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
