Overview
What this challenge is about.
Parse and Structure Clinical Discharge Summaries. Advanced challenge in code. Writing production code that solves real engineering problems, earn a blockchai...
The Brief
What you'll do, and what you'll demonstrate.
Build a clinically reliable structured-extraction pipeline for discharge summaries with auditable span pointers and proven miss-rate reduction.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Combine traditional NLP and LLM-based extraction for reliability
- Build auditable spans linking structured fields to source text
- Evaluate clinical NLP with field-level precision/recall
- Communicate failure modes to a clinical audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Structured Extraction
Apply structured extraction to solve real industry problems and demonstrate production-level capability.
- Clinical Nlp
Apply clinical nlp to solve real industry problems and demonstrate production-level capability.
- Parsing
Apply parsing to solve real industry problems and demonstrate production-level capability.
- Llm Tool Use
Apply llm tool use to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply evaluation to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
NLP Engineer
Building clinically-reliable structured extraction with auditable spans is the NLP-engineer work that healthtech AI companies need urgently.
This challenge sharpens
- structured-extraction
- clinical-nlp
- parsing
AI Engineer
Combining traditional NLP and LLMs with schema-enforced decoding is the AI-engineer pattern every regulated AI team adopts after the first hallucination incident.
This challenge sharpens
- llm-tool-use
- structured-extraction
- parsing
Applied AI Scientist
Designing audits and miss-rate analyses for clinical extraction is the applied-AI work that determines whether a healthtech AI product can ship.
This challenge sharpens
- clinical-nlp
- evaluation
- structured-extraction