Predicting Heart Failure. Группа авторов

Читать онлайн.
Название Predicting Heart Failure
Автор произведения Группа авторов
Жанр Медицина
Серия
Издательство Медицина
Год выпуска 0
isbn 9781119813033



Скачать книгу

rel="nofollow" href="#ulink_37f1f656-6486-5622-949a-69514286b3d8">14 Tripoliti, E.E., et al. (2017). Heart Failure: Diagnosis, severity estimation and prediction of adverse events through machine learning techniques. Computational and Structural Biotechnology Journal 15: 26–47.

      15 15 Ledley, R.S.and Lusted, L.B. (1959). Reasoning foundations of medical diagnosis: Symbolic logic, probability, and value theory aid our understanding of how physicians reason. Science 130: 9–21.

      16 16 Shortliffe, E.H.and Sepulveda, M.J. (2018). Clinical decision support in the era of artificial intelligence. JAMA 320: 2199–2200.

      17 17 Jackson, P. (1998). Introduction to Expert Systems (3rd ed.). Addison Wesley.

      18 18 IBM Cloud Education. Machine Learning... https://www.ibm.com/cloud/learn/machine-learning. Access date: 15 July 2020.

      19 19 Yang, J., Wang, Y., Liu, Y., Tang, S., and Chen, W. (2009). Novel approach for 3-D reconstruction of coronary arteries from two uncalibrated angiographic images. IEEE Transactions on Image Processing 18 (7): 1563–1572.

      20 20 SainiSai, S, K., Dewal, M., and Rohit, M. (2011). A fast region-based active contour model for boundary detection of echocardiographic images. Journal of Digital Imaging: The Official Journal of the Society for Computer Applications in Radiology 25: 271–278. doi:10.1007/s10278-011-9408-8.

      21 21 Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., and Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology 69 (2017): 2657–2664.

      22 22 Murdoch, T.B.and Detsky, A.S. (2013). The inevitable application of big data to health care. Journal of the American Medical Association 309 (13): 1351–1352. 2013.

      23 23 Yang, G., Ren, Y., Pan, Q., and Ning, G. (2010). A HF diagnosis model based on support vector machine. IEEE International Conference on Biomedical Engineering and Informatics Vol. 3: 1105–1108.

      24 24 Guidi, G., Iadanza, E., Pettenati, M.C., Milli, M., Pavone, F., and Biffi Gentili, G. (2012). Heart failure artificial intelligence-based computer aided diagnosis telecare system. In: Impact Analysis of Solutions for Chronic Disease Prevention and Management, 7251 (ed. M. Donnelly, C. Paggetti, C. Nugent, and M. Mokhtari), ICOST 2012. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-30779-9_44.

      25 25 Elfadil, N.and Ibrahim, I. (2011). Self organizing neural network approach for identification of patients with congestive HF. International Conference on Multimedia Computing and Systems (ICMCS) 1–6.

      26 26 Gharehchopogh, F.S., Mohammadi, P., and Hakimi, P. (2012). Application of decision tree algorithm for data mining in healthcare operations: A case study. International Journal of Computer Applications (IJCA) 52 (6): 21–26. August 2012.

      27 27 Candelieri, A.and Conforti, D. (2010). A hyper-solution framework for SVM classification: Application for predicting destabilizations in chronic HF patients. The Open Medical Informatics Journal 4: 136–140.

      28 28 Pecchia, L., Melillo, P., and Bracale, M. (2011). Remote health monitoring of HF with data mining via CART method on HRV features. IEEE Transactions on Bio-Medical Engineering 58: 800–804.

      29 29 Quinlan, J.R. (1993). C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann.

      30 30 Joyce, J. (2003). Bayes’ Theorem. In: The Stanford Encyclopedia of Philosophy (Spring 2019 ed.) (ed. E.N. Zalta). Metaphysics Research Lab. Stanford University. Retrieved Jan. 17, 2020.

      31 31 Vapnik, V.N. (1995). The Nature of Statistical Learning Theory. New York, NY, USA: Springer-Verlag.

      32 32 MacQueen, J.B. (1967). Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297. University of California Press.

      33 33 Agrawal, R.and Srikant, R. (1994). Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, 487–499. Santiago, Chile: VLDB. September 1994. .

      34 34 Johnson, K.W., Soto, J.T., Glicksberg, B.S., Shameer, K., Miotto, R., Ali, M., Ashley, E., and Dudley, T.D. (2018). Artificial intelligence in cardiology. Journal of the American College of Cardiology 71: 2668–2679.

      35 35 Yang, G., Ren, Y., Pan, Q., Ning, G., Gong, S., Cai, G., et al. (2010). A heart failure diagnosisodel based on support vector machine. 2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI) 3 (2010): 1105–1108. http://dx.doi.org/10.1109/BMEI16828.2010.

      36 36 Son, C.-S., Kim, Y.-N., Kim, H.-S., Park, H.-S., and Kim, M.-S. (2012). Decision-making model for early diagnosis of congestive HF using rough set and decision tree approaches. Journal of Biomedical Informatics 45 (2012): 999–1008. http://dx.doi.org/10.1016/j.jbi.2012.04.013.

      37 37 Masetic, Z.and Subasi, A. (2016). Congestive heart failure detection using random forest classifier. Computer Methods and Programs in Biomedicine 130 (2016): 54–64. https://doi.org/10.1016/j.cmpb.2016.03.020.

      38 38 Wu, J., Roy, J., and Stewart, W.F. (2010). Prediction modeling using EHR data: Challenges, strategies, and a comparison of machine learning approaches. Medical Care 48 (2010): S106–113. http://dx.doi.org/10.1097/MLR.0b013e3181de9e17.

      39 39 Aljaaf, A.J., Al-Jumeily, D., Hussain, A.J., Dawson, T., Fergus, P., and Al-Jumaily, M. (2015). Predicting the likelihood of HF with a multi-level risk assessment using decision tree. In: Third International Conference on Technological Advances in Electrical. Beirut, Lebanon. 2015.

      40 40 Zheng, Y., Guo, X., Qin, J., and Xiao, S. (2015). Computer-assisted diagnosis for chronic HF by the analysis of their cardiac reserve and heart sound characteristics. Computer Methods and Programs in Biomedicine I22 (2015): 372–383. http://dx.doi.org/10.1016/j.cmpb.2015.09.001.

      41