Natural Language Processing in Healthcare: Unlocking Insights from Clinical Text
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Course Modules:
Module 1: Introduction to NLP in Healthcare
Role of unstructured text in clinical data
Common applications: entity extraction, text classification, summarization
Data privacy and regulatory context (HIPAA, GDPR)
Module 2: Clinical Text Types and Preprocessing
Discharge summaries, progress notes, pathology reports
Tokenization, normalization, stopwords, and lemmatization
De-identification and anonymization techniques
Module 3: NLP Models and Pipelines
Named Entity Recognition (NER) for diseases, drugs, and procedures
Text classification models (SVMs, Naive Bayes, transformers)
Topic modeling and summarization (TF-IDF, LDA, BART)
Module 4: Deep Learning and Language Models in Healthcare
BERT and BioBERT for medical language understanding
Clinical language models (ClinicalBERT, Med7)
Transfer learning and fine-tuning on healthcare corpora
Module 5: Real-World Use Cases and Deployment
Automated coding (ICD, CPT), clinical decision support
Sentiment analysis for patient feedback
Conversational AI for virtual health assistants
Module 6: Capstone Project – NLP for Clinical Insight
Choose a task: condition detection, NER, summarization, or classification
Use a dataset like MIMIC-III (de-identified) or synthetic clinical notes
Submit a cleaned dataset, model notebook, and analysis report
Tools & Technologies Used:
Python
spaCy, NLTK, Scikit-learn
Transformers (Hugging Face), BioBERT, ClinicalBERT
Pandas, Seaborn, Matplotlib (for results visualization)
Target Audience:
AI and healthcare data science students
Clinical informatics professionals
Engineers building AI tools for hospitals and EHR systems
NLP researchers in biomedical or public health fields
Global Learning Benefits:
Transform raw medical notes into structured insights
Apply cutting-edge NLP models to improve healthcare delivery
Understand the challenges of medical language and terminology
Build AI solutions that support clinicians and improve patient care
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