Abstract
Background Venous thromboembolism (VTE) is a chronic disorder with a significant health and
economic burden. Several VTE-specific clinical prediction models (CPMs) have been
used to assist physicians in decision-making but have several limitations. This systematic
review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient
data derived from electronic health records. We aimed to explore ML-CPMs' applications
in VTE for risk stratification, outcome prediction, diagnosis, and treatment.
Methods Three databases were searched: PubMed, Google Scholar, and IEEE electronic library.
Inclusion criteria focused on studies using structured data, excluding non-English
publications, studies on non-humans, and certain data types such as natural language
processing and image processing. Studies involving pregnant women, cancer patients,
and children were also excluded. After excluding irrelevant studies, a total of 77
studies were included.
Results Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver
operating area under the curve in the four clinical domains that were explored. However,
the majority of the studies were retrospective, monocentric, and lacked detailed model
architecture description and external validation, which are essential for quality
audit. This review identified research gaps and highlighted challenges related to
standardized reporting, reproducibility, and model comparison.
Conclusion ML-CPMs show promise in improving risk assessment and individualized treatment recommendations
in VTE. Apparently, there is an urgent need for standardized reporting and methodology
for ML models, external validation, prospective and real-world data studies, as well
as interventional studies to evaluate the impact of artificial intelligence in VTE.
Keywords
machine learning - artificial intelligence - venous thromboembolism - electronic health
record - clinical prediction models
