A novel approach to screening patents for securitization: a machine learning-based predictive analysis of high-quality basic asset

Abstract

PurposeThis paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.Design/methodology/approachThis paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated.FindingsThe machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it.Originality/valueThe main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS.

Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review

“…Intravascular optical coherence tomography (IVOCT) is a sophisticated imaging modality that provides accurate visualization of microstructural plaque constituents associated with an increased susceptibility to rupture [24]. The use of carotid ultrasound (CUS) is an economically efficient and non-intrusive imaging methodology employed for the assessment of asymptomatic atherosclerotic carotid plaque and the quantification of carotid intima-media thickness (cIMT) [25,26]. CVD and stroke risk assessment in patients with RA can be significantly enhanced through the utilization of carotid ultrasound (CUS) as a diagnostic tool [7,27].…”