Estimation of the epidemiology of dementia and associated neuropsychiatric symptoms by applying machine learning to real-world data.

Introduction: Incidence rates of dementia-related neuropsychiatric symptoms (NPS) are not known and this hampers the assessment of their population burden. The objective of this study was to obtain an approximate estimate of the population incidence and prevalence of both dementia and NPS.

Methods: Given the dynamic nature of the population with dementia, a retrospective study was conducted within the database of the Basque Health Service (real-world data) at the beginning and end of 2019.

Estimation of the epidemiology of dementia and associated neuropsychiatric symptoms by applying machine learning to real-world data.

Introduction: Incidence rates of dementia-related neuropsychiatric symptoms (NPS) are not known and this hampers the assessment of their population burden. The objective of this study was to obtain an approximate estimate of the population incidence and prevalence of both dementia and NPS.

Methods: Given the dynamic nature of the population with dementia, a retrospective study was conducted within the database of the Basque Health Service (real-world data) at the beginning and end of 2019.

Validation of Random Forest Machine Learning Models to Predict Dementia-Related Neuropsychiatric Symptoms in Real-World Data.

Background: Neuropsychiatric symptoms (NPS) are the leading cause of the social burden of dementia but their role is underestimated.

Objective: The objective of the study was to validate predictive models to separately identify psychotic and depressive symptoms in patients diagnosed with dementia using clinical databases representing the whole population to inform decision-makers.

Methods: First, we searched the electronic health records of 4,003 patients with dementia to identify NPS.

Calibration model maintenance in melamine resin production: Integrating drift detection, smart sample selection and model adaptation.

The physico-chemical properties of Melamine Formaldehyde (MF) based thermosets are largely influenced by the degree of polymerization (DP) in the underlying resin. On-line supervision of the turbidity point by means of vibrational spectroscopy has recently emerged as a promising technique to monitor the DP of MF resins. However, spectroscopic determination of the DP relies on chemometric models, which are usually sensitive to drifts caused by instrumental and/or sample-associated changes occurring over time.

Improved quantification of important beer quality parameters based on nonlinear calibration methods applied to FT-MIR spectra.

During the production process of beer, it is of utmost importance to guarantee a high consistency of the beer quality. For instance, the bitterness is an essential quality parameter which has to be controlled within the specifications at the beginning of the production process in the unfermented beer (wort) as well as in final products such as beer and beer mix beverages. Nowadays, analytical techniques for quality control in beer production are mainly based on manual supervision, i.

Evolving chemometric models for predicting dynamic process parameters in viscose production.

In viscose production, it is important to monitor three process parameters in order to assure a high quality of the final product: the concentrations of H(2)SO(4), Na(2)SO(4) and Z(n)SO(4). During on-line production these process parameters usually show a quite high dynamics depending on the fiber type that is produced. Thus, conventional chemometric models, which are trained based on collected calibration spectra from Fourier transform near infrared (FT-NIR) measurements and kept fixed during the whole life-time of the on-line process, show a quite imprecise and unreliable behavior when predicting the concentrations of new on-line data.