Abstract
The state of the art of the Medical Diagnosis Systems (MDSs) has demonstrated an exciting advancement in recent years. Clearly, the success of these systems is very much dependent on the quality of their input, however, so far no computer-aided decision support system has been introduced to address this issue. Such a system should be capable of performing the Differential Diagnosis (DDx) process, in which upon receiving the chief compliant, some potential diagnoses are considered, according to which enough evidence and supporting information will be gathered in order to shrink the probability of the other candidates. This paper shows that DDx domain is a holonic domain, and hence, this process can be implemented using a holonic multi-agent based MDS.
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References
IBM Watson Health: Available https://www.ibm.com/watson/health/
Fisher, H., Tomlinson, A., Ramnarayan, P., Britto, J.: ISABEL: support with clinical decision making. Pediatr. Nurs. 15(7), 34–35 (2003)
Merriam-Webster: Differential Diagnosis. https://www.merriam-webster.com/dictionary/differential%20diagnosis
Segen, J.: Concise Dictionary of Modern Medicine. McGraw-Hill, New York (2006)
Akbari, Z., Unland, R.: A holonic multi-agent system approach to differential diagnosis. In: Berndt, J., Unland, R. (eds.) Multiagent System Technologies. MATES 2017. LNCS, vol. 10413, pp. 272–290 (2017)
Akbari, Z., Unland, R.: A holonic multi-agent based diagnostic decision support system for computer-aided history and physical examination. In: Submitted to the 16th International Conference on Practical Applications of Agents and Multi-Agent Systems (2018)
Koestler, A.: The Gost in the Machine. Hutchinson, London (1967)
Gerber, C., Siekmann, J., Vierke, G.: Holonic Multi-Agent Systems, Technical report DFKI-RR-99-03. German Research Centre for Artificial Intelligence (1999)
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Simoudis, E., Han, J., Fayyad, U. (eds.) The 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)
Akbari, Z., Unland, R.: Automated determination of the input parameter of the dbscan based on outlier detection. In: Artificial Intelligence Applications and Innovations. IFIP Advances in Information and Communication Technology, vol. 475, pp. 280–291 (2016)
NIST/SEMATECH e-Handbook of Statistical Methods. http://www.itl.nist.gov/div898/handbook/. Accessed Mar 2018
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Akbari, Z., Unland, R. (2019). Towards Computer-Aided Differential Diagnosis: A Holonic Multi-agent Based Medical Diagnosis System. In: Rodríguez, S., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-319-99608-0_46
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DOI: https://doi.org/10.1007/978-3-319-99608-0_46
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