From Code to Clots: Applying Machine Learning to Clinical Aspects of Venous Thromboembolism Prevention, Diagnosis, and Management

Pavlina Chrysafi; Barbara Lam; Samuel Carton; Rushad Patell

doi:10.1055/a-2415-8408

Hämostaseologie, Table of Contents

Hamostaseologie 2024; 44(06): 429-445
DOI: 10.1055/a-2415-8408

Review Article

From Code to Clots: Applying Machine Learning to Clinical Aspects of Venous Thromboembolism Prevention, Diagnosis, and Management

Authors

Pavlina Chrysafi

¹Department of Medicine, Mount Auburn Hospital, Harvard Medical School, Cambridge, Massachusetts, United States
Barbara Lam

²Division of Hemostasis and Thrombosis, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States

³Division of Clinical Informatics, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States
Samuel Carton

⁴Department of Computer Science, College of Engineering and Physical Sciences, University of New Hampshire, Durham, New Hampshire, United States
Rushad Patell

²Division of Hemostasis and Thrombosis, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States

Abstract

The high incidence of venous thromboembolism (VTE) globally and the morbidity and mortality burden associated with the disease make it a pressing issue. Machine learning (ML) can improve VTE prevention, detection, and treatment. The ability of this novel technology to process large amounts of high-dimensional data can help identify new risk factors and better risk stratify patients for thromboprophylaxis. Applications of ML for VTE include systems that interpret medical imaging, assess the severity of the VTE, tailor treatment according to individual patient needs, and identify VTE cases to facilitate surveillance. Generative artificial intelligence may be leveraged to design new molecules such as new anticoagulants, generate synthetic data to expand datasets, and reduce clinical burden by assisting in generating clinical notes. Potential challenges in the applications of these novel technologies include the availability of multidimensional large datasets, prospective studies and clinical trials to ensure safety and efficacy, continuous quality assessment to maintain algorithm accuracy, mitigation of unwanted bias, and regulatory and legal guardrails to protect patients and providers. We propose a practical approach for clinicians to integrate ML into research, from choosing appropriate problems to integrating ML into clinical workflows. ML offers much promise and opportunity for clinicians and researchers in VTE to translate this technology into the clinic and directly benefit the patients.

Keywords

venous thromboembolism - machine learning - artificial intelligence - prediction modeling

Full Text

References

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