Cardiac EDA Processing

Cardiac ETL workflow for Oracle exports, labs, and curated patient-level datasets.

Track

Enact Business Solutions

Problem, execution, and outcomes

Problem

Cardiac data needed consistent identifiers, timelines, and lab structures before it could be trusted for analysis.

Execution

Processed Oracle exports and multi-sheet clinical workbooks with Python, Pandas, and OpenPyXL validation routines.

Outcomes

Standardized cardiac patient records.

Prepared repeatable data refresh outputs.

Added QC checks for missingness, duplicates, and date consistency.

Proof and Metrics

Oracle exportsMulti-sheet lab parsingRepeatable QC

Capability Proven: Private clinical data integration with repeatable quality enforcement and structured output delivery.

Confidentiality: Private healthcare workflow. Sensitive records are excluded.

Healthcare Data Flow

Stack

PythonPandasOpenPyXLOracle exports

Healthcare data pipeline for patient records, lab entries, and multi-source validation.

Proof

70k+ patient records

Stack

Python / Pandas / OpenPyXL / +2

Rule-driven cleaning for coronary angiogram records and derived clinical features.

Proof

Clinical rules

Stack

Python / Pandas / Clinical Rules / +1