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Objective: Ambulatory arterial stiffness index (AASI) is an index which indicates arterial stiffness. This work aims to explore the mathematical relationship between AASI and mean value of PP (P P ‾), and reveal the importance of P P ‾ during AASI estimating. Meanwhile, a well-performing AASI estimation model is presented. Methods: To evaluate AASI, electrocardiograph (ECG) signal, photoplethysmogram (PPG) signal and arterial blood pressure (ABP) are used as the source of AASI estimation. Features are extracted from the above three signals. Meanwhile, fitting curve analysis and regression models are implemented to describe the relationship between AASI and P P ‾. Results: Among three fitting curves on AASI and P P ‾ , cubic polynomial curve performs best. The introduction of feature P P ‾ in AASI estimation reduced LR's MAE from 0.0556 to 0.0372, SVMR's MAE from 0.0413 to 0.0343 and RFR's MAE from 0.0386 to 0.0256. All three estimation models obtain considerable improvement, especially on the previous worst-performing linear regression. Significance: This work presents the mathematical association between AASI and P P ‾. AASI estimation using regression models can be significantly improved by involving P P ‾ as its key feature, which is not only meaningful for exploring the connection between vascular elasticity function and pulse pressure, but also hold importance for the diagnosis of cardiovascular arteriosclerosis and atherosclerosis at the early stage. • Mathematical relation between AASI and P P ‾. • The fitting formula of AASI and P P ‾. • Results indicate enhanced AASI estimation with P P ‾ in regression methods. [ABSTRACT FROM AUTHOR]

This study constructs a composite indicator system covering the core dimensions of medical equipment input and output. Based on this system, an innovative cone-constrained data envelopment analysis (DEA) model is designed. The model integrates the advantages of the analytic hierarchy process (AHP) with an improved criterion importance through intercriteria correlation (CRITIC) method to determine subjective and objective weights and employs game theory to obtain the final combined weights, which are further incorporated as constraints to form the cone-constrained DEA model. Finally, a bidirectional long short-term memory (Bi-LSTM) model with an attention mechanism is introduced for integration, aiming to provide a novel and practical model for evaluating the effectiveness of medical equipment. The proposed model has essential reference value for optimizing medical equipment management decision-making and investment strategies. [ABSTRACT FROM AUTHOR]