About Meta Health Data

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• Predictive Modeling: Leveraging machine learning techniques like XGBoost, Random Forest, Cox-Lasso, and deep learning to predict disease progression, patient outcomes, and treatment responses.
• Causal Inference & Propensity Score Matching: Helping pharmaceutical companies assess the real-world effectiveness of drugs through comparative effectiveness research (CER).
• Survival & Risk Modeling: Developing Cox proportional hazards models for oncology, respiratory diseases (COPD, asthma), and chronic conditions.
• Cost-Effectiveness & Burden of Disease Analysis: Estimating direct and indirect healthcare costs using real-world claims data, electronic health records (EHRs), and registry data.

• CDISC & ADaM Compliance: Ensuring that clinical trial datasets follow regulatory standards for submission to FDA, EMA, and Health Canada.
• SAS Programming & Automation: Automating tables, listings, and figures (TLFs) generation for regulatory reports.

• Customized AI Solutions: Providing predictive analytics and AI-driven insights for healthcare organizations and insurers.
• Natural Language Processing (NLP) in Healthcare: Using Open source Large Language Models for text mining in medical records, literature reviews, and automated data extraction.
• Big Data Pipelines & Cloud Integration: Implementing Apache Spark, SQL, and Azure solutions for high-volume health data processing.

At Meta Health Data, we are dedicated to providing our customers with high-quality, reliable information services that meet their unique needs. We strive to be a trusted partner for businesses and individuals alike, offering innovative solutions and exceptional customer service at every turn.