Pediatric Rapid WGS Clinical Decision Pipeline with Google ADK
Rapid Whole-Genome Sequencing in neonatal intensive care requires immediate, reliable phenotype-to-genotype matching. Construct a multi-agent diagnostic workflow using Google ADK and Amazon Bedrock to extract HPO terms from clinical notes and rank causal variants. Achieve top-3 variant identification accuracy of over 90%.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct a pediatric rapid WGS decision support system using Google ADK and Amazon Bedrock with FHIR data integration.
How work is evaluated
Evaluates accuracy of phenopacket extraction and causal genomic variant ranking.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
hpo_code_format
Verifies extracted HPO codes follow HP:XXXXXXX format.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
top_3_accuracy
Percentage of benchmark cases where the true causal variant is in top 3 ranked candidates • target: 0.9 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Develop multimodal clinical parsing workflows using Google ADK
Integrate Amazon Bedrock models for scalable medical concept entity extraction
Convert unstructured EHR text into standardized Human Phenotype Ontology (HPO) codes
Rank genomic variant pathogenicity using phenotype-driven prioritizing algorithms
Reference links and supporting material
Annotated pediatric NICU case records paired with VCF files and verified clinical diagnoses.
Challenge setup
Evaluates accuracy of phenopacket extraction and causal genomic variant ranking.
Challenge input
JSON containing patient clinical narrative and list of candidate VCF variants.
Google ADK
Framework specified for constructing multi-modal clinical agents.
Amazon Bedrock
Cross-cloud foundation model inference engine for entity extraction.
Evaluated output
JSON object with extracted_hpo_terms and top_ranked_variants array.
- Verifies extracted HPO codes follow HP:XXXXXXX format.
- Percentage of benchmark cases where the true causal variant is in top 3 ranked candidates • target: 0.9 • r...
- Benchmark: Phenopacket Precision Diagnostic Benchmark
- Top 3 Accuracy target: 0.9
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Google · Required
- Google ADK · Optional
- Amazon Bedrock · Optional
Evaluation contract
- hpo_code_format · Weight 1
- top_3_accuracy · Weight 1
Metrics
- top_3_accuracy · Target 0.9
Benchmarks
- Phenopacket Precision Diagnostic Benchmark
- biotech
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing patient clinical narrative and list of candidate VCF variants.
Output: JSON object with extracted_hpo_terms and top_ranked_variants array.
Execution harness: Python
Evaluation tools
- Google ADK · Agent Architecture Framework
- Amazon Bedrock · Model Runtime Engine
Recipe freeze marker
No recipe freeze time is recorded.