| { |
| "field_id": "F026", |
| "field": "biomedical natural language processing", |
| "directions": [ |
| { |
| "direction_id": "G0", |
| "name": "Reusable biomedical language representations and models", |
| "scope": "Learn language models or semantic representations usable across multiple medical or biomedical tasks, including domain pretraining, subsequent medical specialization, and semantic representations of terms, sentences, or medical records." |
| }, |
| { |
| "direction_id": "G1", |
| "name": "Biomedical language-model capability assessment", |
| "scope": "Assess the capabilities and limitations of language models in biomedical and clinical NLP through cross-task benchmarks, systematic comparisons, or comprehensive analyses." |
| }, |
| { |
| "direction_id": "G2", |
| "name": "Clinical NLP adaptation under limited or shifting evidence", |
| "scope": "Study how clinical text models maintain or acquire usable capabilities when institutions, languages, data sources, or task-label conditions change, rather than addressing only one fixed downstream task." |
| }, |
| { |
| "direction_id": "G3", |
| "name": "Acquiring labels for medical text extraction", |
| "scope": "When training annotations for extraction are scarce, create or obtain usable labeled material; acquiring annotations is itself the research result." |
| }, |
| { |
| "direction_id": "G4", |
| "name": "Patient condition, diagnosis and outcome inference", |
| "scope": "Use patient language or clinical text evidence to make patient-level judgments, including identifying documented states, inferring diagnoses, and predicting subsequent risks or events; individual studies need not address all three." |
| }, |
| { |
| "direction_id": "G5", |
| "name": "Structured facts and relations from biomedical text", |
| "scope": "Turn local information in clinical or biomedical text into usable structures, covering entity mentions, attributes, and events, as well as relationships between entities." |
| }, |
| { |
| "direction_id": "G6", |
| "name": "Mapping medical text to standardized concepts and codes", |
| "scope": "Map medical expressions in text or document content to standardized terminology systems, and assess the accuracy and textual basis of those mappings." |
| }, |
| { |
| "direction_id": "G7", |
| "name": "Interpretation of biomedical research findings", |
| "scope": "Determine what interventions, outcomes, or findings research reports present, and whether research evidence supports or refutes a claim." |
| }, |
| { |
| "direction_id": "G8", |
| "name": "Evidence-grounded medical text communication", |
| "scope": "Transform existing clinical or biomedical material into coherent, trustworthy text suited to its intended readers, including care documentation, overviews of research evidence, and patient-facing explanations." |
| }, |
| { |
| "direction_id": "G9", |
| "name": "Biomedical information seeking and answering", |
| "scope": "Locate relevant material or formulate answers in response to medical or biomedical information needs; retrieval may be a standalone result or support answering." |
| }, |
| { |
| "direction_id": "G10", |
| "name": "Privacy-preserving clinical text transformation", |
| "scope": "Identify and remove or replace identifying information in clinical text while considering privacy protection and preservation of the text’s utility." |
| } |
| ] |
| } |
|
|
