BACKGROUND:
In high-dimensional data analysis, the complexity of predictive models can be reduced by selecting the most relevant features, which is crucial to reduce data noise and increase model accuracy and interpretability. Thus, in the field of clinical decision making, only the most relevant features from a set of medical descriptors should be considered when determining whether a patient is healthy or not. This statistical approach known as feature selection can be performed through regression or classification, in a supervised or unsupervised manner. Several feature selection approaches using different mathematical concepts have been described in the literature. In the field of classification, a new approach has recently been proposed that uses the y-metric, an index measuring separability between different classes in heart rhythm characterization. The present study proposes a filter approach for feature selection in classification using this y-metric, and evaluates its application to automatic atrial fibrillation detection.

METHODS:
The stability and prediction performance of the [Formula: see text]-metric feature selection approach was evaluated using the support vector machine model on two heart rhythm datasets, one extracted from the PhysioNet database and the other from the database of Marseille University Hospital Center, France (Timone Hospital). Both datasets contained electrocardiogram recordings grouped into two classes: normal sinus rhythm and atrial fibrillation. The performance of this feature selection approach was compared to that of three other approaches, with the first two based on the Random Forest technique and the other on receiver operating characteristic curve analysis.

RESULTS:
The [Formula: see text]-metric approach showed satisfactory results, especially for models with a smaller number of features. For the training dataset, all prediction indicators were higher for our approach (accuracy greater than 99% for models with 5 to 17 features), as was stability (greater than 0.925 regardless of the number of features included in the model). For the validation dataset, the features selected with the y-metric approach differed from those selected with the other approaches; sensitivity was higher for our approach, but other indicators were similar.

CONCLUSION:
This filter approach for feature selection in classification opens up new methodological avenues for atrial fibrillation detection using short electrocardiogram recordings.

Background:
Measuring the quality and performance of health care is a major challenge in improving the efficiency of a health system. Patient experience is one important measure of the quality of health care, and the use of patient-reported experience measures (PREMs) is recommended. The aims of this project are 1) to develop item banks of PREMs that assess the quality of health care for adult patients with psychiatric disorders (schizophrenia, bipolar disorder, and depression) and to validate computerized adaptive testing (CAT) to support the routine use of PREMs; and 2) to analyze the implementation and acceptability of the CAT among patients, professionals, and health authorities.

Methods:
This multicenter and cross-sectional study is based on a mixed method approach, integrating qualitative and quantitative methodologies in two main phases: 1) item bank and CAT development based on a standardized procedure, including conceptual work and definition of the domain mapping, item selection, calibration of the item bank and CAT simulations to elaborate the administration algorithm, and CAT validation; and 2) a qualitative study exploring the implementation and acceptability of the CAT among patients, professionals, and health authorities.

Discussion:
The development of a set of PREMs on quality of care in mental health that overcomes the limitations of previous works (ie, allowing national comparisons regardless of the characteristics of patients and care and based on modern testing using item banks and CAT) could help health care professionals and health system policymakers to identify strategies to improve the quality and efficiency of mental health care.

Introduction:
Les technologies de l’information et de la communication ont permis la naissance du web 2.0, caractérisé par la mise en place et l’utilisation de nouveaux outils collaboratifs de communication tels que les blogs, les wikis, les fils RSS et les réseaux sociaux. En s’appropriant ces outils, une médecine participative basée sur le partage d’informations et d’expériences entre professionnels, patients et tout acteur de la santé s’est développée. Depuis juin 2012, une communauté médicale échange sur Twitter avec le hashtag #DocTocToc et contribue à la naissance de la e-santé sur ce réseau social. L’objectif de cette étude est d’analyser les principales thématiques des demandes effectuées via le hashtag #DocTocToc par les médecins généralistes entre juin 2012 et mars 2017.

Méthodes:
Une collecte de données par une méthode de « web scraping » a permis de constituer un corpus de tweets dont les auteurs ont été identifiés manuellement afin de procéder à un échantillonnage, de façon à ne conserver que les tweets émis par les médecins généralistes. Une étape de prétraitement a permis de transformer les formes potentiellement non reconnues par les logiciels de traitement du langage naturel. Le corpus a été appréhendé à l’aide de deux approches : une approche lexicale via le logiciel Iramuteq® et une indexation terminologique par l’extracteur de concepts multi-terminologiques (ECMT) du Catalogue et index des sites médicaux francophones (CISMeF).

Résultats:
Sur les 12 716 tweets recueillis, 7366 étaient rédigés par des médecins généralistes et ont été analysés. L’approche lexicale détermine deux grands mondes lexicaux représentés sous forme de dendrogramme, l’un en lien avec les demandes médico administratives relatives à la gestion du cabinet et à la prise en charge sociale du patient, l’autre en lien avec les demandes d’ordre purement médicales. La méthode d’indexation terminologique met en évidence les spécialités médicales pourvoyeuses de demandes de télé-expertise : gynécologie, neurologie, infectiologie, pédiatrie, cardiologie, dermatologie ; et permet de les croiser avec l’objectif de la demande : diagnostic, thérapeutique.

Conclusion:
Sur Twitter®, le hashtag #DocTocToc est utilisé par les médecins généralistes comme un espace de partage informel d’informations en matière de santé mais aussi de gestion de problèmes administratifs et sociaux. Le DocsTocToc se présente comme un groupe d’échange de pratique à grande échelle ou le médecin compte sur l’avis de ses pairs.(Fig. 1)

Background:
The aim of this study was to propose an alternative approach to item response theory (IRT) in the development of computerized adaptive testing (CAT) in quality of life (QoL) for patients with multiple sclerosis (MS). This approach relied on decision regression trees (DRTs). A comparison with IRT was undertaken based on precision and validity properties.

Materials and methods:
DRT- and IRT-based CATs were applied on items from a unidi-mensional item bank measuring QoL related to mental health in MS. The DRT-based approach consisted of CAT simulations based on a minsplit parameter that defines the minimal size of nodes in a tree. The IRT-based approach consisted of CAT simulations based on a specified level of measurement precision. The best CAT simulation showed the lowest number of items and the best levels of precision. Validity of the CAT was examined using sociodemographic, clinical and QoL data.

Results:
CAT simulations were performed using the responses of 1,992 MS patients. The DRT-based CAT algorithm with minsplit = 10 was the most satisfactory model, superior to the best IRT-based CAT algorithm. This CAT administered an average of nine items and showed satisfactory precision indicators (R = 0.98, root mean square error [RMSE] = 0.18). The DRT-based CAT showed convergent validity as its score correlated significantly with other QoL scores and showed satisfactory discriminant validity.
Conclusion: We presented a new adaptive testing algorithm based on DRT, which has equivalent level of performance to IRT-based approach. The use of DRT is a natural and intuitive way to develop CAT, and this approach may be an alternative to IRT.