Download PDF
Semin Thromb Hemost 2020; 46(04): 410-418
DOI: 10.1055/s-0039-1697949
Review Article
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Using Artificial Intelligence to Manage Thrombosis Research, Diagnosis, and Clinical Management

Aastha Mishra
1   Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India
,
Mohammad Zahid Ashraf
1   Department of Biotechnology, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, India
› Author Affiliations
Further Information

Publication History

Publication Date:
28 September 2019 (online)

    Permissions and Reprints

Abstract

Thrombosis development in either arterial or venous system remains a major cause of death and disability worldwide. This poorly controlled in vivo clotting could result in many severe complications including myocardial infarction, venous thromboembolism, stroke, and cerebral venous thrombosis, to name a few. These conditions are collectively known as thromboembolic disorders (TEDs). Appropriate understanding of TEDs is challenging, as they are multifactorial and involve several and often different risk factors. Hence, it requires a collective effort and data from numerous research studies to fully comprehend molecular mechanisms for prediction, prevention, treatment, and overall management of these conditions. To accomplish this arduous feat, a comprehensive approach is required that can compile thousands of available experimental data and transform these into more applicable and purposeful findings. Thus, large datasets could be utilized to generate models that could be predictive of how an individual would respond when subjected to any kind of additional risk factors or surgery, hospitalization, etc., or in the presence of some susceptible genetic variations. Artificial intelligence-based methods harness the capabilities of computer software to imitate human behaviors such as language translation, visual perception, and, most importantly, decision making. These emerging tools, if appropriately explored, might assist in processing of large data and tackle the complexities of identifying novel or interesting pathways that could otherwise be hidden due to their enormity. This narrative review attempts to compile the applications of various subfields of artificial intelligence and machine learning in the context of thrombosis research to date. It further reflects on the potential of artificial intelligence in transforming enormous research data into translational application in the form of predictive computational models.

Keywords

artificial intelligence - machine learning - thrombosis - natural language processing - artificial neural networks
 

  • References

  • 1 Lee M, Yun JJ, Pyka A. , et al. How to respond to the fourth industrial revolution, or the second information technology revolution? Dynamic new combinations between technology, market, and society through open innovation. J Open Innov Technol Mark Complex 2018; 4: 21
    PubMedGoogle Scholar
  • 2 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
    CrossrefPubMedGoogle Scholar
  • 3 Jiang F, Jiang Y, Zhi H. , et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2017; 2 (04) 230-243
    CrossrefPubMedGoogle Scholar
  • 4 Cao C, Liu F, Tan H. , et al. Deep learning and its applications in biomedicine. Genomics Proteomics Bioinformatics 2018; 16 (01) 17-32
    CrossrefPubMedGoogle Scholar
  • 5 Gupta N, Ashraf MZ. Exposure to high altitude: a risk factor for venous thromboembolism?. Semin Thromb Hemost 2012; 38 (02) 156-163
    Article in Thieme ConnectPubMedGoogle Scholar
  • 6 Mackman N. New insights into the mechanisms of venous thrombosis. J Clin Invest 2012; 122 (07) 2331-2336
    CrossrefPubMedGoogle Scholar
  • 7 Heit JA. Risk factors for venous thromboembolism. Clin Chest Med 2003; 24 (01) 1-12
    CrossrefPubMedGoogle Scholar
  • 8 Cushman M. Epidemiology and risk factors for venous thrombosis. Semin Hematol 2007; 44 (02) 62-69
    CrossrefPubMedGoogle Scholar
  • 9 Rochefort CM, Verma AD, Eguale T, Lee TC, Buckeridge DL. A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data. J Am Med Inform Assoc 2015; 22 (01) 155-165
    CrossrefPubMedGoogle Scholar
  • 10 Pons E, Braun LM, Hunink MG, Kors JA. Natural language processing in radiology: a systematic review. Radiology 2016; 279 (02) 329-343
    CrossrefPubMedGoogle Scholar
  • 11 Demner-Fushman D, Chapman WW, McDonald CJ. What can natural language processing do for clinical decision support?. J Biomed Inform 2009; 42 (05) 760-772
    CrossrefPubMedGoogle Scholar
  • 12 Pham AD, Névéol A, Lavergne T. , et al. Natural language processing of radiology reports for the detection of thromboembolic diseases and clinically relevant incidental findings. BMC Bioinformatics 2014; 15: 266
    CrossrefPubMedGoogle Scholar
  • 13 Swartz J, Koziatek C, Theobald J, Smith S, Iturrate E. Creation of a simple natural language processing tool to support an imaging utilization quality dashboard. Int J Med Inform 2017; 101: 93-99
    CrossrefPubMedGoogle Scholar
  • 14 Fei Y, Hu J, Li WQ, Wang W, Zong GQ. Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis. J Thromb Haemost 2017; 15 (03) 439-445
    CrossrefPubMedGoogle Scholar
  • 15 Romano AVC, Martins TD, Maciel R, De Paula ER, Annichino-Bizzacchi JM. Artificial neural network for prediction of venous thrombosis recurrence. Blood 2016; 128: 3771
    CrossrefPubMedGoogle Scholar
  • 16 Agharezaei L, Agharezaei Z, Nemati A. , et al. The prediction of the risk level of pulmonary embolism and deep vein thrombosis through artificial neural network. Acta Inform Med 2016; 24 (05) 354-359
    CrossrefPubMedGoogle Scholar
  • 17 North BV, Curtis D, Cassell PG, Hitman GA, Sham PC. Assessing optimal neural network architecture for identifying disease-associated multi-marker genotypes using a permutation test, and application to calpain 10 polymorphisms associated with diabetes. Ann Hum Genet 2003; 67 (Pt 4): 348-356
    CrossrefPubMedGoogle Scholar
  • 18 Serretti A, Smeraldi E. Neural network analysis in pharmacogenetics of mood disorders. BMC Med Genet 2004; 5: 27
    PubMedGoogle Scholar
  • 19 Penco S, Grossi E, Cheng S. , et al. Assessment of the role of genetic polymorphism in venous thrombosis through artificial neural networks. Ann Hum Genet 2005; 69 (Pt 6): 693-706
    CrossrefPubMedGoogle Scholar
  • 20 Flamm MH, Colace TV, Chatterjee MS. , et al. Multiscale prediction of patient-specific platelet function under flow. Blood 2012; 120 (01) 190-198
    PubMedGoogle Scholar
  • 21 Goto S, Kimura M, Katsumata Y. , et al. Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients. PLoS One 2019; 14 (01) e0210103
    CrossrefPubMedGoogle Scholar
  • 22 Silvain J, Collet JP, Nagaswami C. , et al. Composition of coronary thrombus in acute myocardial infarction. J Am Coll Cardiol 2011; 57 (12) 1359-1367
    CrossrefPubMedGoogle Scholar
  • 23 Cowper PA, Knight JD, Davidson-Ray L, Peterson ED, Wang TY, Mark DB. ; TRANSLATE-ACS Investigators. Acute and 1-year hospitalization costs for acute myocardial infarction treated with percutaneous coronary intervention: results from the TRANSLATE-ACS Registry. J Am Heart Assoc 2019; 8 (08) e011322
    PubMedGoogle Scholar
  • 24 Wu CC, Hsu WD, Islam MM. , et al. An artificial intelligence approach to early predict non-ST-elevation myocardial infarction patients with chest pain. Comput Methods Programs Biomed 2019; 173: 109-117
    CrossrefPubMedGoogle Scholar
  • 25 Liu W, Zhang M, Zhang Y. , et al. Real-time multilead convolutional neural network for myocardial infarction detection. IEEE J Biomed Health Inform 2018; 22 (05) 1434-1444
    CrossrefPubMedGoogle Scholar
  • 26 Kojuri J, Boostani R, Dehghani P, Nowroozipour F, Saki N. Prediction of acute myocardial infarction with artificial neural networks in patients with nondiagnostic electrocardiogram. J Cardiovasc Dis Res 2015; 6: 51-59
    CrossrefPubMedGoogle Scholar
  • 27 Kwon JM, Kim KH, Jeon KH, Park J. Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography. Echocardiography 2019; 36 (02) 213-218
    CrossrefPubMedGoogle Scholar
  • 28 Tabassian M, Alessandrini M, Herbots L. , et al. Machine learning of the spatio-temporal characteristics of echocardiographic deformation curves for infarct classification. Int J Cardiovasc Imaging 2017; 33 (08) 1159-1167
    CrossrefPubMedGoogle Scholar
  • 29 Nakajima K, Okuda K, Watanabe S. , et al. Artificial neural network retrained to detect myocardial ischemia using a Japanese multicenter database. Ann Nucl Med 2018; 32 (05) 303-310
    CrossrefPubMedGoogle Scholar
  • 30 Wallert J, Tomasoni M, Madison G, Held C. Predicting two-year survival versus non-survival after first myocardial infarction using machine learning and Swedish national register data. BMC Med Inform Decis Mak 2017; 17 (01) 99
    CrossrefPubMedGoogle Scholar
  • 31 Baxt WG. Use of an artificial neural network for the diagnosis of myocardial infarction. Ann Intern Med 1991; 115 (11) 843-848
    PubMedGoogle Scholar
  • 32 Zhang N, Yang G, Gao Z. , et al. Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac cine MRI. Radiology 2019; 291 (03) 606-617
    CrossrefPubMedGoogle Scholar
  • 33 Sharabiani A, Bress A, Douzali E, Darabi H. Revisiting warfarin dosing using machine learning techniques. Comput Math Methods Med 2015; 2015: 560108
    PubMedGoogle Scholar
  • 34 Sharabiani A, Nutescu EA, Galanter WL, Darabi H. A new approach towards minimizing the risk of misdosing warfarin initiation doses. Comput Math Methods Med 2018; 2018: 5340845
    PubMedGoogle Scholar
  • 35 Ma Z, Wang P, Gao Z, Wang R, Khalighi K. Ensemble of machine learning algorithms using the stacked generalization approach to estimate the warfarin dose. PLoS One 2018; 13 (10) e0205872
    CrossrefPubMedGoogle Scholar
  • 36 Gonenc B, Tran N, Gehlbach P, Taylor RH, Iordachita I. Robot-assisted retinal vein cannulation with force-based puncture detection: micron vs. the steady-hand eye robot. Conf Proc IEEE Eng Med Biol Soc 2016; 2016: 5107-5111
    PubMedGoogle Scholar
  • 37 Willekens K, Gijbels A, Schoevaerdts L. , et al. Robot-assisted retinal vein cannulation in an in vivo porcine retinal vein occlusion model. Acta Ophthalmol 2017; 95 (03) 270-275
    CrossrefPubMedGoogle Scholar
  • 38 Owji S, Lu T, Loh TM, Schwein A, Lumsden AB, Bismuth J. Robotic-assisted inferior vena cava filter retrieval. Methodist DeBakey Cardiovasc J 2017; 13 (01) 34-36
    PubMedGoogle Scholar
  • 39 Sun Y, de Castro Abreu AL, Gill IS. Robotic inferior vena cava thrombus surgery: novel strategies. Curr Opin Urol 2014; 24 (02) 140-147
    CrossrefPubMedGoogle Scholar
  • 40 Labovitz DL, Shafner L, Reyes Gil M, Virmani D, Hanina A. Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy. Stroke 2017; 48 (05) 1416-1419
    CrossrefPubMedGoogle Scholar