Publications by authors named "T Chatzikonstantinou"
Article Synopsis
- The study focuses on chronic diseases like Chronic Lymphocytic Leukemia (CLL), which exhibit diverse outcomes, highlighting the need for better predictive models.
- A machine learning approach is used to forecast patient outcomes, aiming to enhance the reliability of these predictions in treating complex conditions.
- Conformal Prediction is integrated into the model to measure uncertainty and deliver personalized predictions, making the results more applicable in clinical settings.
Relying on our experience on the development of data registration and management systems for clinical and biological data coming from patients with hematological malignancies, as well as on the design of strategies for data collection and analysis to support multi-center, clinical association studies, we designed a framework for the standardized collection and transformation of clinically relevant real-world data into evidence, to meet the challenges of gathering biomedical data collected during daily clinical practice in order to promote basic and clinical research.
View Article and Find Full Text PDFPatients with chronic lymphocytic leukemia (CLL) exhibit diverse clinical outcomes. An expanding array of genetic tests is now employed to facilitate the identification of patients with high-risk disease and inform treatment decisions. These tests encompass molecular cytogenetic analysis, focusing on recurrent chromosomal alterations, particularly del(17p).
View Article and Find Full Text PDFArticle Synopsis
- * The updated recommendations suggest that instead of setting a specific variant allele frequency (VAF) cut-off, laboratories should focus on validating their methods for TP53 analysis, taking into account clinical context and treatment options.
- * A simplified algorithm for classifying TP53 variants and a template for clinical reporting are introduced to help clinicians correctly interpret lab results, reducing chances of mismanagement in patient care and enhancing patient stratification in clinical trials.