# Q7a · Healthcare and finance applications

Subject: Natural Language Processing

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/nlp/#tee/papers/legacy-r24/q7a

Canonical: https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r24--q7a/

Previous examination · complete worked answers

**Question. Explain NLP use cases in healthcare and finance.** [10 marks]

### Answer

| Domain / task | Input → method → useful output | Example and limit |
| --- | --- | --- |
| Healthcare: clinical extraction | Notes → entity/negation/relation extraction → diagnoses, medicines and doses | Distinguish no fever from fever; clinician review is needed |
| Healthcare: coding and search | Clinical text → classify/retrieve → candidate billing codes or evidence | Coding suggestions are not autonomous diagnoses |
| Healthcare: patient messaging | Patient question → intent + retrieval → approved explanation | Escalate uncertainty and emergencies; protect identifiers |
| Finance: sentiment/event analysis | News/filings → entities/events/sentiment → issuer-specific signals | Distinguish rumor from confirmed earnings; not guaranteed returns |
| Finance: document extraction | Reports/contracts → fields/relations → searchable structured records | Keep source spans and verify amounts/dates |
| Finance: customer support/compliance triage | Messages → intent/classification → routed cases or flagged text | Human investigation is required; a language flag is not proof of wrongdoing |

Acquire permitted text → Clean and preserve context → Extract / classify / retrieve → Evaluate on domain data → Human-reviewed use