AI for Clinical Decision Support Full Syllabus

Module 1: Introduction to Clinical Decision Support

• What is Clinical Decision Support (CDS)?
• Types of CDS systems: Alerts, Guidelines, Diagnostic Assistance
• Traditional vs AI-powered CDS
• Goals: Improve care, reduce errors, support clinicians

Module 2: Healthcare Data Fundamentals

• Electronic Health Records (EHRs)
• Structured vs Unstructured data in healthcare
• Clinical coding systems: ICD, CPT, SNOMED CT
• HL7 & FHIR standards overview

Module 3: Data Collection, Cleaning & Preprocessing

• De-identification & patient privacy
• Handling missing or noisy medical data
• NLP for clinical notes
• Data normalization techniques (lab units, medications)

Module 4: Machine Learning in CDS

• Supervised learning for diagnosis prediction
• Classification models for disease detection (e.g., diabetes, stroke)
• Risk stratification models (e.g., for ICU patients)
• Case study: Predicting hospital readmissions

Module 5: Deep Learning in CDS

• CNNs for medical imaging interpretation (e.g., X-rays, CT)
• RNNs/LSTMs for time-series patient data (e.g., vitals, EHR logs)
• Transformer models for medical text (BERT in clinical NLP)
• Case study: Detecting sepsis early using deep learning

Module 6: Clinical Use Cases & Applications

• AI for diagnostic support
• AI for medication recommendations
• Predicting treatment outcomes
• Personalized care planning

Module 7: Real-Time Decision Support Systems

• Architecture of real-time CDS systems
• Integration with hospital systems (EHRs, PACS)
• Edge vs cloud processing in hospital environments
• Latency & alert fatigue management

📱 Module 8: Natural Language Processing in CDS

• Clinical note summarization
• Named Entity Recognition (NER) for symptoms, diagnoses
• AI chatbots for patient interaction
• NLP tools: spaCy, scispaCy, MedSpaCy, BioBERT

Module 9: Evaluation Metrics & Clinical Validation

• Accuracy, Sensitivity, Specificity, ROC-AUC in healthcare
• Clinical validation: Randomized controlled trials (RCTs)
• Confounding variables and ethical fairness
• Interpretability using SHAP, LIME

Module 10: Privacy, Security & Regulatory Compliance

• HIPAA compliance and patient data protection
• GDPR & global healthcare data laws
• Federated learning in healthcare
• Explainable AI in clinical decision-making

Module 11: AI Tools & Platforms in CDS

• Libraries: Scikit-learn, TensorFlow, PyTorch, MedCAT
• Datasets: MIMIC-III, eICU, PhysioNet, OMOP
• Platforms: IBM Watson Health, Google Health AI, Amazon HealthLake

Module 12: Challenges & Future of AI in CDS

• Bias & equity in AI models
• Trust and usability for doctors & nurses
• The future: Multimodal AI, Federated CDS, GPT-health assistants
• Clinical AI governance frameworks

Capstone Project (Optional)

• Build a CDS system to predict risk of stroke in patients based on EHR + vitals
• Use real-world open dataset (e.g., MIMIC)
• Include alert system for high-risk patients

Languages & Tools

• Python (pandas, NumPy, scikit-learn, TensorFlow)
• Jupyter / Google Colab
• Streamlit for creating CDS dashboards
• SQL for querying clinical databases