Influential Usage of Big Data and Artificial Intelligence in Healthcare

Overview

Journal Comput Math Methods Med

Publisher Hindawi

Specialty Medical Informatics

Date 2021 Sep 16

PMID 34527076

Citations 13

Authors

Yan Cheng Yang

Saad Ul Islam

Asra Noor

Sadia Khan

Waseem Afsar

Shah Nazir

Affiliations

Soon will be listed here.

Abstract

Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people's lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.

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References

Wu S, Liu S, Sohn S, Moon S, Wi C, Juhn Y . Modeling asynchronous event sequences with RNNs. J Biomed Inform. 2018; 83:167-177. PMC: 6103779. DOI: 10.1016/j.jbi.2018.05.016. View

Beecy A, Gummalla M, Sholle E, Xu Z, Zhang Y, Michalak K . Utilizing electronic health data and machine learning for the prediction of 30-day unplanned readmission or all-cause mortality in heart failure. Cardiovasc Digit Health J. 2022; 1(2):71-79. PMC: 8890080. DOI: 10.1016/j.cvdhj.2020.07.004. View

Bui A, Van Horn J . Envisioning the future of 'big data' biomedicine. J Biomed Inform. 2017; 69:115-117. PMC: 5613673. DOI: 10.1016/j.jbi.2017.03.017. View

Palmer R, Dimairo M, Cooper C, Enderby P, Brady M, Bowen A . Self-managed, computerised speech and language therapy for patients with chronic aphasia post-stroke compared with usual care or attention control (Big CACTUS): a multicentre, single-blinded, randomised controlled trial. Lancet Neurol. 2019; 18(9):821-833. PMC: 6700375. DOI: 10.1016/S1474-4422(19)30192-9. View

Willems S, Abeln S, Feenstra K, de Bree R, van der Poel E, Baatenburg de Jong R . The potential use of big data in oncology. Oral Oncol. 2019; 98:8-12. DOI: 10.1016/j.oraloncology.2019.09.003. View

Bhavnani S, Parakh K, Atreja A, Druz R, Graham G, Hayek S . 2017 Roadmap for Innovation-ACC Health Policy Statement on Healthcare Transformation in the Era of Digital Health, Big Data, and Precision Health: A Report of the American College of Cardiology Task Force on Health Policy Statements and Systems of.... J Am Coll Cardiol. 2017; 70(21):2696-2718. DOI: 10.1016/j.jacc.2017.10.018. View

Ahmad T, Wilson F, Desai N . The Trifecta of Precision Care in Heart Failure: Biology, Biomarkers, and Big Data. J Am Coll Cardiol. 2018; 72(10):1091-1094. PMC: 6345377. DOI: 10.1016/j.jacc.2018.07.009. View

Zhou Y, Wang F, Tang J, Nussinov R, Cheng F . Artificial intelligence in COVID-19 drug repurposing. Lancet Digit Health. 2020; 2(12):e667-e676. PMC: 7500917. DOI: 10.1016/S2589-7500(20)30192-8. View

Sardar P, Abbott J, Kundu A, Aronow H, Granada J, Giri J . Impact of Artificial Intelligence on Interventional Cardiology: From Decision-Making Aid to Advanced Interventional Procedure Assistance. JACC Cardiovasc Interv. 2019; 12(14):1293-1303. DOI: 10.1016/j.jcin.2019.04.048. View

10.

Davoodi R, Moradi M . Mortality prediction in intensive care units (ICUs) using a deep rule-based fuzzy classifier. J Biomed Inform. 2018; 79:48-59. DOI: 10.1016/j.jbi.2018.02.008. View

11.

Nemati M, Ansary J, Nemati N . Machine-Learning Approaches in COVID-19 Survival Analysis and Discharge-Time Likelihood Prediction Using Clinical Data. Patterns (N Y). 2020; 1(5):100074. PMC: 7334917. DOI: 10.1016/j.patter.2020.100074. View

12.

Miller J, Shan G, Lombardo J, Jimenez-Maggoria G . Biomedical informatics applications for precision management of neurodegenerative diseases. Alzheimers Dement (N Y). 2018; 4:357-365. PMC: 6118097. DOI: 10.1016/j.trci.2018.03.007. View

13.

Waring J, Lindvall C, Umeton R . Automated machine learning: Review of the state-of-the-art and opportunities for healthcare. Artif Intell Med. 2020; 104:101822. DOI: 10.1016/j.artmed.2020.101822. View

14.

Hasselgren A, Kralevska K, Gligoroski D, Pedersen S, Faxvaag A . Blockchain in healthcare and health sciences-A scoping review. Int J Med Inform. 2019; 134:104040. DOI: 10.1016/j.ijmedinf.2019.104040. View

15.

Heude B, Scherdel P, Werner A, Guern M, Gelbert N, Walther D . A big-data approach to producing descriptive anthropometric references: a feasibility and validation study of paediatric growth charts. Lancet Digit Health. 2020; 1(8):e413-e423. DOI: 10.1016/S2589-7500(19)30149-9. View

16.

Cha D, Pae C, Seong S, Choi J, Park H . Automated diagnosis of ear disease using ensemble deep learning with a big otoendoscopy image database. EBioMedicine. 2019; 45:606-614. PMC: 6642402. DOI: 10.1016/j.ebiom.2019.06.050. View

17.

Wang Y, Mckee M, Torbica A, Stuckler D . Systematic Literature Review on the Spread of Health-related Misinformation on Social Media. Soc Sci Med. 2019; 240:112552. PMC: 7117034. DOI: 10.1016/j.socscimed.2019.112552. View

18.

Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T . Artificial Intelligence in Precision Cardiovascular Medicine. J Am Coll Cardiol. 2017; 69(21):2657-2664. DOI: 10.1016/j.jacc.2017.03.571. View

19.

Kitson N, Constantinou A . Learning Bayesian networks from demographic and health survey data. J Biomed Inform. 2020; 113:103588. DOI: 10.1016/j.jbi.2020.103588. View

20.

Carney T, Kong A . Leveraging health informatics to foster a smart systems response to health disparities and health equity challenges. J Biomed Inform. 2017; 68:184-189. PMC: 11407115. DOI: 10.1016/j.jbi.2017.02.011. View