Welcome to

Hellenic Complex Systems Laboratory

Established in 1993, Hellenic Complex Systems Laboratory (HCSL) is an innovative research laboratory dedicated to evaluating and reducing uncertainty in complex systems. Employing a transdisciplinary approach, HCSL develops novel clinical, laboratory, research, and educational tools to assess and address uncertainties inherent in complex processes. Please refer to HCSL Publications and HCSL Software for more information on our work.

Our primary research areas are:
 a) Design, evaluation, and optimization of quality control (QC) in laboratory medicine.
 b) Measurement uncertainty evaluation and expression.
 c) Diagnostic accuracy assessment techniques.
 d) Application and methodology of Bayesian inference in medical diagnosis.
Additionally, HCSL explores network science, genetic algorithms (GAs), neural networks (NNs) and statistics of complexity.

Notable achievements include:
 a) 1993: Pioneered the GAs based design of statistical QC (see A Note on Design and Optimization of Statistical QC).
 b) 2009: Developed a theoretical framework and algorithm for optimizing statistical QC of an analytical process based on the reliability of the analytical system and the risk of analytical error (see A Note on Reliability and Risk Based Optimization of Statistical QC).
 c) 2020: Developed a software tool for exploring the relation between diagnostic accuracy and measurement uncertainty (see HCSL Publications on Diagnostic Accuracy).
 d) 2021: Introduced a method for estimating the uncertainty of diagnostic accuracy measures via uncertainty propagation rules (see HCSL Publications on Diagnostic Accuracy).
 e) 2022: Designed one-dimensional convolutional NNs to be applied to QC samples of very small size (see HCSL Publications on NNs based QC).
 f) 2023: Developed a computational tool for parametric and nonparametric Bayesian medical diagnosis (see HCSL Publications on Bayesian Medical Diagnosis).
 g) 2024: Developed a software tool for parametric estimation of Bayesian diagnostic measures and their uncertainty (see HCSL Publications on Bayesian Medical Diagnosis).

HCSL has actively participated in standards-developing committees of the Clinical and Laboratory Standards Institute (CLSI), and has been a founding node of the Network of Excellence in Evolutionary Computing (Evonet).