PDA Technical Glossary

Digitized patient records that include medical history, diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, and laboratory test results. This information supports mor effective treatment when patients switch healthcare providers due to new therapies, re-location, or for epidemiological research and predictive health analytics.

Instances where AI systems, particularly those based on advanced models like GPT-4, generate outputs that are incorrect, misleading, or nonsensical, despite displaying high confidence. Hallucinations can result from biases in the training data, model limitations, or inherent uncertainties in the problem space.

The intersection of information science, computer science, and healthcare that involves the use of technology to store, manage, and analyze health-related information.

Human-in-the-Loop (HITL) is a capability and role whereby qualified personnel can meaningfully intervene within the system’s decision cycle during operation or oversight activities or to enhance trust and continuous improvement. These actions are in place to address uncertainty and limitations, override or adjust outputs, and provide feedback that supports continuous performance assurance. Professionals can actively guide, review, and verify the AI output. HITL is applied in a risk-based manner, with the level and timing of oversight, controls, and documentation proportionate to the system’s intended use and risk and evaluated on the performance of the human–AI team, not the model alone.

A computational model that combines elements from multiple modeling approaches, such as empirical, mechanistic, and machine learning techniques, to solve complex problems. By leveraging the strengths of each approach, hybrid models enhance predictive accuracy, robustness, and interpretability. Applications include integrating mechanistic drug metabolism models with machine learning algorithms trained on experimental data to predict the pharmacokinetic behavior of new drug candidates. Similarly, hybrid models can merge empirical data with physiological knowledge to simulate drug-disease interactions or optimize formulation designs.

The process of optimizing the configuration settings (hyperparameters) of a machine learning model to maximize performance. Proper tuning prevents underfitting and overfitting, improving overall accuracy.