A big-data approach to producing descriptive anthropometric references: a feasibility and validation study of paediatric growth charts.

Background: Both national and WHO growth charts have been found to be poorly calibrated with the physical growth of children in many countries. We aimed to generate new national growth charts for French children in the context of huge datasets of physical growth measurements routinely collected by office-based health practitioners.

Methods: We recruited 32 randomly sampled primary care paediatricians and ten volunteer general practitioners from across the French metropolitan territory who used the same electronic medical records software, from which we extracted all physical growth data for the paediatric patients, with anonymisation.

Modular Knowledge-Based Decision Support System Dedicated to a Cooperative Decision to Prevent Cardiovascular Diseases.

Despite the success of artificial intelligence solutions in the recent years, physicians are still reticent to use integrated functionalities to support their decision. Methods used to create these functionalities can be divided into two groups, each being associated to different questions. Data-based methods are seen as black boxes for which it is impossible to understand how the decision is taken; knowledge-based methods need to rely on formalized knowledge sources on the basis of evidence, which can be discussed and criticized by physicians for their use in real life.

Building a Knowledge-Based Tool for Auto-Assessing the Cardiovascular Risk.

The prevention of cardiovascular diseases needs first to quantify the cardiovascular risk. To estimate this risk, French national health authorities provided clinical practice guidelines extending the existing European SCORE, which doesn't include all the cardiovascular risk factors (e.g.

Priority target conditions for algorithms for monitoring children's growth: Interdisciplinary consensus.

Background: Growth monitoring of apparently healthy children aims at early detection of serious conditions through the use of both clinical expertise and algorithms that define abnormal growth. Optimization of growth monitoring requires standardization of the definition of abnormal growth, and the selection of the priority target conditions is a prerequisite of such standardization.

Objective: To obtain a consensus about the priority target conditions for algorithms monitoring children's growth.