AIMC Topic: Machine Learning

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Enhancing Trust by a Keycloak-Flower Integration for Federated Machine Learning.

Studies in health technology and informatics
Since its introduction, federated learning (FL) has attracted a lot of attention in the medical field, but its actual application in healthcare organisations remains limited. Flower is a leading FL framework known for its good documentation and wide ...

Does Whole Brain Radiomics on Multimodal Neuroimaging Make Sense in Neuro-Oncology? A Proof of Concept Study.

Studies in health technology and informatics
Employing a whole-brain (WB) mask as a region of interest for extracting radiomic features is a feasible, albeit less common, approach in neuro-oncology research. This study aims to evaluate the relationship between WB radiomic features, derived from...

Intelligent System for Automated Spheroid Segmentation Using Machine Learning.

Studies in health technology and informatics
Image segmentation is a crucial task of medical image processing, including the analysis of multicellular tumour spheroids (MTSs), a common in vitro model used in cancer research for drug screening. Accurate segmentation of MTSs images allows the ext...

How Useful Is Synthetic Data in Developing Predictive Models for Health?

Studies in health technology and informatics
Synthetic data, generated using generative AI techniques, closely mimics the characteristics of real data while enhancing privacy for sensitive health data. This study evaluates synthetic tabular data based on fidelity and utility for predictive mode...

Utilising Machine Learning for Better Mental Health and Decision Making: A Case Study of Timebanking UK.

Studies in health technology and informatics
This study explores how the integration of predictive models with machine learning and natural language processing can optimise community-based service operations, using Timebanking UK as a case study. The research evaluated these models in terms of ...

GRU-D Characterizes Age-Specific Temporal Missingness in MIMIC-IV.

Studies in health technology and informatics
Temporal missingness, defined as unobserved patterns in time series, and its predictive potentials represent an emerging area in clinical machine learning. We trained a gated recurrent unit with decay mechanisms, called GRU-D, for a binary classifica...

XGBOrdinal: An XGBoost Extension for Ordinal Data.

Studies in health technology and informatics
We propose XGBOrdinal, an extension of XGBoost designed for ordinal classification problems commonly found in fields like medicine, where outcomes are often represented as scores, scales, stages, or grades. The proposed approach builds on the theoret...

An Interpretable Model for Predicting Acute Myocardial Infarction in Distinct Patient Profiles.

Studies in health technology and informatics
INTRODUCTION: Acute myocardial infarction (AMI) is highly prevalent (3.8% in developed countries), affecting heterogenous populations, and can be influenced by varied factors, including demographics, clinical risk factors, and comorbidities. Identify...

Clinical Requirements for Transparent Machine Learning Model Information: A Mixed Methods Study Protocol.

Studies in health technology and informatics
Limited transparency of machine learning models poses risks their effective use. Through semi-structured interviews with physicians, this mixed methods study will qualitatively identify requirements for transparent machine learning model information ...

Assessing Healthcare Stakeholder Understanding of Machine Learning Documentation.

Studies in health technology and informatics
Artificial Intelligence (AI) has significantly advanced clinical decision support systems in healthcare, particularly using Machine Learning (ML) models. However, the technical nature of current ML model documentation often leads to lack of comprehen...