Data Requirements

 Data Requirements within the Data Science Methodology, the analogy of cooking with data is used to emphasize the importance of identifying necessary ingredients (data) and understanding how to source, work with, and prepare them for analysis. Just as a chef needs specific ingredients to make a dish successfully, a data scientist must identify the required data elements, formats, and sources to address the problem at hand effectively.

The case study presented focuses on applying data requirements to a decision tree classification approach in healthcare, specifically regarding congestive heart failure patients. The process involves:

Defining the Patient Cohort: Identifying criteria for selecting patients for analysis, including admission within the provider's service area, primary diagnosis of congestive heart failure, and continuous enrollment for at least six months prior to the primary admission.

Exclusion Criteria: Patients with additional significant medical conditions that could skew results are excluded from the cohort.

Data Content and Format: Determining the necessary data content for decision tree classification, which includes comprehensive clinical histories covering admissions, diagnoses, procedures, prescriptions, and other relevant services. Data must be represented in a format suitable for decision tree modeling, with one record per patient and columns representing model variables.

Data Preparation: Aggregating transactional records to the patient level to create one record per patient, incorporating relevant attributes and variables for analysis.

The case study underscores the importance of meticulous planning and anticipation of subsequent stages, such as data preparation, to ensure the success of the analysis. By defining clear data requirements upfront, data scientists can effectively gather, prepare, and analyze data to derive insights and make informed decisions.