ABSTRACT
Using mathematical models of physiological systems in medicine has allowed for the development of diagnostic, treatment, and medical educational tools. However, their complexity restricts, in most cases, their application for predictive, preventive, and personalized purposes. Although there are strategies that reduce the complexity of applying models based on fitting techniques, most of them are focused on a single instant of time, neglecting the effect of the system's temporal evolution. The objective of this research was to introduce a dynamic fitting strategy for physiological models with an extensive array of parameters and a constrained amount of experimental data. The proposed strategy focused on obtaining better predictions based on the temporal trends in the system's parameters and being capable of predicting future states. The study utilized a cardiorespiratory model as a case study. Experimental data from a longitudinal study of healthy adult subjects undergoing aerobic exercise were used for fitting and validation. The model predictions obtained in a steady state using the proposed strategy and the traditional single-fit approach were compared. The most successful outcomes were primarily linked to the proposed strategy, exhibiting better overall results regarding accuracy and behavior than the traditional population fitting approach at a single instant in time. The results evidenced the usefulness of the dynamic fitting strategy, highlighting its use for predictive, preventive, and personalized applications.
ABSTRACT
Applying complex mathematical models of physiological systems is challenging due to the large number of parameters. Identifying these parameters through experimentation is difficult, and although procedures for fitting and validating models are reported, no integrated strategy exists. Additionally, the complexity of optimization is generally neglected when the number of experimental observations is restricted, obtaining multiple solutions or results without physiological justification. This work proposes a fitting and validation strategy for physiological models with many parameters under various populations, stimuli, and experimental conditions. A cardiorespiratory system model is used as a case study, and the strategy, model, computational implementation, and data analysis are described. Using optimized parameter values, model simulations are compared to those obtained using nominal values, with experimental data as a reference. Overall, a reduction in prediction error is achieved compared to that reported for model building. Furthermore, the behavior and accuracy of all the predictions in the steady state were improved. The results validate the fitted model and provide evidence of the proposed strategy's usefulness.
ABSTRACT
Conducting research associated with mechanically ventilated patients often requires the recording of several biomedical signals to dispose of multiple sources of information to perform a robust analysis. This is especially important in the analysis of the relationship between pressure, volume and flow, signals available from mechanical ventilators, and other biopotentials such as the electromyogram of respiratory muscles, intrinsically related with the ventilatory process, but not commonly recorded in the clinical practice. Despite the usefulness of recording signals from multiple sources, few medical devices include the possibility of synchronizing its data with other provided by different biomedical equipment and some may use inaccurate sampling frequencies. Even thought a variant or inaccurate sampling rate does not affect the monitoring of critical patients, it restricts the study of simultaneous related events useful in research of respiratory system activity. In this article a device for temporal synchronization of signals recorded from multiple biomedical devices is described as well as its application in the study of patients undergoing mechanical ventilation with research purposes.