Development of a comprehensive database for research on foetal acidosis.

Objective: To develop a research database for mother-and-child clinical and laboratory data and digital foetal heart rate (FHR) recordings.

Methods: The Base Bien Naître (BBN) database was derived from a single-centre health data warehouse. It contains exhaustive data on all parturients with a singleton pregnancy, a vaginal or caesarean delivery in labour with a cephalic presentation after at least 37 weeks of amenorrhea, and a live birth between February 1st, 2011, and December 31st, 2018.

Comparison of the validity, perceived usefulness and usability of I-MeDeSA and TEMAS, two tools to evaluate alert system usability: a study protocol.

Introduction: Research has shown that improvements to the usability of medication alert systems are needed. For designers and decisions-makers to assess usability of their alert systems, two paper-based tools are currently available: the instrument for evaluating human-factors principles in medication-related decision support alerts (I-MeDeSA) and the tool for evaluating medication alerting systems (TEMAS). This study aims to compare the validity, usability and usefulness of both tools to identify their strengths and limitations and assist designers and decision-makers in making an informed decision about which tool is most suitable for assessing their current or prospective system.

A Deep Learning Framework for Automated ICD-10 Coding.

The International Statistical Classification of Diseases and Related Health Problems (ICD) is one of the widely used classification system for diagnoses and procedures to assign diagnosis codes to Electronic Health Record (EHR) associated with a patient's stay. The aim of this paper is to propose an automated coding system to assist physicians in the assignment of ICD codes to EHR. For this purpose, we created a pipeline of Natural Language Processing (NLP) and Deep Learning (DL) models able to extract the useful information from French medical texts and to perform classification.

Use of automated fetal heart rate analysis to identify risk factors for umbilical cord acidosis at birth.

Objective: To identify clinical parameters and intrapartum fetal heart rate parameters associated with a risk of umbilical cord acidosis at birth, using an automated analysis method based on empirical mode decomposition.

Methods: Our single-center study included 381 cases (arterial cord blood pH at birth pHa ≤7.15) and 1860 controls (pHa ≥7.

Fetal heart rate baseline computation with a weighted median filter.

Background: Automated fetal heart rate (FHR) analysis removes inter- and intra-expert variability, and is a promising solution for reducing the occurrence of fetal acidosis and the implementation of unnecessary medical procedures. The first steps in automated FHR analysis are determination of the baseline, and detection of accelerations and decelerations (A/D). We describe a new method in which a weighted median filter baseline (WMFB) is computed and A/Ds are then detected.