AIMC Topic: Decision Support Techniques
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Validation of an Electronic Health Record-Based Machine Learning Model Compared With Clinical Risk Scores for Gastrointestinal Bleeding.
Gastroenterology
Jul 5, 2024
BACKGROUND & AIMS: Guidelines recommend use of risk stratification scores for patients presenting with gastrointestinal bleeding (GIB) to identify very-low-risk patients eligible for discharge from emergency departments. Machine learning models may o...
Deep learning prediction of survival in patients with heart failure using chest radiographs.
The international journal of cardiovascular imaging
Jul 5, 2024
Heart failure (HF) is associated with high rates of morbidity and mortality. The value of deep learning survival prediction models using chest radiographs in patients with heart failure is currently unclear. The aim of our study is to develop and val...
Exploring decision-makers' challenges and strategies when selecting multiple systematic reviews: insights for AI decision support tools in healthcare.
BMJ open
Jul 5, 2024
BACKGROUND: Systematic reviews (SRs) are being published at an accelerated rate. Decision-makers may struggle with comparing and choosing between multiple SRs on the same topic. We aimed to understand how healthcare decision-makers (eg, practitioners...
Predicting Successful Chronic Total Occlusion Crossing With Primary Antegrade Wiring Using Machine Learning.
JACC. Cardiovascular interventions
Jul 3, 2024
BACKGROUND: There is limited data on predicting successful chronic total occlusion crossing using primary antegrade wiring (AW).
Using machine-learning models to predict extubation failure in neonates with bronchopulmonary dysplasia.
BMC pulmonary medicine
Jul 1, 2024
AIM: To develop a decision-support tool for predicting extubation failure (EF) in neonates with bronchopulmonary dysplasia (BPD) using a set of machine-learning algorithms.
A machine learning tool for identifying patients with newly diagnosed diabetes in primary care.
Primary care diabetes
Jun 28, 2024
BACKGROUND AND AIM: It is crucial to identify a diabetes diagnosis early. Create a predictive model utilizing machine learning (ML) to identify new cases of diabetes in primary health care (PHC).
Owning Decisions: AI Decision-Support and the Attributability-Gap.
Science and engineering ethics
Jun 18, 2024
Artificial intelligence (AI) has long been recognised as a challenge to responsibility. Much of this discourse has been framed around robots, such as autonomous weapons or self-driving cars, where we arguably lack control over a machine's behaviour a...
Predicting ICU Interventions: A Transparent Decision Support Model Based on Multivariate Time Series Graph Convolutional Neural Network.
IEEE journal of biomedical and health informatics
Jun 6, 2024
In this study, we present a novel approach for predicting interventions for patients in the intensive care unit using a multivariate time series graph convolutional neural network. Our method addresses two critical challenges: the need for timely and...
Does machine learning have a high performance to predict obesity among adults and older adults? A systematic review and meta-analysis.
Nutrition, metabolism, and cardiovascular diseases : NMCD
May 29, 2024
AIM: Machine learning may be a tool with the potential for obesity prediction. This study aims to review the literature on the performance of machine learning models in predicting obesity and to quantify the pooled results through a meta-analysis.
Leveraging artificial intelligence and software engineering methods in epidemiology for the co-creation of decision-support tools based on mechanistic models.
Preventive veterinary medicine
May 25, 2024
Epidemiological modeling is a key lever for infectious disease control and prevention on farms. It makes it possible to understand the spread of pathogens, but also to compare intervention scenarios even in counterfactual situations. However, the act...
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