Using Clinical Data and AI to reshape COPD – fractional dynamics deep learning models

Chronic Obstructive Pulmonary Disease (COPD) is a progressive inflammatory lung disease that causes obstructed airflow and breathing difficulties. COPD is a major global health burden, yet diagnosis can be challenging. Spirometry is the standard diagnostic test but has limitations in accessibility and accuracy. There is a need for an accurate, non-invasive approach to detect COPD […]

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VentAI: The Future of Mechanical Ventilation for Critically Ill Patients

Introduction In the realm of critical care, the optimization of mechanical ventilation strategies for patients is paramount. Recent developments have seen the introduction of VentAI, a reinforcement learning algorithm designed to dynamically optimize mechanical ventilation regimes for critically-ill patients, offering hope for improved outcomes. Reinforcing Optimal Ventilation Development and Evaluation of VentAI The objective of […]

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New paper: Artificial intelligence and machine learning in intensive care research and clinical application

Hardly any other development is pre-dicted to have a greater impact on our daily working life than artificial intelli-gence (AI). A popular field of application of artificial intelligence is the so-called “machine learning”, the discipline that deals with the generation of computerised knowledge from experience through self-adaptive algoriths. Especially the high practical relevance, for example, […]

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New publication: Deep learning on Intensive Care Units to visually track medical consumables

Deep learning on Intensive Care Units

High numbers of consumable medical materials (eg, sterile needles and swabs) are used during the daily routine of intensive care units (ICUs) worldwide. Although medical consumables largely contribute to total ICU hospital expenditure, many hospitals do not track the individual use of materials. Current tracking solutions meeting the specific requirements of the medical environment, like […]

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New approach in Likelihood-Based Adaptive Learning for Stochastic State-Based Models

Likelihood-Based Adaptive Learning for Stochastic State-Based Models

SSMs are a useful modelling tool in systems biology and medicine. While models in these disciplines are traditionally hand-crafted, an automated generation based on experimental data becomes a topic of research interest. In particular, our goal was to classify measured processes using the generated models. An innovative likelihood-based adaptive learning approach capable of learning the […]

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DMEA Young Investigators Award of the Digital Health Industry goes to Clinomic CEO Dr. Arne Peine

DMEA Young Investigators Award Dr. Arne Peine

The Young Investigators Award of the Digital Health Industry honours outstanding works in the fields of medical informatics and health management. This year’s award with prize money of 2,000 euros went to Clinomic’s Chief Executive Officer Dr. Arne Peine. It is endowed with a total of 6,500 euros. He convinced the jury with his topic […]

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