Electronic Health Records (EHR) & Data Handling in AI-Powered Healthcare
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Course Modules:
Module 1: Introduction to Electronic Health Records
What are EHRs and how are they used in healthcare?
Common systems: Epic, Cerner, Allscripts
Key EHR components: diagnoses, medications, labs, notes
Module 2: Data Formats & Standards
HL7 and FHIR data models
Structured vs. unstructured clinical data
Integrating EHRs with AI tools
Module 3: Data Privacy, Security & Compliance
HIPAA, GDPR, and patient data protection
Data anonymization, encryption, and role-based access
Ethical AI in healthcare data use
Module 4: Preprocessing and Data Cleaning
Handling missing data, duplicates, and outliers
Normalizing medical codes (ICD, LOINC, SNOMED)
Text cleaning in clinical notes (NLP techniques)
Module 5: AI Applications Using EHRs
Predictive modeling: readmission, mortality, disease onset
Patient risk stratification and personalized medicine
Time series and longitudinal patient data handling
Module 6: Capstone Project – Build an EHR-Ready Dataset
Choose a public dataset (e.g., MIMIC-III, eICU)
Clean, normalize, and format it for AI training
Submit a data dictionary, notebook, and use-case summary
Tools & Technologies Used:
Python (Pandas, NumPy)
Scikit-learn, TensorFlow (for model testing)
NLP libraries for notes (spaCy, NLTK)
FHIR APIs and HL7 tools (optional)
Target Audience:
AI and healthcare data science students
Medical researchers working with patient data
Engineers developing clinical AI applications
Compliance teams learning about secure health data handling
Global Learning Benefits:
Understand how to manage sensitive health records responsibly
Prepare medical data for AI and machine learning pipelines
Learn industry standards in healthcare data interoperability
Build compliant, scalable healthcare AI systems
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