Ερευνητικό Προσωπικό
Δημόπουλος Δημήτριος
Προσωπικά Στοιχεία
Δημόπουλος Δημήτριος
ddimopoulos [at] aegean [dot] gr
Copyright Notice: Το υλικό αυτό παρουσιάζεται για έγκαιρη διάδοση επιστημονικής και τεχνικής εργασίας. Τα πνευματικά δικαιώματα και όλα τα σχετικά δικαιώματα παραμένουν στους συγγραφείς ή σε άλλους κατόχους πνευματικών δικαιωμάτων. Όσοι αντιγράφουν αυτές τις πληροφορίες αναμένεται να τηρούν τους όρους και τους περιορισμούς που επιβάλλει το πνευματικό δικαίωμα κάθε συγγραφέα. Στις περισσότερες περιπτώσεις, τα έργα αυτά δεν μπορούν να αναδημοσιευτούν ή να αναπαραχθούν μαζικά χωρίς τη ρητή άδεια του κατόχου των πνευματικών δικαιωμάτων.
Επιστημονικά Περιοδικά
V. Danilatou, D. Dimopoulos, T. Kostoulas, J. Douketis, Machine learning-based predictive models for patients with venous thromboembolism: A Systematic Review, Thrombosis and Haemostasis , 2024, Thieme, https://doi.org/10.1055/a-2299-4758
Περίληψη:
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 (EHRs). 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 AI in VTE.
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 (EHRs). 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 AI in VTE.