Journal of Biomedical and Sustainable Healthcare Applications


A Survey on Biomedical Informatics for Computer-Aided Decision Support System



Journal of Biomedical and Sustainable Healthcare Applications

Received On : 10 October 2020

Revised On : 25 December 2020

Accepted On : 06 April 2021

Published On : 05 July 2021

Volume 01, Issue 02

Pages : 086-095


Abstract


Biomedical computing for computer-aided biomedical diagnostics and the decision support system has developed a platform for the biomedical setting during the last few decades. As early as 1971, there were elaborate and basic applications of management information systems driven by biomedical informatics. According to a 1994 assessment, this field's literature stretches back to the 1950s. Medical decision is more challenging than ever for doctors and other caregivers due to the amount and complexity of contemporary patient information. This circumstance necessitates the application of medical computing technologies to evaluate data and formulate suggestions and/or forecasts to aid decision makers. Over the past two decades, healthcare informatics tools, such as computer-aided decision support, have grown indispensable and extensively employed. This article gives a quick overview of such technologies, their productivity applications and methodology, as well as the problems and directions they imply for the future.


Keywords


Clinical Decision Support System (CDSS), Biomedical Computing, Computer-Assisted Medical Diagnosis


  1. G. Rzevski, "Computer-aided design of control systems", Computer-Aided Design, vol. 11, no. 6, p. 363, 1979. Available: 10.1016/0010-4485(79)90043-5.
  2. A. Ciapponi, "Does a mobile clinical decision-support system (CDSS) help improve the quality of primary health care?", Cochrane Clinical Answers, 2021. Available: 10.1002/cca.3745.
  3. S. Ji, R. Smith, T. Huynh and K. Najarian, "A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries", BMC Medical Informatics and Decision Making, vol. 9, no. 1, 2009. Available: 10.1186/1472-6947-9-2.
  4. L. Pinheiro, G. Candore, C. Zaccaria, J. Slattery and P. Arlett, "An algorithm to detect unexpected increases in frequency of reports of adverse events in EudraVigilance", Pharmacoepidemiology and Drug Safety, vol. 27, no. 1, pp. 38-45, 2017. Available: 10.1002/pds.4344.
  5. "Of current interest: Radio-electrocardiograph system for electrocardiograms while patients exercise", Electrical Engineering, vol. 80, no. 7, pp. 556-557, 1961. Available: 10.1109/ee.1961.6433350.
  6. L. Arknæs-Pedersen, "Method and an apparatus for processing an auscultation signal", The Journal of the Acoustical Society of America, vol. 120, no. 2, p. 583, 2006. Available: 10.1121/1.2336707.
  7. N. Sánchez-Pi, J. Carbó and J. Molina, "A knowledge-based system approach for a context-aware system", Knowledge-Based Systems, vol. 27, pp. 1-17, 2012. Available: 10.1016/j.knosys.2011.08.017.
  8. S. Kositbowornchai, S. Siriteptawee, S. Plermkamon, S. Bureerat and D. Chetchotsak, "An Artificial Neural Network for Detection of Simulated Dental Caries", International Journal of Computer Assisted Radiology and Surgery, vol. 1, no. 2, pp. 91-96, 2006. Available: 10.1007/s11548-006-0040-x.
  9. M. Moinet, G. Mandil and P. Serre, "Defining tools to address over-constrained geometric problems in Computer Aided Design", Computer-Aided Design, vol. 48, pp. 42-52, 2014. Available: 10.1016/j.cad.2013.11.002.
  10. A. Azami, "Credit Derivatives: CDSs and Value’s Firms; Effect on Financial Statements", SSRN Electronic Journal, 2011. Available: 10.2139/ssrn.1940716.
  11. A. Segev, "Integrating computer vision with web-based knowledge for medical diagnostic assistance", Expert Systems, vol. 27, no. 4, pp. 247-258, 2010. Available: 10.1111/j.1468-0394.2010.00520.x.
  12. H. Atabakhsh, "A survey of constraint based scheduling systems using an artificial intelligence approach", Artificial Intelligence in Engineering, vol. 6, no. 2, pp. 58-73, 1991. Available: 10.1016/0954-1810(91)90001-5.
  13. G. Campanella and R. Ribeiro, "A framework for dynamic multiple-criteria decision making", Decision Support Systems, vol. 52, no. 1, pp. 52-60, 2011. Available: 10.1016/j.dss.2011.05.003.
  14. A. Bagheri and M. Mardaneh, "Fuzzy logic-based technique for enhancement of kalman filter based PLL", Journal of Intelligent & Fuzzy Systems, vol. 28, no. 3, pp. 1371-1383, 2015. Available: 10.3233/ifs-141421.
  15. J. Cockx, K. Denolf, B. Vanhoof and R. Stahl, "SPRINT: A Tool to Generate Concurrent Transaction-Level Models from Sequential Code", EURASIP Journal on Advances in Signal Processing, vol. 2007, no. 1, 2007. Available: 10.1155/2007/75373.
  16. Y. Malla, "A Machine Learning Approach for Early Prediction of Breast Cancer", International Journal Of Engineering And Computer Science, 2017. Available: 10.18535/ijecs/v6i5.31.
  17. M. Yang, S. Nazir, Q. Xu and S. Ali, "Deep Learning Algorithms and Multicriteria Decision-Making Used in Big Data: A Systematic Literature Review", Complexity, vol. 2020, pp. 1-18, 2020. Available: 10.1155/2020/2836064.
  18. J. Zimmerman and J. Rothmeier, "History and future of MUMPS in medical computing", Medical Informatics, vol. 4, no. 1, pp. 5-11, 1979. Available: 10.3109/14639237909044355.
  19. J. Gero, "Special issue: Artificial intelligence in computer-aided design: Progress and prognosis", Computer-Aided Design, vol. 28, no. 3, pp. 153-154, 1996. Available: 10.1016/0010-4485(96)86821-7.
  20. S. Jagdale, M. Kolekara and U. Khot, "Smart Sensing Using Bayesian Network for Computer Aided Diagnostic Systems", Procedia Computer Science, vol. 45, pp. 762-769, 2015. Available: 10.1016/j.procs.2015.03.150.
  21. D. Ksiądzyna, "Drug-induced acute pancreatitis related to medications commonly used in gastroenterology", European Journal of Internal Medicine, vol. 22, no. 1, pp. 20-25, 2011. Available: 10.1016/j.ejim.2010.09.004.
  22. "Computer Aided Detection of Polyps During Colonoscopy Procedures", Case Medical Research, 2019. Available: 10.31525/ct1-nct04195646.
  23. S. Senarath, "Securitization and Credit Default Swaps (CDSs): Towards Diagnostic of the Fundamental Problem and Suggested Solutions", SSRN Electronic Journal, 2014. Available: 10.2139/ssrn.2478313.
  24. A. Haldorai and A. Ramu, “Canonical Correlation Analysis Based Hyper Basis Feedforward Neural Network Classification for Urban Sustainability,” Neural Processing Letters, Aug. 2020. doi:10.1007/s11063-020-10327-3
  25. D. Devikanniga, A. Ramu, and A. Haldorai, “Efficient Diagnosis of Liver Disease using Support Vector Machine Optimized with Crows Search Algorithm,” EAI Endorsed Transactions on Energy Web, p. 164177, Jul. 2018. doi:10.4108/eai.13-7-2018.164177
  26. H. Anandakumar and K. Umamaheswari, “Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers,” Cluster Computing, vol. 20, no. 2, pp. 1505–1515, Mar. 2017.
  27. H. Anandakumar and K. Umamaheswari, “A bio-inspired swarm intelligence technique for social aware cognitive radio handovers,” Computers & Electrical Engineering, vol. 71, pp. 925–937, Oct. 2018. doi:10.1016/j.compeleceng.2017.09.016
  28. R. Arulmurugan and H. Anandakumar, “Early Detection of Lung Cancer Using Wavelet Feature Descriptor and Feed Forward Back Propagation Neural Networks Classifier,” Lecture Notes in Computational Vision and Biomechanics, pp. 103–110, 2018. doi:10.1007/978-3-319-71767-8_9

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Cite this article


Meilin Gray, “A Survey on Biomedical Informatics for Computer-Aided Decision Support System”, Journal of Biomedical and Sustainable Healthcare Applications, vol.1, no.2, pp. 086-095, July 2021. doi: 10.53759/0088/JBSHA202101011.


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© 2021 Meilin Gray. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.