Celebrating a Milestone: The Launch of Clinomic AI Lab

Today marks a pivotal moment in Clinomic’s journey as we proudly announce the inauguration of our Clinomic AI Lab, spearheaded by Head of Technology Ahmed Hallawa, a gifted researcher and early associate of the company. This milestone is not just an accomplishment but also a realization of a long-cherished vision. From the inception of Clinomic, […]

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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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A Novel NLP-FUZZY System Prototype for Information Extraction from Medical Guidelines

Clinomic NLP-FUZZY System Prototype

Medical guidelines have a significant role in the field of evidence-based medical treatment. The content of a medical guideline is based on a systematic review of clinical evidence with instructions and recommendations that clinicians can refer to. Most of the guidelines are available in an unstructured text format. Hence, clinicians must take a considerable time […]

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Machine Learning in Future Intensive Care: Classification of Stochastic Petri Nets via Continuous-time Markov Chains

Machine Learning in Future Intensive Care

The fast growing digitalization of medicine has facilitated the collection of patient data in databases. A smart city must facilitate such databases in its hospitals. However, handling this big data requires new strategies for filtering and processing such high volumes of patient data. In this regard, Machine Learning (ML) algorithms are increasingly applied to support […]

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On the Use of Evolutionary Computation for In-Silico Medicine: Modelling Sepsis via Evolving Continuous Petri Nets

Use of Evolutionary Computation for In-Silico Medicine

Sepsis is one of the leading causes of death in Intensive Care Units (ICU) world-wide. Continuous Petri Nets (CPNs) offer a promising solution in modelling its underlying, complex pathophysiological processes. In this work, we propose a framework to evolve CPNs, i.e. evolve its places, transitions, arc weights, topology, and kinetics. This facilitates modeling complex biological […]

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Heparan Sulfate Induces Necroptosis in Murine Cardiomyocytes: A Medical-In silico Approach Combining In vitro Experiments and Machine Learning

Heparan Sulfate Induces Necroptosis in Murine Cardiomyocytes

Life-threatening cardiomyopathy is a severe, but common, complication associated with severe trauma or sepsis. Several signaling pathways involved in apoptosis and necroptosis are linked to trauma- or sepsis-associated cardiomyopathy. However, the underling causative factors are still debatable. Heparan sulfate (HS) fragments belong to the class of danger/damage-associated molecular patterns liberated from endothelial-bound proteoglycans by heparanase […]

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