Reza Ziaeian

• Developed AI-driven analytics solutions to optimize clinical trial data and real-world evidence analysis.
• Applied Bayesian optimization techniques to improve machine learning models for patient outcome prediction.
• Managed and structured large-scale clinical datasets, ensuring high-quality data processing pipelines.

Key Achievements:

- MRI-Based Brain Tumor Detection Model: Achieved 99% accuracy in classifying pituitary, glioma, and meningioma tumors, significantly improving diagnostic reliability.

- MRI-Based Brain Tumor Segmentation Model: Achieved 99% accuracy in detection and 90% Dice Score, proving the effectiveness of deep learning in real-world medical applications.
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• Provided statistical consultation and clinical data analysis for international clients.
• Assisted in developing real-world data analytics frameworks for pharmaceutical and healthcare organizations.

• Developed a highly accurate (95% accuracy) predictive model using decision tree algorithms to assess the likelihood of hospitalization for COVID-19 patients based on various risk factors, including human leukocyte antigen polymorphisms.
• Conducted a comprehensive inferential analysis of data from 1,725 COVID-19 patients.
• The model provides critical insights to healthcare providers, enabling early intervention and resource allocation for high-risk patients
• Demonstrated expertise in statistical analysis, machine learning algorithms, and healthcare data utilization, contributing to the management of the COVID-19 pandemic.

• Spearheaded the design and development of a comprehensive questionnaire for a large-scale psychology project involving 4,000 participants, focusing on mental health in the workplace within Saudi Arabia.
• Led the preprocessing and analysis of complex datasets, employing advanced inferential statistical methods to identify key risk factors and trends.
• Authored a detailed project report, synthesizing findings and insights with clarity and precision.
• Developed and executed innovative data visualization strategies to effectively communicate complex results to a diverse range of stakeholders, enhancing comprehension and impact.

• Played a pivotal role in a Phase II clinical trial for Artemic™, a nutraceutical product, focusing on its safety and efficacy assessment.
• Responsible for the preprocessing and statistical analysis of trial data, ensuring accuracy and reliability of results.
• Designed and created engaging infographics, effectively visualizing complex trial data for better understanding and impact.
• Authored a concise and informative summary of the trial results and findings, tailored for presentation and marketing purposes, enhancing the product's visibility and understanding among stakeholders.

• Managed a comprehensive analysis of unstructured healthcare data from50,000 patients who underwentorthopedicsurgery at Al-Razi Hospital, Kuwait, between 2012 and 2018.
• Implemented advancednatural language processing andrecurrent neural network techniquesfor preprocessing and categorizing diagnosticreports and surgical data.
• Developed a comprehensiveorthopedic abbreviation and definition database, enhancing the accuracy and efficiency of data categorization.
• Utilized this database tofine-tune ChatGPT, enabling it to classify orthopedic diseases and operations based on anatomical regions(spine, hand, hip, foot, etc.).
• The project significantly improved data utility and analysis in orthopedic healthcare, offering insightsfor better patientcare and surgical outcomes.