V3_L1: Data Collection

A group of professionals, including data scientists and analysts, responsible for performing data analysis and modeling.

The process of gathering data from various sources, including demographic, clinical, coverage, and pharmaceutical information.

The merging of data from multiple sources to remove redundancy and prepare it for further analysis.

The process of organizing and formatting data to meet the requirements of the modeling technique.

The identification and definition of the necessary data elements, formats, and sources required for analysis.

A stage where data scientists discuss various ways to manage data effectively, including automating certain processes in the database.

The professionals who are responsible for managing and extracting data from databases.

A modeling technique that uses a tree-like structure to classify data based on specific conditions and variables.

Information about patient characteristics, such as age, gender, and location.

Techniques used to analyze and summarize data, providing initial insights and identifying gaps in data.

Partial results obtained from predictive modeling can influence decisions on acquiring additional data.

A group of patients with specific criteria selected for analysis in a study or model.

The building of models to predict future outcomes based on historical data.

A subset of data used to train or fit a machine learning model; consists of input data and corresponding known or labeled output values.

Data elements are not currently accessible or integrated into the data sources.

Modeling analysis focused on a single variable or feature at a time, considering its characteristics and relationship to other variables independently.

Data that does not have a predefined structure or format, typically text images, audio, or video, requires special techniques to extract meaning or insights.

The process of representing data visually to gain insights into its content and quality.