Agentic AI for Healthcare: Accelerating Analysis with SageMaker Data Agent

Clinical researchers often face a "Data Wall." They have the questions—"How does hypertension progression vary by age in our cohort?"—but answering them requires navigating complex SQL databases, writing PySpark connectors, and debugging data pipelines.

Amazon SageMaker Data Agent breaks down this wall. By integrating Agentic AI directly into SageMaker Unified Studio, it turns natural language clinical questions into complete analytical workflows. In this guide, we'll follow "CardioMetrics Institute" as they use SageMaker Data Agent to analyze synthetic heart health data without writing a single line of initial boilerplate code.

Step 1: Setup & Data Ingestion

Before the AI can help, it needs access to your data catalog. In SageMaker Unified Studio, we first connect to our AWSDataCatalog.

-- Quick check to ensure our synthetic data is loaded
SELECT * 
FROM "AwsDataCatalog"."cardio_db"."conditions" 
WHERE description LIKE '%Hypertension%'
LIMIT 10;

Step 2: Natural Language Analysis

Instead of writing the join logic manually, we open the Data Agent Panel inside the notebook and type our research intent effectively.

The Agent will:

• Plan: Identify keys (patient_id) to join patients and conditions tables.
• Code: Write the PySpark or Pandas transformation logic.
• Verify: Check column names against the catalog schema.
• Visualize: Generate the matplotlib or seaborn plotting code.

Step 3: Advanced Iteration (Survival Analysis)

The power of Agentic AI is in refinement. We can ask it to build upon the previous context.

The Data Agent recognizes it needs the lifelines library or similar survival analysis tools. It will generate a plan to: 1. Calculate "Time to Event" (Diagnosis or Death). 2. Handle censored data (patients still alive). 3. Plot the survival curves.

from lifelines import KaplanMeierFitter
import matplotlib.pyplot as plt

# Agent auto-generated data preparation
kmf = KaplanMeierFitter()

ax = plt.subplot(111)

# Cohort A: With Hypertension
ix = df['has_hypertension'] == True
kmf.fit(df.loc[ix]['T'], df.loc[ix]['E'], label='Hypertensive')
kmf.plot_survival_function(ax=ax)

# Cohort B: Control
kmf.fit(df.loc[~ix]['T'], df.loc[~ix]['E'], label='Control')
kmf.plot_survival_function(ax=ax)

plt.title('Survival Analysis: Hypertension Impact')
plt.show()

Conclusion

SageMaker Data Agent didn't just "auto-complete" code; it acted as a junior data scientist. It understood the clinical intent, handled the data engineering, and produced publication-ready survival curves. For CardioMetrics Institute, this meant moving from raw data to research insights in an afternoon rather than a week.