AIMC Topic: Machine Learning

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Quality Assessment of Brain MRI Defacing Using Machine Learning.

Studies in health technology and informatics
Defacing of brain magnetic resonance imaging (MRI) scans is a crucial process in medical imaging research aimed at preserving patient privacy while maintaining data integrity. However, existing defacing algorithms are prone to errors, potentially com...

Balancing Acts: Tackling Data Imbalance in Machine Learning for Predicting Myocardial Infarction in Type 2 Diabetes.

Studies in health technology and informatics
Type 2 Diabetes (T2D) is a prevalent lifelong health condition. It is predicted that over 500 million adults will be diagnosed with T2D by 2040. T2D can develop at any age, and if it progresses, it may cause serious comorbidities. One of the most cri...

Assessing the Reliability of Machine Learning Explanations in ECG Analysis Through Feature Attribution.

Studies in health technology and informatics
Feature attribution methods stand as a popular approach for explaining the decisions made by convolutional neural networks. Given their nature as local explainability tools, these methods fall short in providing a systematic evaluation of their globa...

Secure Extraction of Personal Information from EHR by Federated Machine Learning.

Studies in health technology and informatics
Secure extraction of Personally Identifiable Information (PII) from Electronic Health Records (EHRs) presents significant privacy and security challenges. This study explores the application of Federated Learning (FL) to overcome these challenges wit...

Explainable Machine Learning Based Prediction of Severity of Heart Failure Using Primary Electronic Health Records.

Studies in health technology and informatics
Heart Failure (HF) is a life-threatening condition. It affects more than 64 million people worldwide. Early diagnosis of HF is extremely crucial. In this study, we propose utilization of machine learning (ML) models to predict severity of HF from pri...

Machine Learning with Clinical and Intraoperative Biosignal Data for Predicting Cardiac Surgery-Associated Acute Kidney Injury.

Studies in health technology and informatics
Early identification of patients at high risk of cardiac surgery-associated acute kidney injury (CSA-AKI) is crucial for its prevention. We aimed to leverage perioperative clinical and intraoperative biosignal data to develop machine learning models ...

INSAFEDARE Project: Innovative Applications of Assessment and Assurance of Data and Synthetic Data for Regulatory Decision Support.

Studies in health technology and informatics
Digital health solutions hold promise for enhancing healthcare delivery and patient outcomes, primarily driven by advancements such as machine learning, artificial intelligence, and data science, which enable the development of integrated care system...

A Modular Service for Causal Inference in the Context of the Gaia-X Ecosystem.

Studies in health technology and informatics
Causal inference seeks to learn the effect of interventions on outcomes. Its potential in the health domain has been dramatically increasing recently, due to advancements in machine learning, as well as in the growing amount of medical data collected...

Prediction of Poor Glycemic Control in Children with Type 1 Diabetes.

Studies in health technology and informatics
This study developed and validated a machine learning model for predicting glycemic control in children with type 1 diabetes at the time of diagnosis, revealing age at diagnosis as the most informative predictor.

Applying Machine Learning for Prescriptive Support: A Use Case with Unfractionated Heparin in Intensive Care Units.

Studies in health technology and informatics
Continuous unfractionated heparin is widely used in intensive care, yet its complex pharmacokinetic properties complicate the determination of appropriate doses. To address this challenge, we developed machine learning models to predict over- and und...