Abstract
In this paper, we present two approaches to generate prognosis from general blood test results. The first approach is a knowledge-based approach using ripple-down rules (RDR). The knowledge-based approach with RDR converts knowledge of pathologists into a knowledge base with the minimum intervention of knowledge engineers. The second approach is a machine-learning(ML)-based approach using decision tree, random forest and deep neural network (DNN). The ML-based approach learns patterns of attributes from various cases of general blood test. Our experimental results show that there are indeed some important patterns of the attributes in general blood test results, and they are adequately encoded by the both approaches.
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References
Foster, D., McGregor, C., El-Masri, S.: A survey of agent-based intelligent decision support systems to support clinical management and research. In: the 2nd International Workshop on Multi-Agent Systems for Medicine, Computational Biology, and Bioinformatics, pp. 16–34 (2005)
Abbasi, M.M., Kashiyarndi, S.: Clinical Decision Support Systems: A discussion on different methodologies used in Health Care. Marlaedalen University, Sweden (2006)
Smith, A.E., Nugent, C.D., McClean, S.I.: Implementation of intelligent decision support systems in health care. J. Manage. Med. 16(2), 206–218 (2002)
Jao, C.S., Hier, D.B.: Clinical decision support systems: An effective pathway to reduce medical errors and improve patient safety. INTECH Open Access Publisher (2010)
Rokach, L., Maimon, O.: Data Mining with Decision Trees: Theory and Applications. World Scientific, River Edge (2014)
Harold, C.S., Michael, C., Higgins, D.K.: Owens: Medical Decision Making, 2nd edn. Wiley, Chichester (2013)
Moses, A.J., Lieberman, M., Kittay, I., Learreta, J.A.: Computer-aided diagnoses of chronic head pain: explanation, study data, implications, and challenges. J. Craniomandibular Sleep Pract. 24(1), 60–66 (2006). Taylor & Francis
Aljaaf, A.J., Al-Jumeily, D., Hussain, A.J., Fergus, P., Al-Jumaily, M., Abdel-Aziz, K.: Toward an optimal use of artificial intelligence techniques within a clinical decision support system. In: Science and Information Conference(SAI), pp. 548–554. IEEE (2015)
Ye, Y., Diao, X., Jiang, Z., Du, G.: A knowledge-based variance management system for supporting the implementation of clinical pathways. In: International Conference on Management and Service Science, MASS 2009, pp. 1–4, IEEE (2009)
Compton, P., Edwards, G., Kang, B., Lazarus, L., Malor, R., Menzies, T., Sammut, C.: Ripple down rules: possibilities and limitations. In: the 6th AAAI Knowledge Acquisition for Knowledge-Based Systems Workshop, Calgary, Canada, University of Calgary, pp. 6–1. AAAI (1991)
Kang, B., Compton, P., Preston, P.: Multiple classification ripple down rules: evaluation and possibilities. In: the 9th Knowledge Acquisition for Knowledge Based Systems Workshop (1995)
Gaines, B.R., Compton, P.: Induction of ripple-down rules applied to modeling large databases. J. Intell. Inform. Syst. 5(3), 211–228 (1995). Springer
Berner, E.S.: Clinical Decision Support Systems, pp. 3–22. Springer Science + Business Media, New York (2007)
Sorower, M.S.: A Literature Survey on Algorithms for Multi-label Learning. Oregon State University, Corvallis (2010)
Godbole, S., Sarawagi, S.: Discriminative methods for multi-labeled classification. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 22–30. Springer, Heidelberg (2004). doi:10.1007/978-3-540-24775-3_5
Acknowledgments
This work was supported by the Industrial Strategic Technology Development Program, 10052955, Experiential Knowledge Platform Development Research for the Acquisition and Utilization of Field Expert Knowledge, funded by the Ministry of Trade, Industry & Energy (MOTIE), Korea.
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Kim, Y., Hyeon, J., Oh, KJ., Choi, HJ. (2016). Medical Prognosis Generation from General Blood Test Results Using Knowledge-Based and Machine-Learning-Based Approaches. In: Kang, B.H., Bai, Q. (eds) AI 2016: Advances in Artificial Intelligence. AI 2016. Lecture Notes in Computer Science(), vol 9992. Springer, Cham. https://doi.org/10.1007/978-3-319-50127-7_10
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DOI: https://doi.org/10.1007/978-3-319-50127-7_10
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