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Thromb Haemost 2024; 124(11): 1040-1052
DOI: 10.1055/a-2299-4758
Stroke, Systemic or Venous Thromboembolism

Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review

Vasiliki Danilatou 
1   School of Medicine, European University of Cyprus, Nicosia, Cyprus
2   Healthcare Division, Sphynx Technology Solutions, Nicosia, Cyprus
,
Dimitrios Dimopoulos 
3   School of Engineering, Department of Information and Communication Systems Engineering, University of the Aegean, North Aegean, Greece
,
Theodoros Kostoulas
3   School of Engineering, Department of Information and Communication Systems Engineering, University of the Aegean, North Aegean, Greece
,
James Douketis
4   Department of Medicine, McMaster University, Hamilton, Canada
5   Department of Medicine, St. Joseph's Healthcare Hamilton, Ontario, Canada
› Author Affiliations Funding This work was partially funded by RETENTION project, a European Union's Horizon 2020 research and innovation program under Grant Agreement No 965343.
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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

Supplementary Material



Publication History

Received: 21 August 2023

Accepted: 03 April 2024

Accepted Manuscript online:
04 April 2024

Article published online:
13 May 2024

© 2024. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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