Sarah Ben Othman | Decision Sciences | Best Researcher Award

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Mrs. Sarah Ben Othman | Decision Sciences | Best Researcher Award

Sarah Ben Othman at CRIStAL France

Summary:

Dr. Sarah Ben Othman is a computer scientist specializing in healthcare logistics, decision support systems, and optimization techniques. With a doctorate from Blaise Pascal University and a Master’s degree in Health Logistics from École Centrale de Lille, she combines interdisciplinary expertise to enhance therapeutic care and hospital systems. Her research has contributed to advanced solutions for multi-skill task scheduling, pharmaceutical logistics, and augmented reality applications in healthcare. Dr. Ben Othman has extensive teaching and research experience across renowned institutions in France and Tunisia.

Professional Profile:

👩‍🎓Education:

Doctoral Degree in Computer Science (2013–2015):

  • Institution: Blaise Pascal University, Laboratory of Computer Science, Modeling, and Optimization of Systems CNRS UMR 6158.
  • Thesis Title: Collaborative System to Support the Scheduling and Orchestration of Multi-Skill Care Tasks (ANR TECSAN HOST Program).
  • Thesis Defense Date: December 14, 2015.
  • Thesis Director: Prof. Alain Quilliot.
  • Co-supervisor: Prof. Slim Hammadi.
  • Jury Members: Prof. Sophie Dupuy-Chessa, Prof. Christian Tahon, Prof. Emmanuel Duflos, and Dr. Jean-Marie Renard.

Master’s Degree in Health Logistics (2012–2013):

  • Institution: École Centrale de Lille.
  • Graduated with honors.

National Engineering Diploma (2012):

  • Institution: National Engineering School of Gabès, Tunisia (ENIG).
  • Specialty: Electric-Automatic.

Preparatory Cycle for Engineering Studies (2007–2009):

  • Institution: Preparatory Institute for Engineering Studies in Monastir, Tunisia.
  • Specialization: Mathematics/Physics.

Professional Experience:

Postdoctoral Researcher (2021–2023): Faculty of Sciences and Technologies (FST), as part of the E-LoDi industrial chair focused on optimizing therapeutic care for patients using interdisciplinary methods.

Teaching and Research Roles (2015–2023):

  • Contractor and part-time roles at ILIS (Faculty of Engineering and Health Management), École Centrale de Lille, Polytech Lille, and ENSAIT Roubaix.
  • Focus: Scheduling optimization, hospital logistics, pharmaceutical logistics, and accessibility systems for people with disabilities.

Research Projects:

  • Optimization of routes and travel systems for individuals with motor disabilities.
  • Augmented reality applications in pharmaceutical logistics.
  • Multi-agent decision support systems for pediatric emergency departments.

Previous Industry Experience:

  • Research Intern at Airbus Defense and Space (2013).
  • Various engineering internships in Brazil and Tunisia (2010–2011).

Research Interests:

Sarah Ben Othman’s research focuses on the integration of modeling, optimization, and simulation in healthcare systems. Her expertise spans the following areas:

  • Hospital logistics and care task orchestration.
  • Multi-agent systems for decision support in healthcare.
  • Accessibility optimization for individuals with disabilities.
  • Pharmaceutical logistics and augmented reality applications.
  • Digital ecosystems for therapeutic care optimization.

Author Metrics and Editorial Appointments:

  • Publications: Several peer-reviewed articles and conference papers in healthcare optimization, multi-agent systems, and hospital logistics.
  • Research Projects: Collaborated on prominent projects like ANR TECSAN HOST and ANR OILH.
  • Teaching Impact: Over 1,000 hours of teaching in advanced computational techniques and logistics management.

Top Noted Publication:

1. Explainable Approach for Air Quality Classification Based on Granular Computing Rule Extraction

  • Authors: Jairi, I., Ben-Othman, S., Canivet, L., Zgaya-Biau, H.
  • Journal: Engineering Applications of Artificial Intelligence, 2024, Volume 133, Article 108096.
  • Abstract Summary: This study presents a novel explainable approach for air quality classification utilizing granular computing-based rule extraction techniques. The methodology aims to enhance interpretability while ensuring robust classification accuracy.

2. Symbolic Artificial Intelligence to Diagnose Tuberculosis Using Ontology

  • Authors: Gerard, N., Ben Othman, S., Rangandin, P., Broucqsault, M., Hammadi, S.
  • Conference: Studies in Health Technology and Informatics, 2024, Volume 310, Pages 1574–1578.
  • Abstract Summary: This paper discusses an ontology-driven symbolic AI model designed to improve tuberculosis diagnosis. The framework leverages AI for enhanced decision support in clinical environments.

3. Pharmaceutical Decision Support System Using Machine Learning to Analyze and Limit Drug-Related Problems in Hospitals

  • Authors: Ben Othman, S., Decaudin, B., Odou, P., Cousein, E., Hammadi, S.
  • Conference: Studies in Health Technology and Informatics, 2024, Volume 310, Pages 1593–1597.
  • Abstract Summary: The research introduces a machine learning-based decision support system tailored to address and minimize drug-related problems in hospitals, aiming to improve patient safety and therapeutic outcomes.

4. AI-Driven Strategies for Precision and Efficiency in Optimizing Medical Iatrogeny Detection

  • Authors: Ben Othman, S., Ajmi, F., Decaudin, B., Cousein, E., Hammadi, S.
  • Conference: 10th 2024 International Conference on Control, Decision and Information Technologies (CoDIT 2024), Pages 2019–2024.
  • Abstract Summary: This work explores AI-driven techniques for enhancing the precision and efficiency of medical iatrogeny (harm caused by medical interventions) detection, offering a framework for improved healthcare quality.

5. Using Machine Learning to Support Prostate Cancer Detection Without a Dedicated Biomarker

  • Authors: Salem, H., Othman, S.B., Broucqsault, M., Hammadi, S.
  • Conference: IEEE International Conference on Automation Science and Engineering, 2024, Pages 1061–1066.
  • Abstract Summary: This research proposes a machine learning-based model for detecting prostate cancer without relying on specific biomarkers, emphasizing non-invasive diagnostic techniques.

Conclusion:

Dr. Sarah Ben Othman is a strong contender for the Best Researcher Award due to her innovative, interdisciplinary contributions to healthcare logistics and decision sciences. Her work addresses critical global challenges in hospital systems, therapeutic care, and accessibility. While her academic achievements and societal impact are commendable, further emphasis on leading high-impact, funded projects and fostering global collaborations could bolster her profile.

In conclusion, Dr. Ben Othman exemplifies the qualities of a distinguished researcher and is well-suited for the award, given her substantial contributions to science, education, and societal well-being.