Longitudinal Model Shifts of Machine Learning-Based Clinical Risk Prediction Models: Evaluation Study of Multiple Use Cases Across Different Hospitals.

Background: In recent years, machine learning (ML)-based models have been widely used in clinical domains to predict clinical risk events. However, in production, the performances of such models heavily rely on changes in the system and data. The dynamic nature of the system environment, characterized by continuous changes, has significant implications for prediction models, leading to performance degradation and reduced clinical efficacy.

Evaluating gender bias in ML-based clinical risk prediction models: A study on multiple use cases at different hospitals.

Background: An inherent difference exists between male and female bodies, the historical under-representation of females in clinical trials widened this gap in existing healthcare data. The fairness of clinical decision-support tools is at risk when developed based on biased data. This paper aims to quantitatively assess the gender bias in risk prediction models.

Machine Learning-Based Prediction Models for Different Clinical Risks in Different Hospitals: Evaluation of Live Performance.

Background: Machine learning algorithms are currently used in a wide array of clinical domains to produce models that can predict clinical risk events. Most models are developed and evaluated with retrospective data, very few are evaluated in a clinical workflow, and even fewer report performances in different hospitals. In this study, we provide detailed evaluations of clinical risk prediction models in live clinical workflows for three different use cases in three different hospitals.

Exploring Novel Funding Strategies for Innovative Medical Research: The HORAO Crowdfunding Campaign.

Background: The rise of the internet and social media has boosted online crowdfunding as a novel strategy to raise funds for kick-starting projects, but it is rarely used in science.

Objective: We report on an online crowdfunding campaign launched in the context of the neuroscience project HORAO. The aim of HORAO was to develop a noninvasive real-time method to visualize neuronal fiber tracts during brain surgery in order to better delineate tumors and to identify crucial cerebral landmarks.

On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities.

As artificial intelligence (AI) systems begin to make their way into clinical radiology practice, it is crucial to assure that they function correctly and that they gain the trust of experts. Toward this goal, approaches to make AI "interpretable" have gained attention to enhance the understanding of a machine learning algorithm, despite its complexity. This article aims to provide insights into the current state of the art of interpretability methods for radiology AI.

Volumetric Food Quantification Using Computer Vision on a Depth-Sensing Smartphone: Preclinical Study.

Background: Quantification of dietary intake is key to the prevention and management of numerous metabolic disorders. Conventional approaches are challenging, laborious, and lack accuracy. The recent advent of depth-sensing smartphones in conjunction with computer vision could facilitate reliable quantification of food intake.

Dehydroepiandrosterone (DHEA) modulates the activity and the expression of lymphocyte subpopulations induced by cecal ligation and puncture.

Dehydroepiandrosterone (DHEA) exerts a variety of positive effects on the immunologic alterations after trauma and sepsis. We therefore measured the therapeutic efficacy of DHEA after cecal ligation and puncture (CLP) on the expression of lymphocyte subpopulations and on the delayed type hypersensitivity (DTH) reaction. Male NMRI-mice were randomly assigned to four different treatment groups.