The Use of Machine Learning Techniques to Predict Deep Vein Thrombosis in Rehabilitation Inpatients

• Published in: Clinical and applied thrombosis/hemostasis 2023-01, Vol.29, p.10760296231179438-10760296231179438
• Main Authors: Hou, Tingting, Qiao, Wei, Song, Sijin, Guan, Yingchao, Zhu, Chunyang, Yang, Qing, Gu, Qi, Sun, Li, Liu, Su
• Format: Article
• Language: English
• Subjects: Algorithms; Humans; Inpatients; Machine Learning; Neural networks; Original Manuscript; Prognosis; Quality of life; Regression analysis; Rehabilitation; Thrombosis; Venous Thrombosis - diagnosis
• ISSN: 1076-0296; 1938-2723
• DOI: 10.1177/10760296231179438

Background Rehabilitation is crucial to recovering patients’ dysfunction, improving their life quality, and promoting an early return to their family and society. In China, most patients in rehabilitation units are patients transferred from neurology, neurosurgery, and orthopedics, and most of these patients face problems such as continuously bedridden or varying degrees of limb dysfunction, all of which are risk factors for deep venous thrombosis. The formation of deep venous thrombosis can delay the recovery process and result in significant morbidity, mortality, and higher healthcare costs, so early detection and individualized treatment are needed. Machine learning algorithms can help develop more precise prognostic models, which can be of great significance in the development of rehabilitation training programs. In this study, we aimed to develop a model of deep venous thrombosis for inpatients in the Department of Rehabilitation Medicine at the Affiliated Hospital of Nantong University using machine learning methods. Methods We analyzed and compared 801 patients in the Department of Rehabilitation Medicine using machine learning. Support vector machine, logistic regression, decision tree, random forest classifier, and artificial neural network were used to build models. Results Artificial neural network was the better predictor than other traditional machine learnings. D-dimer levels, bedridden time, Barthel Index, and fibrinogen degradation products were common predictors of adverse outcomes in these models. Conclusions Risk stratification can help healthcare practitioners to achieve improvements in clinical efficiency and specify appropriate rehabilitation training programs.