References
- [1] CARVALHO, D. V. – PEREIRA, E. M. – CARDOSO, J. S.: Machine learning interpretability: A survey on methods and metrics. Electronics, vol. 8, no. 832, 1–34, 2019.10.3390/electronics8080832
- [2] SIMPAO, A. F. – AHUMADA, L. M. – GÁLVEZ, J. A. - REHMAN, M. A: A review of analytics and clinical informatics in health care. Journal of Medical Systems, vol. 38, no. 4, Apr. 2014.10.1007/s10916-014-0045-x24696396
- [3] STIGLIC, G. – KOCBEK, P. – FIJACKO, N. -ZITNIK, M. – VERBERT, K. – CILAR, L.: Interpretability of machine learning based prediction models in healthcare, WIREs Data Mining Knowledge Discovery, vol. 10, no. 5, Jun. 2020.10.1002/widm.1379
- [4] MAJNARIĆ, L.T. – BABIČ, F., O’SULLIVAN, S. – HOLZINGER, A.: AI and Big Data in Healthcare: Towards a More Comprehensive Research Framework for Multimorbidity, Journal of Clinical Medicine, vol. 10, no. 4, 766, Feb. 2021.10.3390/jcm10040766791866833672914
- [5] HUND, M. – BÖHM, D. – STRUM, W. et al.: Visual analytics for concept exploration in subspaces of patient groups, Brain Inf., vol. 3, pp. 233–247, Dec. 2016.10.1007/s40708-016-0043-5510640627747817
- [6] MAJNARIĆ, L.T. – BABIČ, F. – BOSNIC, Z., ZEKIC-SUŠAC, M. – WITTLINGER, T.: The use of Artificial Intelligence in assessing glucose variability in individuals with Diabetes type 2 from routine primary care data, Int. J. Diabetes Clin. Res., vol.7, no. 121, 2020.10.23937/2377-3634/1410121
- [7] ROKOŠNÁ, J. – BABIČ, F. – MAJNARIĆ, L.T. – PUSTZOVÁ, L.: Cooperation between data analysts and medical experts, A case study. CD-MAKE 2020, Dublin, Ireland, 25–28 August, pp. 173–190, Aug. 2020.10.1007/978-3-030-57321-8_10
- [8] MURTHY, K.S.: Automatic construction of decision tress from data: A multidisciplinary survey, Data Mining and Knowledge Discovery, pp. 345-389, 1997.10.1023/A:1009744630224
- [9] QUINLAN, J. R.: C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, 1993.
- [10] BREIMAN, L. – FRIEDMAN, J. H. – OLSHEN, R. A. – STONE, Ch. J.: Classification and Regression Trees, CRC Press, 1999.
- [11] BREIMAN, L.: Random Forests. Machine Learning 45, 5–32, 2001.10.1023/A:1010933404324
- [12] ALTARAWNEH, R. – HUMAVOUN, S. R.: Visualizing Software Structures through Enhanced Interactive Sunburst Layout, In Proceedings of the International Working Conference on Advanced Visual Interfaces (AVI ‘16), Association for Computing Machinery, New York, NY, USA, pp. 288–289, 2016.10.1145/2909132.2926066
- [13] LUNDBERG, S. M. – LEE, S.: A Unified Approach to Interpreting Model Predictions, In: 31st Conference on Neural Information Processing Systems, Long Beach, CA, USA, pp. 1-10, 2017.
- [14] RIBEIRO, M. – SINGH, S. – GUESTRIN, C.: „Why Should I Trust You?“ Explaining the predictions of any classifier, In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016. pp. 1135-1144, 2016.
- [15] SHAPLEY, S. L.: Contributions to the Theory of Game, Princeton: Princeton University Press, 1953.
- [16] LIU, C. – WANG, P.: A Sunburst-based hierarchical information visualization method and its application in public opinion analysis, In: 2015 8th International Conference on Biomedical Engineering and Informatics (BMEI), Shenyang, China, pp. 832-836, 2015.10.1109/BMEI.2015.7401618
- [17] SMITH, A. – HAWES, T. – MYERS, M.: Hiérarchie: Interactive visualization for hierarchical topic models. In: ACL Workshop on Interactive Language Learning, Visualization, and Interfaces, pp. 71–78, 2014.
- [18] ZHANG, Z. et al.: The Five Ws for Information Visualization with Application to Healthcare Informatics, In: IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 11, pp. 1895-1910, 2013.
- [19] KAUSHAL, K. K. et al.: Patient Journey Visualizer: A Tool for Visualizing Patient Journeys, 2017 International Conference on Machine Learning and Data Science (MLDS), Noida, India, pp. 106-113, 2017.10.1109/MLDS.2017.19
- [20] KUMARAKULASINGHE, N. B. – BLOMBERG, T. – LIU, J. – LEAO, A. S. – PAPAPETROU, P.: Evaluating Local Interpretable Model-Agnostic Explanations on Clinical Machine Learning Classification Models, In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Rochester, MN, USA, pp. 7-12, 2020.10.1109/CBMS49503.2020.00009
- [21] MESKE, C. – BUNDE, E.: Transparency and Trust in Human-AI-Interaction: The Role of Model-Agnostic Explanations in Computer Vision-Based Decision Support, In: Degen H., Reinerman-Jones L. (eds) Artificial Intelligence in HCI. HCII 2020. Lecture Notes in Computer Science 12217, Springer, Cham., 2020.10.1007/978-3-030-50334-5_4
- [22] FREITAS DA CRUZ, H. – SCHNEIDER, F. – SCHAPRANOW, M.: Prediction of Acute Kidney Injury in Cardiac Surgery Patients: Interpretation using Local Interpretable Model-agnostic Explanations, In: Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies, vol. 5, pp. 380-387, 2019.10.5220/0007399203800387
- [23] THIMOTEO, L. M.: Interpretable Machine Learning for COVID-19 Diagnosis Through Clinical Variables, In: Congresso Brasileiro de Automática, vol. 2, 2020.
- [24] DETRANO, R. – JANOSI, A. – STEINBRUNN, W. – PFISTERER, M. – SCHMID, J. J. – SANDHU, S. – GUPPY, K. H. – LEE, S. – FROELICHER, V.: International application of a new probability algorithm for the diagnosis of coronary artery disease, Am J Cardiol., vol. 64, no. 5, pp. 304-10, 1989.10.1016/0002-9149(89)90524-9
- [25] BABIČ, F. – OLEJÁR, J. – VANTOVÁ, Z. – PARALIČ, J.: Predictive and descriptive analysis for heart disease diagnosis, In: Federated Conference on Computer Science and Information Systems (FedCSIS), 2017. pp. 155-163, 2017.10.15439/2017F219
- [26] MOLNAR, CH.: SHAP (SHapley Additive exPlanations). Interpretable machine learning. A Guide for Making Black Box Models Explainable, 2019.
- [27] HOLZINGER, A. – CARRINGTON, A. – MÜLLER, H.: Measuring the Quality of Explanations: The System Causability Scale (SCS). Künstl Intell, vol. 34, 193–198, 2020.10.1007/s13218-020-00636-z727105232549653
