Review
Research progress of machine learning algorithm in prevention, diagnosis and treatment of venous thromboembolism
Xi Linfeng, Yang Haoyu, Liu Min, Zhai Zhenguo, Wang Chen
Published 2024-01-25
Cite as Int J Respir, 2024, 44(1): 98-103. DOI: 10.3760/cma.j.cn131368-20231010-00230
Abstract
As an important branch of artificial intelligence, machine learning (ML) has been widely used in disease diagnosis and evaluation.Venous thromboembolism (VTE) is a common thrombotic disease, which requires accurate and efficient assessment strategies in clinical settings.With the gradual deepening of medical and engineering crossover and the continuous optimization of ML methodology, ML plays an increasingly important role in the prevention and treatment of VTE.Meanwhile, significant progresses have been made on relevant research.ML can help identify the risk factors for VTE and establish a targeted risk prediction model.Through the integration of multi-modality data, ML can assist clinicians to quickly and accurately diagnose and evaluate the severity of VTE.In the field of VTE treatment, ML can assist in making clinical decisions like the use of anticoagulant drugs, dosages and course of treatment, and predicting drug-related adverse effects, especially the risk of bleeding.In addition, ML can also assist in the development of new drugs by exploring the pathogenesis of VTE to find intervention targets.
Key words:
Venous thromboembolism; Artificial intelligence; Machine learning
Contributor Information
Xi Linfeng
China-Japan Friendship School of Clinical Medicine, Capital Medical University, Beijing 100069, China
Yang Haoyu
China-Japan Friendship School of Clinical Medicine, Peking University, Beijing 100191, China
Liu Min
Department of Radiology, China-Japan Friendship Hospital, Beijing 100029, China
Zhai Zhenguo
National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, Beijing 100029, China
Wang Chen
National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, China-Japan Friendship Hospital, Beijing 100029, China
