2nd International Conference on Chemo and Bioinformatics ICCBIKG 2023 (379-381)
АУТОР(И) / AUTHOR(S): Tijana Geroski, Nenad Filipović
Е-АДРЕСА / E-MAIL: tijanas@kg.ac.rs
DOI: 10.46793/ICCBI23.379G
САЖЕТАК / ABSTRACT:
Machine learning (ML) leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers clinician decision making. Starting from data (medical images, biomarkers, patients’ data) and using powerful tools such as convolutional neural networks, classification and regression models, etc., it aims at creating personalized models, adapted to each patient, which can be applied in real clinical practice as a decision support system to doctors.
КЉУЧНЕ РЕЧИ / KEYWORDS:
image processing, deep learning, data mining, medical expert systems
ЛИТЕРАТУРА / REFERENCES:
- TAXINOMISIS project: A multidisciplinary approach for the stratification of patients with carotid artery disease, https://taxinomisis-project.eu/
- SILICOFCM project: In Silico trials for drug tracing the effects of sarcomeric protein mutations leading to familial cardiomyopathy, https://silicofcm.eu/
- SGABU project: Increasing scientific, technological and innovation capacity of Serbia as a Widening country in the domain of multiscale modelling and medical informatics in biomedical engineering, http://sgabu.eu/
- PANBIORA project: Personalised and generalised integrated biomaterial risk assessment, https://www.panbiora.eu/
- DECODE project: Drug-coated balloon simulation and optimization system for the improved treatment of peripheral artery disease, https://www.decodeitn.eu/
- COVIDAI project: Use of Regressive Artificial Intelligence (AI) and Machine Learning (ML) Methods in Modelling of COVID-19 Spread, http://www.covidai.kg.ac.rs/