Training activity information
Details
Pre-process and analyse unstructured clinical data using NLP techniques
Type
Developmental training activity (DTA)
Evidence requirements
Evidence the activity has been undertaken by the trainee.
Reflection on the activity at one or more time points after the event including learning from the activity and/or areas of the trainees practice for development.
An action plan to implement learning and/or to address skills or knowledge gaps identified.
Considerations
- NLP terminology: tokens, documents and corpus
- Tokenisation, stop words and stemming
- Term Frequency – Inverse document frequency
- Sentiment analysis
Reflective practice guidance
The guidance below is provided to support reflection at different time points, providing you with questions to aid you to reflect for this training activity. They are provided for guidance and should not be considered as a mandatory checklist. Trainees should not be expected to provide answers to each of the guidance questions listed.
Before action
- Consider the nature of unstructured clinical data (e.g., text from clinical notes, reports).
- Why might it be useful to pre-process and analyse this data? What are the specific applications in healthcare and in your own organisation?
- What specific NLP techniques are relevant for pre-processing and analysing this type of data? What tools or libraries might be useful?
- What specific insights do you hope to gain about the challenges and opportunities of working with unstructured clinical data?
- How will this activity develop your skills in applying NLP techniques for information extraction and analysis in healthcare?
- What is your current understanding of NLP concepts and techniques?
- Discuss with your training officer the type of unstructured clinical data you will be working with and the specific analysis goals.
- Research common NLP pre-processing techniques (e.g., tokenisation, stemming, lemmatisation) and analysis methods (e.g., sentiment analysis, topic modelling).
- Explore relevant NLP libraries in programming languages like Python (e.g., NLTK, SpaCy, Transformers).
- How do you feel about working with unstructured textual data and applying computational linguistic methods?
In action
- How are you approaching the task of pre-processing and analysing the unstructured clinical data? What NLP techniques are you considering applying initially and why?
- What decisions are you making about the specific steps in the pre-processing pipeline and the parameters for the NLP algorithms?
- Which aspects of the task (e.g., data cleaning, tokenisation, sentiment analysis, entity recognition) feel more familiar, and which are more challenging?
- How effectively are you preparing the unstructured data for analysis using NLP?
- What challenges are you encountering in dealing with the inherent complexities of clinical text data?
- What insights are you gaining about the information that can be extracted from unstructured clinical data using NLP techniques as you proceed?
- How does this activity connect with your existing knowledge of natural language processing and data analysis?
- If your initial choice of NLP techniques is not providing the desired information, what alternative approaches could you explore?
- Do you need to consult documentation or seek advice on how to best handle specific data challenges or implement particular NLP methods?
- Are you ensuring that your data handling and analysis are appropriate for the sensitive nature of clinical information and within your current technical skills? What are the risks of conducting this sort of pre-processing and analysis, related to specific applications of this work?
On action
- Describe the unstructured clinical data you worked with and the NLP techniques you applied. What were the key pre-processing steps you undertook? What insights or findings did you obtain from your analysis?
- What new knowledge or skills did you acquire regarding NLP techniques and their application to clinical data? Were there any unexpected challenges or interesting patterns you discovered in the data? What did you learn from these? How did this activity enhance your understanding of working with unstructured healthcare data? How might the ability to pre-process and analyse unstructured data using NLP be relevant in your future practice?
- What specific NLP techniques or data pre-processing methods do you want to explore further? How will you apply your understanding of NLP to future analyses of unstructured clinical data? What actions will you take to improve your skills in NLP for healthcare applications? What support or resources might you need to further develop your NLP skills for clinical data analysis?
Beyond action
- Have you reviewed your approach to pre-processing and analysing the unstructured data? Have you encountered other examples of unstructured clinical data and NLP techniques since this training activity? How does your experience here relate?
- Has your understanding of NLP techniques informed your interpretation of research or discussions involving the analysis of text-based clinical data? Has this training activity enhanced your appreciation of the challenges and opportunities associated with using unstructured data in healthcare AI?
- What transferable skills (e.g., data pre-processing, applying specific analytical techniques, interpreting results from unstructured data) did you develop that will be valuable in future work with clinical data? What specific NLP techniques or applications in healthcare are you now more interested in exploring?
Relevant learning outcomes
| # | Outcome |
|---|---|
| # 4 |
Outcome
Apply AI and machine learning techniques to address healthcare provision and clinical questions. |