Biofeedback rehabilitation in children with encopresis due to retentive constipation using simple tools: a real-world study in a French paediatric centre.

This study aimed to describe the effectiveness of biofeedback (BFB) rehabilitation in children with retentive encopresis (RE). A retrospective, single-institution study was conducted in children with BFB sessions for RE between 2017 and 2020. Manometry data and associated envy scores were analysed.

Active learning for extracting rare adverse events from electronic health records: A study in pediatric cardiology.

Objective: Automate the extraction of adverse events from the text of electronic medical records of patients hospitalized for cardiac catheterization.

Methods: We focused on events related to cardiac catheterization as defined by the NCDR-IMPACT registry. These events were extracted from the Necker Children's Hospital data warehouse.

Facilitating phenotyping from clinical texts: the medkit library.

Summary: Phenotyping consists in applying algorithms to identify individuals associated with a specific, potentially complex, trait or condition, typically out of a collection of Electronic Health Records (EHRs). Because a lot of the clinical information of EHRs are lying in texts, phenotyping from text takes an important role in studies that rely on the secondary use of EHRs. However, the heterogeneity and highly specialized aspect of both the content and form of clinical texts makes this task particularly tedious, and is the source of time and cost constraints in observational studies.

Machine learning approaches for the discovery of clinical pathways from patient data: A systematic review.

Background: Clinical pathways are sequences of events followed during the clinical care of a group of patients who meet pre-defined criteria. They have many applications ranging from healthcare evaluation and optimization to clinical decision support. These pathways can be discovered from existing healthcare data, in particular with machine learning which is a family of methods used to learn patterns from data.

Deep Reinforcement Learning for personalized diagnostic decision pathways using Electronic Health Records: A comparative study on anemia and Systemic Lupus Erythematosus.

Background: Clinical diagnoses are typically made by following a series of steps recommended by guidelines that are authored by colleges of experts. Accordingly, guidelines play a crucial role in rationalizing clinical decisions. However, they suffer from limitations, as they are designed to cover the majority of the population and often fail to account for patients with uncommon conditions.