Following the nuanced sentiment analysis, the Step 4 Agent applies multiple layers of content classification to further categorize and structure the information within the dataset. This goes significantly beyond the initial user intent labels from Step 1 or the emergent thematic clusters from Step 3, adding formal categorical dimensions crucial for targeted querying and analysis within the Matrix of Meaning.
Purpose: The goal is to assign predefined or learned categorical labels to text segments, enabling users to filter, group, and analyze the data based on specific facets relevant to CF research. This adds essential organizational structure, facilitating the identification of patterns within specific types of discourse or subject areas.
Methodology & Scope: The Agent applies various classification schemes to the relevant linguistic data points (sentences, paragraphs, FAQs, etc., originating from Step 1 and annotated through Steps 2 & 3). This process likely involves a combination of machine learning classifiers (potentially trained on CF-specific corpora or utilizing large pre-trained models fine-tuned for the domain) and potentially rule-based systems or ontology mapping. Key classification types include:
- Domain-Specific Taxonomies: Applying classifications based on established biomedical knowledge or custom taxonomies created for the LOCKSMITH project. This could involve tagging text segments related to:
- Specific CFTR mutations (beyond just entity recognition).
- Biological pathways (e.g., ‘Inflammatory Response Pathway’, ‘Ion Transport Regulation’).
- Drug classes or mechanisms of action (e.g., ‘CFTR Modulators’, ‘Potentiators’, ‘Correctors’, ‘Anti-inflammatories’).
- Symptom categories or physiological systems affected (e.g., ‘Pulmonary Symptoms’, ‘Gastrointestinal Manifestations’, ‘Endocrine Complications’).
- Source/Perspective Attribution: Classifying the origin or viewpoint expressed in the text, potentially with fine granularity (e.g., ‘Patient Reported Outcome’, ‘Clinician Assessment Note’, ‘Basic Research Finding Abstract’, ‘Clinical Trial Protocol Section’, ‘Review Article Summary’).
- Discourse & Intent Analysis (Advanced): Categorizing segments based on their communicative function or nuanced intent, refining the basic Step 1 categories (e.g., ‘Expressing Uncertainty’, ‘Reporting Clinical Evidence’, ‘Posing Research Question’, ‘Stating Hypothesis’, ‘Sharing Personal Experience Narrative’, ‘Contrasting Viewpoints’).
- Fine-Grained Topic Tagging: Applying highly specific topic labels that may be narrower or more technically precise than the broader Step 3 themes (e.g., tagging with specific concepts like ‘Nasal Potential Difference Measurement’, ‘Sweat Chloride Test Interpretation’, ‘Pseudomonas Aeruginosa Biofilm Formation’).
- Integration into the Matrix: The classification results (eg.: labels, tags, confidence scores) are integrated into the main JSON data structure as additional attributes or dimensions linked to the corresponding text segments. A single text segment might receive multiple classifications from different schemes (e.g., a sentence could be tagged as ‘Patient Reported Outcome’, related to ‘Pulmonary Symptoms’, expressing ‘Uncertainty’, and discussing ‘CFTR Modulators’). This multi-labeling capability significantly enhances the matrix’s analytical power, allowing complex filtering and aggregation (e.g., “Analyze sentiment patterns specifically within clinician notes classified under ‘Treatment Failure’ regarding ‘CFTR Correctors’”).
Step 6: Advanced Relationship Extraction & Typing: