AIMC Topic: Machine Learning

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Machine Learning to Improve Decision Support for Preventing Adverse Drug Events.

Studies in health technology and informatics
One approach to preventing adverse drug events (ADEs), such as harmful drug interactions, is the implementation of clinical decision support systems (CDSS). In an ongoing project, we are investigating the accuracy of the rule-based CDSS currently uti...

A Federated Learning Model for the Prediction of Blood Transfusion in Intensive Care Units.

Studies in health technology and informatics
Accurate prediction of blood transfusion requirements is crucial for patient outcomes and resource management in clinical settings. We developed a machine learning model using XGBoost to predict the need for a blood transfusion 2 hours in advance bas...

Predicting Care Times at PACU.

Studies in health technology and informatics
Patients undergoing anesthetic surgery are treated postoperatively in a Post-Anesthesia Care Unit (PACU). Traditional planning methods often fail to account for the complexity of patient data. This study aims to develop a machine learning (ML) tool t...

Machine Learning-Based Clinical Decision Support System for Suicide Risk Management: The PERMANENS Project.

Studies in health technology and informatics
The PERMANENS European project addresses the global public health challenge of self-harm and suicide by developing a machine learning-based Clinical Decision Support System (CDSS) to assist emergency departments (EDs) in providing personalized care. ...

Predicting 1-Year Survival Using Machine Learning in Very Old Patients Before ICU Admission.

Studies in health technology and informatics
Discussions about the benefits of admitting very old individuals to intensive care unit (ICU) remain challenging. We hypothesized that data-driven algorithms could leverage extensive real-life data to provide more accurate long-term predictions. Our ...

Machine Learning Models Predicting Hospital Admissions During Chemotherapy Utilising Longitudinal Symptom Severity Reports and Patient-Reported Outcome Measures.

Studies in health technology and informatics
Chemotherapy toxicity can lead to acute hospital admissions, negatively impacting the healthcare system and patients' well-being. Machine learning (ML) models identifying patients at risk of emergency admissions are often developed on data lacking pa...

Exploring the Role of Digital Twins in Heart Care: Research Directions and Applications.

Studies in health technology and informatics
The concept of digital twins has emerged as a transformative innovation in healthcare. Digital twins are virtual replicas of physical entities that can be updated with real-time data allowing for simulation analysis and optimization. Their applicatio...

A Medical Decision Support System for Automatic Treatment Plan Generation Using Machine Learning Algorithms.

Studies in health technology and informatics
Due to demographic change, health economics is increasingly focused on the quality of life in advanced age and the associated cost aspects. Dementia is one of the key issues in this area and its efficient treatment will become increasingly relevant i...

Subgroup Discovery to Identify Determinants of Influence on CDSS Medication Alert Handling: A Feasibility Study.

Studies in health technology and informatics
Clinical decision support systems (CDSSs) are designed to enhance patient safety by providing alerts to prescribers about potential medication issues. However, a significant proportion of these alerts are ignored, which can compromise patient safety....

On the Harmonisation of Time Series Data for the Optimisation of Machine Learning Using the Example of Rejection Prediction After Kidney Transplantation.

Studies in health technology and informatics
A significant risk following a kidney transplantation is graft loss. The Screen Reject Project has developed a Clinical Data Warehouse (CDWH) as a foundation for a clinical decision support system designed to improve the diagnosis of graft rejections...