This course is part of the UBC Micro-certificate in Natural Language Processing to Improve Patient Care. The program consists of three courses that can be taken individually or combined into the Micro-certificate.
Patients share their experiences through reviews, forums and social media, creating rich data on needs, sentiments and behaviours. This course shows you how to analyse user-generated healthcare content to identify themes, detect mood changes and understand patient perspectives.
You develop practical skills in topic modelling, sentiment analysis, needs identification and summarization, enabling you to support patient experience initiatives and public health monitoring.
By the end of this course, you will be able to:
- Apply topic modelling techniques to patient-generated text
- Use sentiment analysis to monitor patient experiences
- Detect mood variations in unstructured content
- Identify patient needs through automated text analysis
- Evaluate the quality and coherence of NLP-generated insights in healthcare settings
For those who are new to NLP, we recommend starting with Overview of NLP and Large Language Models [link].
Course activities include videos, quizzes, hands-on lab assignments and discussion board participation. Optional weekly office hours allow you to explore examples, ask questions and build confidence working with patient-generated data.
Course outline
Module 1: Overview – Natural Language Processing and Categories of Data
Explore the unique characteristics of user-generated content in healthcare, compare it to clinical documents and examine real-world applications such as social media analysis and patient experience dashboards.
Module 2: Topic Modeling
This module introduces topic modeling techniques, including BERTopic, to uncover themes and patterns in patient-generated text data.
Module 3: Needs Identification
Build classifiers to detect and categorize patient needs from unstructured content, which supports more responsive and personalized healthcare services.
Module 4: Sentiment and Mood Analysis
This module focuses on analyzing emotional tone and mood variations in patient narratives using sentiment analysis and prompt-based techniques.
Module 5: Dialogue Acts Identification and Summarization
Examine conversational structures and apply abstractive summarization to extract meaningful insights from patient dialogues.
Module 6: Neuro-Degenerative Conditions & Coherence Analysis
This module introduces coherence analysis as a tool for detecting cognitive patterns in patient-generated content, with applications in monitoring neurodegenerative conditions.
Module 7: Future UGC Uses & Conclusions
Explore emerging developments such as AI scribes, advances in multilingual NLP and the potential for Canadian healthcare-specific LLMs, and reflect on key takeaways from the three courses.
How am I assessed?
You are assessed through quizzes, discussion posts and hands-on lab assignments. Multiple-choice quizzes confirm your understanding of lecture content. Discussion posts evaluate your critical thinking and engagement with weekly topics, with feedback from the instructor or TAs. Lab assignments assess your ability to apply techniques and explain your results using scoring rubrics.
A minimum grade of 70% is required to pass.
Expected effort
Expect to spend five to seven hours per week to complete readings, videos, quizzes, lab assignments and optional office hours.
Technology requirements
- an email account
- a computer, laptop or tablet, using Windows, macOS or Linux
- the latest version of a web browser (or previous major version release)
- a Google account to access Google Drive
- a reliable internet connection
- a video camera and microphone
For virtual office hours, you’ll also need:
- a video camera and microphone
One day before the start of your course, we’ll email you step-by-step instructions for accessing your course.
Course format
This course is 100% online and instructor supported with weekly instructor office hours. Course work is done independently and at your own pace within deadlines set by your instructor. Log in anytime to your course to access the lessons as they become available.