ARIA 2016: Care pathways implementing emerging technologies for predictive medicine in rhinitis and asthma across the life cycle

The Allergic Rhinitis and its Impact on Asthma (ARIA) initiative commenced during a World Health Organization workshop in 1999. The initial goals were (1) to propose a new allergic rhinitis classification, (2) to promote the concept of multi-morbidity in asthma a

Identification by cluster analysis of patients with asthma and nasal symptoms using the MASK-air® mHealth app

peer reviewedBackground: The self-reporting of asthma frequently leads to patient misidentification in epidemiological studies. Strategies combining the triangulation of data sources may help to improve the identification of people with asthma. We aimed to combine information from the self-reporting of asthma, medication use and symptoms to identify asthma patterns in the users of an mHealth app. Methods: We studied MASK-air® users who reported their daily asthma symptoms (assessed by a 0-100 visual analogue scale – “VAS Asthma”) at least three times (either in three different months or in any period). K-means cluster analysis methods were applied to identify asthma patterns based on: (i) whether the user self-reported asthma; (ii) whether the user reported asthma medication use and (iii) VAS asthma. Clusters were compared by the number of medications used, VAS asthma levels and Control of Asthma and Allergic Rhinitis Test (CARAT) levels. Findings: We assessed a total of 8,075 MASK-air® users. The main clustering approach resulted in the identification of seven groups. These groups were interpreted as probable: (i) severe/uncontrolled asthma despite treatment (11.9-16.1% of MASK-air® users); (ii) treated and partly-controlled asthma (6.3-9.7%); (iii) treated and controlled asthma (4.6-5.5%); (iv) untreated uncontrolled asthma (18.2-20.5%); (v) untreated partly-controlled asthma (10.1-10.7%); (vi) untreated controlled asthma (6.7-8.5%) and (vii) no evidence of asthma (33.0-40.2%). This classification was validated in a study of 192 patients enrolled by physicians. Interpretation: We identified seven profiles based on the probability of having asthma and on its level of control. mHealth tools are hypothesis-generating and complement classical epidemiological approaches in identifying patients with asthma. © 2022 Sociedade Portuguesa de Pneumologi

Etalonnage direct amélioré

International audienceRegression methods are often used for prediction of concentration of an interest analyte using NIR spectroscopy, the most popular method being PLS regression. Direct calibration methods were never employed since SBC was proposed. We propose another new method called IDC, based on Direct Calibration (DC) improvement

Pretreatments by means of orthogonal projections

This article describes several linear pretreatments based on orthogonal projections. The main differences of these pretreatments lie in the way the information to be removed are identified, using calibration dataset, pure spectra, experimental designs or mathematical models. Removing all the undesired spectral information yields spectra proportional to the net analyte signal, so it is important to collect the most complete information possible, using the complementarities of different approaches. The correction should then be processed with a single Euclidian orthogonal projection that gathers all the information, rather than with successive operations. By embedding Euclidian orthogonal projections into the calibration, it is not necessary to reapply them to new datasets

Improvement of Direct Calibration in spectroscopy

Several linear calibration methods have been proposed for predicting the concentration of a particular compound from a spectrum. Some methods are based on experimental data, such as Partial Least Square Regression. Other methods are based on expert data, e.g. Direct Calibration. This article proposes a new method, called Improved Direct Calibration, which uses expert and experimental information. It performs a projection onto the pure interest spectrum, after correcting it from influence factors. No calibration dataset is necessary to build this model. This method has been successfully applied to the quantification of ethanol in musts during fermentation, using near infra-red spectrometry

Étalonnage robuste basé sur des projections orthogonales et des plans d'expériences. Application à la correction de l'effet de la diffusion sur des spectres PIR de produits turbides.

Correspondance: [email protected] audienceMany pre-processing methods aim at improving calibration model robustness in relation to the effect of an influence factor G. Orthogonal projection methods, such as OSC (Orthogonal Signal Correction) or EPO (External Parameter Orthogonalisation), are particularly well suited to process existing calibration databases. This work proposes a pre-processing strategy for the numerous cases where G variability is missing in the existing calibration database, and where effects of G and Y, the variable of interest, are not independent. The application in this study concerns the correction of the light scattering effect in NIR turbid spectra of grape musts. Ethanol content was thus correctly predicted (RMSEP = 0.5°) on very turbid samples (below 3000 NTU), much better than using all other geometric or multidimensional existing pre-processings tested

Cell Controllers: Analysis and Comparison of Three Major Projects

There is a critical need to achieve Computer Integrated Manufacturing, to link the factory-level functions (Product Design, Process Planning and Manufacturing Resource Planning) with the manufacturing functions (Parts Manufacturing, Product Assembly, and Quality Control). The primary functions performed by this link for all jobs issued to the shop floor, (i.e. all the parts to be manufactured in a specified period of time) include: i) the allocation of resources (machines, material handling devices, etc), and ii) the scheduling of tasks (manufacturing operations, material transfers, etc.). This paper defines these functions and presents the different methods that have been proposed to solve the associated problems. It provides analysis and a critical comparison of the current research at the National Institute of Standards and Technology (NIST, formerly NBS), European Strategic Program for Research and Development in Information Technology (ESPRIT, Project 932), and Computer Aided Manufacturing International (CAM-i) concerning Planning, Scheduling and Control at the Shop-Floor level