Question4

Challenges in Collecting and Preparing Data for Predicting Patient Readmission Rates

Introduction

A healthcare provider aims to predict patient readmission within 30 days of discharge using patient records, doctor notes, and lab test results. These datasets come in different formats, creating data collection and preparation challenges.

Challenges in Data Collection & Preparation

(Referring to Phase 2: Data Preparation & Phase 3: Model Planning in the PDF)

1. Data Integration Issues

• Structured Data (Electronic Health Records, Lab Results) vs. Unstructured Data (Doctor's Notes, Imaging Reports)
• Different Formats:
  • Lab results in numerical form
  • Doctor's notes in free text (requiring NLP processing)
• Solution: Use ETL (Extract, Transform, Load) pipelines to standardize data

2. Missing or Incomplete Data

• Missing Lab Test Results: Not all patients undergo the same tests
• Incomplete Doctor's Notes: Subjective observations may lack structure
• Solution:
  • Imputation Techniques (Mean, Median for numerical data)
  • Text Completion Models (LLMs for incomplete notes)

3. Data Inconsistency & Standardization

• Varying Units (e.g., Blood pressure in mmHg vs. kPa)
• Different Terminologies (e.g., "Hypertension" vs. "HTN")
• Solution: Use medical ontologies (SNOMED, ICD-10) for standardization

4. Privacy & Compliance Issues