Don't be misled: 3 misconceptions about external validation of clinical prediction models.

Clinical prediction models provide risks of health outcomes that can inform patients and support medical decisions. However, most models never make it to actual implementation in practice. A commonly heard reason for this lack of implementation is that prediction models are often not externally validated.

Development and clinical impact assessment of a machine-learning model for early prediction of late-onset sepsis.

Background And Aim: Preterm infants are prone to neonatal infections such as late-onset sepsis (LOS). The consequences of LOS can be severe and potentially life-threatening. Unfortunately, LOS often presents with unspecific symptoms, and early screening laboratory tests have limited diagnostic value and are often late.

A Perspective on a Quality Management System for AI/ML-Based Clinical Decision Support in Hospital Care.

Although many artificial intelligence (AI) and machine learning (ML) based algorithms are being developed by researchers, only a small fraction has been implemented in clinical-decision support (CDS) systems for clinical care. Healthcare organizations experience significant barriers implementing AI/ML models for diagnostic, prognostic, and monitoring purposes. In this perspective, we delve into the numerous and diverse quality control measures and responsibilities that emerge when moving from AI/ML-model development in a research environment to deployment in clinical care.

The Sleep Well Baby project: an automated real-time sleep-wake state prediction algorithm in preterm infants.

Study Objectives: Sleep is an important driver of early brain development. However, sleep is often disturbed in preterm infants admitted to the neonatal intensive care unit (NICU). We aimed to develop an automated algorithm based on routinely measured vital parameters to classify sleep-wake states of preterm infants in real-time at the bedside.

Efficacy of subcutaneous allergen immunotherapy in atopic dogs: A retrospective study of 664 cases.

Background: Canine atopic dermatitis (cAD) is an allergic skin disease affecting approximately 10% of dogs. allergen-specific immunotherapy (ASIT) is currently the only treatment option able to induce tolerance to the causative allergens.

Objective: To retrospectively establish the efficacy of ASIT in atopic dogs.

Identification of parameters and formulation of a statistical and machine learning model to identify Babesia canis infections in dogs using available ADVIA hematology analyzer data.

Background: Canine babesiosis is an important tick-borne disease in endemic regions. One of the relevant subspecies in Europe is Babesia canis, and it can cause severe clinical signs such as hemolytic anemia. Apart from acute clinical symptoms dogs can also have a more chronic disease development or be asymptomatic carriers.

Strong Support for the Millisecond Pulsar Origin of the Galactic Center GeV Excess.

Using γ-ray data from the Fermi Large Area Telescope, various groups have identified a clear excess emission in the Inner Galaxy, at energies around a few GeV. This excess resembles remarkably well a signal from dark-matter annihilation. One of the most compelling astrophysical interpretations is that the excess is caused by the combined effect of a previously undetected population of dim γ-ray sources.