Strategy, Education, Documentation & IT (SEDI)

Our Research Department specializes in leveraging advanced data analysis and computational intelligence techniques to drive innovations in healthcare. Combining expertise in machine learning, deep learning, oncology, and signal processing, our team excels in transforming raw data into actionable insights. We focus on medical image analysis, feature engineering, and pattern recognition to enhance diagnostic accuracy and treatment planning. By integrating these advanced methodologies, we aim to improve healthcare outcomes and develop robust decision support systems that empower medical professionals in making informed decisions.

Additionally, the members of the Department of Strategy, Education, Documentation, and IT (SEDI) collective carry out activities in the following areas:

  • Implementation of managerial control standards.
  • Maintenance and updating of the website
  • Proper functioning of the computing equipment in the institute.
  • Optimal management of the book and periodical fund.

Objectives of the SEDI collective:

  • Monitoring the achievement of scientific objectives for research projects.
  • Coordinating activities to promote personnel with higher education in scientific positions within the institute.
  • Compiling scientific and administrative synthesis works regarding the institute’s activities.
  • Coordinating activities for implementing managerial control standards within the institute.
  • Coordinating secretariat activities for the doctoral school in the field of Medicine within the institute.
  • Managing scientific research projects and preparing scientific and technical reports.
  • Overseeing IT activities within the institute (equipment maintenance and internet network administration, management of the website).
  • Coordinating activities at the institute’s scientific library.
Expertise area includes oncology, signal transduction, RNA interference, targeted anti-tumor therapy, and immunotherapy.
Expertise area includes: machine learning and deep learning approach for medical image analysis, feature engineering,
Preda M., Tanase B.C., Zob D.L., Gheorghe A.S., Lungulescu C.V., Dumitrescu E.A., Stanculeanu D.L., Manolescu L.S.C., Popescu O., Ibraim E., Mahler B. The Bidirectional Relationship between Pulmonary Tuberculosis and Lung Cancer. International Journal of Environmental Research and Public Health 2023, 20, doi:10.3390/ijerph20021282.