PDA Technical Glossary

AI driven identification of potential drug side effects and adverse events, supporting risk mitigation.

The protection of patient and healthcare data from unauthorized access, use, disclosure, disruption, modification, or destruction.

The process of analyzing and structuring raw data into a more organized format that can be easily manipulated and understood by computer systems. In the pharmaceutical industry, this includes extracting relevant information from diverse sources such as scientific literature, clinical trial reports, and laboratory records. For example, AI can extract key insights from unstructured documents, such as drug patents or research articles, helping identify drug candidates, therapeutic targets, and adverse effects to accelerate data analysis, improve data quality, and support decision-making in drug discovery and development.

The process of identifying and interpreting patterns, trends, and relationships within large and complex datasets, leveraging statistical and computational techniques such as machine learning and data mining to extract meaningful insights from diverse types of data (e.g., chemical structures, biological assays, and clinical outcomes). Pharmaceutical applications include analysis of high-throughput screening data to identify compounds with desired pharmacological properties, such as potency, selectivity, and safety profiles, and in patient data to discover biomarkers, subpopulations, or disease signatures that aid in personalized treatment approaches.

The science and activities related to the detection, assessment, understanding, and prevention of adverse effects of drugs.

An approach to medical treatment and healthcare that customizes interventions based on the individual patient’s characteristics, often leveraging AI for personalized diagnostics and tailored treatment plans.

The application of statistical and AI techniques to analyze historical data and identify patterns, trends, and relationships that can be used to forecast trends, identify risks, and make and make data driven predictions in drug discovery, development, and manufacturing.

The use of statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data, and recommend optimal actions based on these predictions, improving pharmaceutical processes such as manufacturing efficiency and regulatory compliance.

The systematic design and refinement of instructions provided to AI models, particularly in the context of language models such as GPT (Generative Pre-trained Transformer), to optimize user interactions and generated outputs. Effective prompt engineering enhances model accuracy and relevance, improving applications such as automated text generation, conversational AI, and decision support systems.

The use of software robots or "bots" to automate routine and repetitive tasks, often used in administrative processes (e.g., small chat bubbles that appear when you enter a website and offer support or guidance). In pharmaceutical applications, RPA may be used to automate data entry and documentation processes.