Artificial neural networks in cardiology: analysis of numerical and text data

P. S. Onishchenko ; K. Yu. Klyshnikov ; E. A. Ovcharenko

Matematičeskaâ biologiâ i bioinformatika, Tome 15 (2020) no. 1, pp. 40-56.

Résumé

This review discusses works on the use of artificial neural networks for processing numerical and textual data. Application of a number of widely used approaches is considered, such as decision support systems; prediction systems, providing forecasts of outcomes of various methods of treatment of cardiovascular diseases, and risk assessment systems. The possibility of using artificial neural networks as an alternative approach to standard methods for processing patient clinical data has been shown. The use of neural network technologies in the creation of automated assistants to the attending physician will make it possible to provide medical services better and more efficiently.

Bibliographie

[1] R. Dhingra, R. S. Vasan, “Biomarkers in cardiovascular disease: Statistical assessment and section on key novel heart failure biomarkers”, Trends in Cardiovascular Medicine, 27:2 (2017), 123–133 | DOI | MR

[2] K. V. Kopeva, E. V. Grakova, A. T. Teplyakov, “Novye markery serdechnoi nedostatochnosti: znachenie dlya diagnostiki i prognozirovaniya NT-proBNP i interleikinovykh retseptorov – chlenov semeistva ST2”, Kompleksnye problemy serdechno-sosudistykh zabolevanii, 7:1 (2018), 94–101 | DOI

[3] J. Wang, G. J. Tan, L. N. Han, Y. Y. Bai, M. He, H. B. Liu, “Novel biomarkers for cardiovascular risk prediction”, Journal of Geriatric Cardiology, 14:2 (2017), 135–150 | DOI

[4] P. Pyakurel, P. Karki, M. Lamsal, A. Ghimire, P. K. Pokharel, “Cardiovascular risk factors among industrial workers: a cross-sectional study from eastern Nepal”, J. Occup. Med. Toxicol, 11 (2016), 25 | DOI

[5] M. Kinoshita, K. Yokote, H. Arai, M. Iida, Y. Ishigaki, S. Ishibashi, S. Umemoto, G. Egusa, H. Ohmura, T. Okamura, S. Kihara, S. Koba, I. Saito, T. Shoji, H. Daida, K. Tsukamoto, J. Deguchi, S. Dohi, K. Dobashi, H. Hamaguchi, M. Hara, T. Hiro, S. Biro, Y. Fujioka, C. Maruyama, Y. Miyamoto, Y. Murakami, M. Yokode, H. Yoshida, H. Rakugi, A. Wakatsuki, S. Yamashita, “Japan Atherosclerosis Society (JAS) Guidelines for Prevention of Atherosclerotic Cardiovascular Diseases 2017”, J. Atheroscler. Thromb., 25:9 (2018), 846–84 | DOI

[6] Tabakaev M.V., Artamonova G.V., “Urbanization and cardiovascular diseases in modern society”, Russ. J. Cardiol., 6:122 (2015), 94–99 | DOI

[7] K. Studziński, T. Tomasik, J. Krzysztoń, J. Jóźwiak, A. Windak, “Effect of using cardiovascular risk scoring in routine risk assessment in primary prevention of cardiovascular disease: protocol for an overview of systematic reviews”, BMJ Open, 7:3 (2017) | DOI

[8] N. Garg, S. K. Muduli, A. Kapoor, S. Tewari, S. Kumar, R. Khanna, P. K. Goel, “Comparison of different cardiovascular risk score calculators for cardiovascular risk prediction and guideline recommended statin uses”, Indian Heart Journal, 69:4 (2017), 458–463 | DOI

[9] F. Jiang, Y. Jiang, H. Zhi, Y. Dong, H. Li, S. Ma, Y. Wang, Q. Dong, H. Shen, Y. Wang, “Artificial intelligence in healthcare: past, present and future”, Stroke Vasc. Neurol., 2:4 (2017), 230–243 | DOI

[10] L. Sacchi, G. Lanzola, N. Viani, S. Quaglini, “Personalization and Patient Involvement in Decision Support Systems: Current Trends”, Yearb. Med. Inform., 10:1 (2015), 106–118 | DOI

[11] R. A. Miller, “Medical diagnostic decision support systems past, present, and future: a threaded bibliography and brief commentary”, J. Am. Med. Inform. Assoc., 1994, no. 1, 8–27 | DOI

[12] P. Tiwari, N. Shah, E. Samuel, P. Shah, Y. Patel, P. Tiwari, “Clinical decision support systems”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 5:2 (2019), 993–995 | DOI

[13] A. N. Ramesh, C. Kambhampati, J. R.T. Monson, P. J. Drew, “Artificial intelligence in medicine”, Ann. R. Coll. Surg. Engl., 86:5 (2004), 334–338 | DOI

[14] V. S. Medvedev, V. G. Potemkin, Neironnye seti MATLAB 6, Dialog-MIFI, M., 2002, 496 pp.

[15] L. Herkenhoff, J. Fogli, Applied statistics for business and management using Microsoft Excel, Springer, New York, 2013 | DOI

[16] C. H. Weiß, “StatSoft, Inc., Tulsa, OK.: STATISTICA, Version 8”, Advances in Statistical Analysis, 91:3 (2007), 339–341 | DOI | Zbl

[17] D. Grapov, J. Fahrmann, K. Wanichthanarak, S. Khoomrung, “Rise of deep learning for genomic, proteomic, and metabolomic data integration in precision medicine”, OMICS: A Journal of Integrative Biology, 22:10 (2018), 630–636 | DOI

[18] S. H. Han, K. W. Kim, S. Kim, Y. C. Youn, “Artificial neural network: understanding the basic concepts without mathematics”, Dement Neurocognitive Disord, 17:3 (2018), 83–89 | DOI

[19] O. G. Zharikov, V. A. Kovalev, A. A. Litvin, “Sovremennye vozmozhnosti ispolzovaniya nekotorykh ekspertnykh sistem v meditsine”, Vrach i informatsionnye tekhnologii, 2008, no. 5, 24–30

[20] I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT Press, Cambridge, 2016, 800 pp. | MR | Zbl

[21] G. Panchal, A. Ganatra, P. Shah, D. Panchal, “Determination of over-learning and over-fitting problem in back propagation neural network”, International Journal on Soft Computing, 2:2 (2011), 40–51 | DOI | MR

[22] G. A. Rozykhodzhaeva, D. A. Rozykhodzhaeva, “Osobennosti formirovaniya obuchayuschei vyborki i obucheniya neironnoi seti s nepolnymi vkhodnymi dannymi pri reshenii chastnykh meditsinskikh zadach”, Nauchnoe obozrenie. Biologicheskie nauki, 2017, no. 5, 28–32

[23] E. Choi, A. Schuetz, W. F. Stewart, J. Sun, “Using recurrent neural network models for early detection of heart failure onset”, J. Am. Med. Inform. Assoc., 24:2 (2017), 361–370 | DOI

[24] J. K. Kim, S. Kang, “Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis”, J. Healthc. Eng., 2017 | DOI

[25] A. I. Kalyakulina, I. I. Yusipov, V. A. Moskalenko, A. V. Nikolskiy, A. A. Kozlov, N. Yu. Zolotykh, M. V. Ivanchenko, “Finding morphology points of electrocardiographic-signal waves using wavelet analysis”, Radiophysics and Quantum Electronics, 61:8-9 (2019), 689–703 | DOI

[26] G. Sannino, G. De Pietro, “A deep learning approach for ECG-based heartbeat classification for arrhythmia detection”, Future Generation Computer Systems, 86 (2018), 446–455 | DOI

[27] A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, H. E. Stanley, “Physiobank, physiotoolkit, physionet”, Circulation, 101:23 (2000) | DOI

[28] G. B. Moody, R. G. Mark, “The impact of the MIT-BIH arrhythmia database”, IEEE Engineering in Medicine and Biology Magazine, 20:3 (2001), 45–50 | DOI

[29] Ö. Y{\i}ld{\i}r{\i}m, P. Pławiak, R. S. Tan, U. R. Acharya, “Arrhythmia detection using deep convolutional neural network with long duration ECG signals”, Computers in biology and medicine, 102 (2018), 411–420 | DOI

[30] V. Moskalenko, N. Zolotykh, G. Osipov, “Deep Learning for ECG Segmentation”, International Conference on Neuroinformatics, eds. Kryzhanovsky B., W. Dunin-Barkowski, V. Redko, Yu. Tiumentsev, Springer, Cham, 2019, 246–254

[31] V. A. Moskalenko, A. V. Nikolskii, N. Yu. Zolotykh, A. A. Kozlov, K. A. Kosonogov, A. I. Kalyakulina, I. I. Yusipov, V. M. Levanov, “Programmnyi kompleks «Kiberserdtse-diagnostika» dlya avtomaticheskogo analiza elektrokardiogramm s primeneniem metodov mashinnogo obucheniya”, Sovremennye tekhnologii v meditsine, 11:2 (2019), 86–91 | DOI

[32] S. Savalia, V. Emamian, “Cardiac arrhythmia classification by multi-layer perceptron and convolution neural networks”, Bioengineering (Basel), 5:2 (2018) | DOI

[33] P. Lu, S. Guo, H. Zhang, Q. Li, Y. Wang, Y. Wang, L. Qi, “Research on improved depth belief network-based prediction of cardiovascular diseases”, J. Healthc Eng., 2018 | DOI | MR

[34] S. Iftikhar, K. Fatima, A. Rehman, A. S. Almazyad, T. Saba, “An evolution based hybrid approach for heart disease classification and associated risk factor identification”, Biomedical Research, 28:8 (2017), 3451–3455

[35] V. Chaurasia, S. Pal, “Data Mining Approach to Detect Heart Diseases”, International Journal of Advanced Computer Science and Information Technology, 2:4 (2013), 56–66

[36] G. H. Tison, J. M. Sanchez, B. Ballinger, A. Singh, J. E. Olgin, M. J. Pletcher, E. Vittinghoff, E. S. Lee, S. M. Fan, R. A. Gladstone, C. Mikell, N. Sohoni, J. Hsieh, G. M. Marcus, “Passive detection of atrial fibrillation using a commercially available smartwatch”, JAMA Cardiology, 3:5 (2018), 409–416 | DOI

[37] P. F. Tsai, P. C. Chen, Y. Y. Chen, H. Y. Song, H. M. Lin, F. M. Lin, Q.-P. Huang, “Length of hospital stay prediction at the admission stage for cardiology patients using artificial neural network”, J. Healthc. Eng., 2016 | DOI

[38] E. Kutafina, I. Bechtold, K. Kabino, S. M. Jonas, “Recursive neural networks in hospital bed occupancy forecasting”, BMC Medical Informatics and Decision Making, 19:39 (2019) | DOI

[39] J. M. Kwon, Y. Lee, Y. Lee, S. Lee, J. Park, “An Algorithm Based on Deep Learning for Predicting In-Hospital Cardiac Arrest”, J. Am. Heart Assoc., 7:13 (2018) | DOI

[40] A. Meyer, D. Zverinski, B. Pfahringer, J. Kempfert, T. Kuehne, S. H. Sündermann, C. Stamm, T. Hofmann, V. Falk, C. Eickhoff, “Machine learning for real-time prediction of complications in critical care: a retrospective study”, Lancet Respir. Med., 6:12 (2018), 905–914 | DOI

[41] S. Nanayakkara, S. Fogarty, M. Tremeer, K. Ross, B. Richards, C. Bergmeir, S. Xu, D. Stub, K. Smith, M. Tacey, D. Liew, D. Pilcher, D. M. Kaye, “Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study”, PLoS Med, 15:11 (2018) | DOI

[42] L. N. Yasnitskii, F. M. Cherepanov, “Neiroekspertnaya sistema diagnostiki, prognozirovaniya i upravleniya riskami serdechno-sosudistykh zabolevanii”, Prikladnaya matematika i voprosy upravleniya, 2018, no. 3, 107–126 | DOI

[43] R. Narain, S. Saxena, A. K. Goyal, “Cardiovascular risk prediction: a comparative study of Framingham and quantum neural network based approach”, Patient Prefer Adherence, 2016, no. 10, 1259–1270 | DOI

[44] A. Kordzadeh, S. S. Esfahlani, “The Role of Artificial Intelligence in the Prediction of Functional Maturation of Arteriovenous Fistula”, Ann. Vasc. Dis., 12:1 (2019), 44–49 | DOI

[45] H. Li, M. Luo, J. Zheng, J. Luo, R. Zeng, N. Feng, Q. Du, J. Fang, “An artificial neural network prediction model of congenital heart disease based on risk factors”, Medicine (Baltimore), 96:6 (2017) | DOI | Zbl

[46] S. F. Weng, J. Reps, J. Kai, J. M. Garibaldi, N. Qureshi, “Can machine-learning improve cardiovascular risk prediction using routine clinical data”, PLoS ONE, 12:4 (2017) | DOI

[47] K. K. Stout, C. J. Daniels, J. A. Aboulhosn, B. Bozkurt, C. S. Broberg, J. M. Colman, S. R. Crumb, J. A. Dearani, S. Fuller, M. Gurvitz, P. Khairy, M. J. Landzberg, A. Saidi, A. M. Valente, G. F. Van Hare, “AHA/ACC guideline for the management of adults with congenital heart disease”, Journal of the American College of Cardiology, 2018 | DOI

[48] A. Sbrollini, M. C. De Jongh, C. C. Ter Haar, R. W. Treskes, S. Man, L. Burattini, C. A. Swenne, “Serial electrocardiography to detect newly emerging or aggravating cardiac pathology: a deep-learning approach”, Biomed. Eng. Online, 18:15 (2019) | DOI | MR

[49] S. Sadek, A. Al-Hamadi, B. Michaelis, U. Sayed, “Face detection and localization in color images: an efficient neural approach”, Journal of Software Engineering and Applications, 04:12 (2011), 682–687 | DOI

[50] T. Xiao, S. Li, B. Wang, L. Lin, X. Wang, End-to-end deep learning for person search, , 2016 1604.01850 [cs.CV]

[51] R. Bin Sulaiman, “Artificial Intelligence Based Autonomous Car”, SSRN Electronic Journal, 2018 | DOI

[52] P. Shaw, I. Williams, “Reuse in practice: the UK's car and clothing sectors”, Detritus: Multidisciplinary journal of waste resources residues, 04 (2018), 36–47 | DOI

[53] IBM Watson, (data obrascheniya: 28.01.2020) https://www.ibm.com/watson/

[54] A. Curioni-Fontecedro, “A new era of oncology through artificial intelligence”, ESMO Open, 2:2 (2017) | DOI

[55] Y. Chen, J. D. Elenee Argentinis, G. Weber, “IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research”, Clin. Ther., 38:4 (2016), 688–701 | DOI

[56] A. Y. Hannun, P. Rajpurkar, M. Haghpanahi, G. H. Tison, C. Bourn, M. P. Turakhia, A. Y. Ng, “Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network”, Nature Medicine, 25:1 (2019), 65–69 | DOI

[57] E. Kańtoch, “Recognition of sedentary behavior by machine learning analysis of wearable sensors during activities of daily living for telemedical assessment of cardiovascular risk”, Sensors, 18:10 (2018) | DOI

[58] L. J. Mena, V. G. Félix, A. Ochoa, R. Ostos, E. González, J. Aspuru, P. Velarde, G. E. Maestre, “Mobile personal health monitoring for automated classification of electrocardiogram signals in elderly”, Computational and Mathematical Methods in Medicine, 2018 (2018), 1–9 | DOI | MR

[59] F. Beritelli, G. Capizzi, G. Lo Sciuto, C. Napoli, F. Scaglione, “Automatic heart activity diagnosis based on Gram polynomials and probabilistic neural networks”, Biomed. Eng. Lett., 8:1 (2017), 77–85 | DOI

[60] A. Palomäki, V. Hällberg, M. Ala-Korpela, P. T. Kovanen, K. Malminiemi, “Prompt impact of first prospective statin mega-trials on postoperative lipid management of CABG patients: a 20-year follow-up in a single hospital”, Lipids Health Dis., 15 (2016), 124 | DOI

[61] J. Niemi, V. P. Mäkinen, J. Heikkonen, L. Tenkanen, Y. Hiltunen, M. L. Hannuksela, M. Jauhiainen, C. Forsblom, M. R. Taskinen, Y. A. Kesäniemi, M. J. Savolainen, K. Kaski, P. H. Groop, P. T. Kovanen, M. Ala-Korpela, “Estimation of VLDL, IDL, LDL, HDL2, apoA-I, and apoB from the Friedewald inputs–apoB and IDL, but not LDL, are associated with mortality in type 1 diabetes”, Annals of Medicine, 41:6 (2009), 451–461 | DOI

[62] J. Yao, S. Tridandapani, W. F. Auffermann, C. A. Wick, P. T. Bhatti, “An adaptive seismocardiography (scg)-ecg multimodal framework for cardiac gating using artificial neural networks”, IEEE J. Transl. Eng. Health Med., 6 (2018) | DOI

[63] S. Sakr, R. Elshawi, A. Ahmed, W. T. Qureshi, C. Brawner, S. Keteyian, M. J. Blaha, M. H. Al-Mallah, “Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) Project”, PLoS One, 13:4 (2018) | DOI

[64] P. D. Myers, B. M. Scirica, C. M. Stultz, “Machine learning improves risk stratification after acute coronary syndrome”, Sci. Rep., 7 (2017) | DOI