A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data

D. Mozaffarian, et al., Heart disease and stroke statistics-2015 update. a report from the american heart association, Circulation 131 (24) (2015) E29–E322.

K. Srinath Reddy, S. Yusuf, Emerging epidemic of cardiovascular disease in developing countries. http://ahajournals.org

K. Thygesen, J. Alpert, A. Jaffe, M. Simoons, B. Chaitman, H. White, Third universal definition of myocardial infarction, Circulation 126 (16) (2012) 2020–2035.

Article  PubMed  Google Scholar 

S. Chattopadhyay, A framework for studying perceptions of rural healthcare staff and basic ict support for e-health use: an indian experience, Telemedicine and e-Health 16 (1) (2010) 80–88.

Article  PubMed  Google Scholar 

S. Vallabhajosyula, S. Vallabhajosyula, B. Burstein, B. W. Ternus, P. R. Sundaragiri, R. D. White, G. W. Barsness, J. C. Jentzer, Epidemiology of in-hospital cardiac arrest complicates nonst–segment elevation myocardial infarction receiving early coronary angiography, American Heart Journal 223 (2020) 59–64. https://doi.org/10.1016/j.ahj.2020.01.016.

Article  PubMed  Google Scholar 

M. Mitka, Heart disease a global health threat. http://jama.jamanetwork.com/

M. Kosuge, K. Kimura, T. Ishikawa, T. Ebina, K. Hibi, K. Tsukahara, M. Kanna, N. Iwahashi, J. Okuda, N. Nozawa, H. Ozaki, H. Yano, T. Nakati, I. Kusama, S. Umemura, Differences between men and women in terms of clinical features of st-segment elevation acute myocardial infarction, Circulation Journal 70.

F. Pedersen, V. Butrymovich, H. Kelbaek, K. Wachtell, S. Helqvist, J. Kastrup, L. Holmvang, P. Clemmensen, T. Engstrøm, K. Saunamäki, E. Jørgensen, Short-and long-term cause of death in patients treated with primary pci for stemi.

K. Smolina, F. L. Wright, M. Rayner, M. J. Goldacre, Determinants of the decline in mortality from acute myocardial infarction in england between 2002 and 2010: Linked national database study, BMJ (Online) 344 (7842). https://doi.org/10.1136/bmj.d8059.

H. Sharif Nia, O. Gorgulu, N. Naghavi, E. S. Froelicher, F. K. Fomani, A. H. Goudarzian, S. P. Sharif, R. Pourkia, A. A. Haghdoost, A time-series prediction model of acute myocardial infarction in northern iran: the risk of climate change and religious mourning, BMC Cardiovascular Disorders 21 (1). https://doi.org/10.1186/s12872-021-02372-0.

E. M. DeFilippis, N. Reza, E. Donald, M. M. Givertz, J. A. Lindenfeld, M. Jessup, Considerations for heart failure care during the covid-19 pandemic, JACC: Heart Failure 8 (8) (2020) 681–691. https://doi.org/10.1016/j.jchf.2020.05.006.

S. S. Virani, A. Alonso, E. J. Benjamin, M. S. Bittencourt, C. W. Callaway, A. P. Carson, A. M. Chamberlain, A. R. Chang, S. Cheng, F. N. Delling, L. Djousse, M. S. V. Elkind, J. F. Ferguson, M. Fornage, S. S. Khan, B. M. Kissela, K. L. Knutson, T. W. Kwan, D. T. Lackland, C. W. Tsao, Heart disease and stroke statistics–2020 update a report from the american heart association, Circulation 141 (9) (2020) E139–E596. https://doi.org/10.1161/CIR.0000000000000757.

Article  PubMed  Google Scholar 

O. Gaidai, Y. Cao, S. Loginov, Global cardiovascular diseases death rate prediction, Current Problems in Cardiology 48 (5). https://doi.org/10.1016/J.CPCARDIOL.2023.101622.

B. Lim, S. Zohren, Time-series forecasting with deep learning: A survey, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379 (2194). https://doi.org/10.1098/rsta.2020.0209.

S. Naher, F. Rabbi, M. M. Hossain, R. Banik, S. Pervez, A. B. Boitchi, Forecasting the incidence of dengue in bangladesh–application of time series model, Health Science Reports 5 (4). https://doi.org/10.1002/hsr2.666.

O. Faust, V. R. Prasad, G. Swapna, S. Chattopadhyay, T.-C. Lim, Comprehensive analysis of normal and diabetic heart rate signals: A review, Journal of Mechanics in Medicine and Biology 12 (05) (2012) 1240033.

Article  Google Scholar 

H. Sharif Nia, O. Gorgulu, N. Naghavi, E. S. Froelicher, F. K. Fomani, A. H. Goudarzian, S. P. Sharif, R. Pourkia, A. A. Haghdoost, A time-series prediction model of acute myocardial infarction in northern iran: the risk of climate change and religious mourning, BMC Cardiovascular Disorders 21 (1). https://doi.org/10.1186/s12872-021-02372-0.

S. Satapathy, S. Chattopadhyay, Observation-prevention framework of cardiac risk factors: An indian study, Journal of Medical Imaging and Health Informatics 2 (2) (2012) 102–113.

Article  Google Scholar 

V. Vaičiulis, J. Venclovienė, A. Miškinytė, R. Ustinavičienė, A. Dėdelė, G. Kalinienė, D. Lukšienė, A. Tamošiūnas, L. Seiduanova, R. Radišauskas, Association between outdoor air pollution and fatal acute myocardial infarction in lithuania between 2006 and 2015: A time series design, International Journal of Environmental Research and Public Health 20 (5) (2023) 4549.

Article  PubMed  PubMed Central  Google Scholar 

U. R. Acharya, O. Faust, D. N. Ghista, S. V. Sree, A. P. C. Alvin, S. Chattopadhyay, T.-C. Lim, E. Y.-K. Ng, W. Yu, A systems approach to cardiac health diagnosis, Journal of Medical Imaging and Health Informatics 3 (2) (2013) 261–267.

Article  Google Scholar 

A. Gasparrini, G. Gorini, A. Barchielli, On the relationship between smoking bans and incidence of acute myocardial infarction, European Journal of Epidemiology 24 (10) (2009) 597–602. https://doi.org/10.1007/s10654-009-9377-0.

Article  PubMed  Google Scholar 

Z. Akhtar, M. A. Aleem, P. K. Ghosh, A. K. M. M. Islam, F. Chowdhury, C. R. MacIntyre, O. Fröbert, In-hospital and 30-day major adverse cardiac events in patients referred for st-segment elevation myocardial infarction in dhaka, bangladesh, BMC Cardiovascular Disorders 21 (1). https://doi.org/10.1186/s12872-021-01896-9.

J. G. M. Rosmalen, A. M. G. Wenting, A. M. Roest, P. De Jonge, E. H. Bos, Revealing causal heterogeneity using time series analysis of ambulatory assessments: Application to the association between depression and physical activity after myocardial infarction, Psychosomatic Medicine 74 (4) (2012) 377–386. https://doi.org/10.1097/PSY.0b013e3182545d47.

Article  PubMed  Google Scholar 

A. Sofogianni, N. Stalikas, C. Antza, K. Tziomalos, Cardiovascular risk prediction models and scores in the era of personalized medicine, Journal of Personalized Medicine 12 (7) (2022) 1180.

Article  PubMed  PubMed Central  Google Scholar 

R. Blackburn, H. Zhao, R. Pebody, A. Hayward, C. Warren-Gash, Laboratory-confirmed respiratory infections as predictors of hospital admission for myocardial infarction and stroke: Time-series analysis of english data for 2004-2015, Clinical Infectious Diseases 67 (1) (2018) 8–17. https://doi.org/10.1093/cid/cix1144.

Article  PubMed  PubMed Central  Google Scholar 

S. Chattopadhyay, R. Das, Statistical validation of cardiovascular digital biomarkers towards monitoring the cardiac risk in copd: A lyfas case study, Artificial Intelligence Evolution (2022) 1–16.

A. Raza, I. Akhtar, L. Abualigah, R. A. Zitar, M. Sharaf, M. S. Daoud, H. Jia, Preventing road accidents through early detection of driver behavior using smartphone motion sensor data: An ensemble feature engineering approach, IEEE Access 11 (2023) 138457–138471. https://doi.org/10.1109/ACCESS.2023.3340304.

Article  Google Scholar 

A. Raza, A. M. Qadri, I. Akhtar, N. A. Samee, M. Alabdulhafith, Logrf: An approach to human pose estimation using skeleton landmarks for physiotherapy fitness exercise correction, IEEE Access 11 (2023) 107930–107939. https://doi.org/10.1109/ACCESS.2023.3320144.

Article  Google Scholar 

M. Udenio, E. Vatamidou, J. C. Fransoo, Exponential smoothing forecasts: taming the bullwhip effect when demand is seasonal, International Journal of Production Research 61 (6) (2023) 1796–1813.

Article  Google Scholar 

P. Thayyib, M. N. Thorakkattle, F. Usmani, A. T. Yahya, N. H. Farhan, Forecasting indian goods and services tax revenue using tbats, ets, neural networks, and hybrid time series models, Cogent Economics & Finance 11 (2) (2023) 2285649.

Article  Google Scholar 

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