Farid Kacimi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Farid Kacimi has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Larbi Boubchir
LIASD research Lab, University of Paris 8, France
2 shared publications - Selestine Melchane
Laboratoire LITAN, École supérieure en Sciences et Technologies de l’Informatique et du Numerique, Amizour, Algérie
2 shared publications - Youssef Elmir
Laboratoire LITAN, École supérieure en Sciences et Technologies de l’Informatique et du Numerique, Amizour, Algérie
2 shared publications
Latest Publications
- 8 September 2025
- Published by Springer Nature in Network Modeling Analysis in Health Informatics and Bioinformatics
- Vol. 14 (1) , 1-12
A decision support approach to infectious disease classification using tweets and routine blood tests
- 8 September 2025
- Published by Springer Nature in Network Modeling Analysis in Health Informatics and Bioinformatics
- Vol. 14 (1) , 1-12
A decision support approach to infectious disease classification using tweets and routine blood tests
- Open Access
Artificial Intelligence for Infectious Disease Prediction and Prevention: A Comprehensive Review
Artificial Intelligence (AI) and infectious diseases prediction have recently experienced a common development and advancement. Machine learning (ML) apparition, along with deep learning (DL) emergence, extended many approaches against diseases apparition and their spread. And despite their outstanding results in predicting infectious diseases, conflicts appeared regarding the types of data used and how they can be studied, analyzed, and exploited using various emerging methods. This has led to some ongoing discussions in the field. This research aims not only to provide an overview of what has been accomplished, but also to highlight the difficulties related to the types of data used, and the learning methods applied for each research objective. It categorizes these contributions into three areas: predictions using Public Health Data to prevent the spread of a transmissible disease within a region; predictions using Patients' Medical Data to detect whether a person is infected by a transmissible disease; and predictions using both Public and patient medical data to estimate the extent of disease spread in a population. The paper also critically assesses the potential of AI and outlines its limitations in infectious disease management.
Open AccessArtificial Intelligence for Infectious Disease Prediction and Prevention: A Comprehensive Review
Artificial Intelligence (AI) and infectious diseases prediction have recently experienced a common development and advancement. Machine learning (ML) apparition, along with deep learning (DL) emergence, extended many approaches against diseases apparition and their spread. And despite their outstanding results in predicting infectious diseases, conflicts appeared regarding the types of data used and how they can be studied, analyzed, and exploited using various emerging methods. This has led to some ongoing discussions in the field. This research aims not only to provide an overview of what has been accomplished, but also to highlight the difficulties related to the types of data used, and the learning methods applied for each research objective. It categorizes these contributions into three areas: predictions using Public Health Data to prevent the spread of a transmissible disease within a region; predictions using Patients' Medical Data to detect whether a person is infected by a transmissible disease; and predictions using both Public and patient medical data to estimate the extent of disease spread in a population. The paper also critically assesses the potential of AI and outlines its limitations in infectious disease management.