritize remote patients with multi-chronic diseases for emergency healthcare services. Netw Model Anal Heal Informatics Bioinforma 12(1):11. https://doi.org/10.1007/s13721-022-00407-w

    Article  MathSciNet  Google Scholar 

  30. Manickam P et al (2022) Artificial intelligence (AI ) and Internet of Medical Things (IoMT) assisted biomedical systems for intelligent healthcare. Biosensors. https://doi.org/10.3390/bios12080562

    Article  Google Scholar 

  31. Khan MF et al (2021) An IoMT-enabled smart healthcare model to monitor elderly people using machine learning technique. Comput Intell Neurosci. https://doi.org/10.1155/2021/2487759

    Article  Google Scholar 

  32. Seng JKP, Ang KLM (2017) Big feature data analytics: split and combine linear discriminant analysis (SC-LDA) for integration towards decision making analytics. IEEE Access 5(14056):14056–14065. https://doi.org/10.1109/ACCESS.2017.2726543

    Article  Google Scholar 

  33. Ahmed Z, Mohamed K, Zeeshan S, Dong X (2020) Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database 2020:baaa010

    Article  Google Scholar 

  34. Pradeepa P, Jeyakumar MK (2022) Modelling of IDBN with LSNN based optimal feature selection for the prediction of CKD using real time data. Multimed Tools Appl. https://doi.org/10.1007/s11042-022-13561-0

    Article  Google Scholar 

  35. Cao H, Wachowicz M (2020) A holistic overview of anticipatory learning for the internet of moving things: research challenges and opportunities. ISPRS Int J Geo-Inf. https://doi.org/10.3390/ijgi9040272

    Article  Google Scholar 

  36. Jin X, Liu C, Xu T, Su L, Zhang X (2020) Artificial intelligence biosensors: challenges and prospects. Biosens Bioelectron. https://doi.org/10.1016/j.bios.2020.112412

    Article  Google Scholar 

  37. Gomatheeshwari B, Selvakumar J (2020) Appropriate allocation of workloads on performance asymmetric multicore architectures via deep learning algorithms. Microprocess Microsyst 73:102996. https://doi.org/10.1016/j.micpro.2020.102996

    Article  Google Scholar 

  38. Reddy GT, Reddy MPK, Lakshmanna K, Kaluri R, Rajput DS (2020) Analysis of dimensionality reduction techniques on big data. IEEE Access 8:54776–54788. https://doi.org/10.1109/ACCESS.2020.2980942

    Article  Google Scholar 

  39. Raptis TP, Passarella A (2023) A survey on networked data streaming with Apache Kafka. IEEE Access 11(August):85333–85350. https://doi.org/10.1109/ACCESS.2023.3303810

    Article  Google Scholar 

  40. Salman OH, Aal-nouman MI, Taha ZK, Alsabah MQ, Hussein YS, Abdelkareem ZA (2021) Formulating multi diseases dataset for identifying, triaging and prioritizing patients to multi medical emergency levels: simulated dataset accompanied with codes. Data Br 34:106576. https://doi.org/10.1016/j.dib.2020.106576

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank all the reviewers for the helpful comments and suggestions they gave us while we were planning and making this research. Their generosity with their time is very much appreciated. In addition, the authors would like to thank the doctors who provide the validation for the data generated.

Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310, Johor Bahru, Johor, Malaysia

    Omar Sadeq Salman, Nurul Mu’azzah Abdul Latiff & Sharifah Hafizah Syed Ariffin

  2. Network Department, Faculty of Engineering, AL Iraqia University, Baghdad, Iraq

    Omar H. Salman

Authors
  1. Omar Sadeq Salman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nurul Mu’azzah Abdul Latiff
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Omar H. Salman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sharifah Hafizah Syed Ariffin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Omar Sadeq Salman contributed to investigation, writing—original draft; Nurul Mu’azzah Abdul Latiff contributed to conceptualization, methodology, project administration, and review; Omar. H. Salman contributed to conceptualization, methodology, supervision, review, and editing; Sharifah Hafizah Syed Arifin contributed to conceptualization, investigation, review, and editing.

Corresponding author

Correspondence to Omar Sadeq Salman.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Salman, O.S., Abdul Latiff, N.M., Salman, O.H. et al. A hybrid computational approach to process real-time streaming multi-sources data and improve classification for emergency patients triage services: moving forward to an efficient IoMT-based real-time telemedicine systems. Neural Comput & Applic 36, 10109–10122 (2024). https://doi.org/10.1007/s00521-024-09600-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-024-09600-6

Keywords

Search

Navigation