Pediatric Rapid WGS Clinical Decision Multi-Agent Network via AutoGen
Pediatric whole-genome sequencing requires rapid variant interpretation under stringent operational latencies. Deploy an AutoGen multi-agent clinical consensus workflow on decentralized Akash Network infrastructure to evaluate pediatric genomic variants and generate standardized diagnostic FHIR reports within targeted turnaround limits.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a multi-agent system using AutoGen hosted on Akash Network compute to process pediatric rapid whole-genome sequencing datasets.
How work is evaluated
Evaluates multi-agent AutoGen consensus speed, HPO phenotype matching, and FHIR report output validity.
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.
FHIR Schema Compliance
Validates that DiagnosticReport meets FHIR R4 schema requirements.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Phenotype Match Rate
Accuracy of correlating phenotypic HPO terms with causative genes. • target: 0.95 • 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
Design multi-agent role delegation using Microsoft AutoGen for clinical genetics.
Deploy scalable containerized compute workloads onto Akash Network decentralized cloud.
Automate phenotype (HPO terms) to genotype cross-referencing.
Export structured diagnostic recommendations into HL7 FHIR DiagnosticReport resources.
Reference links and supporting material
Pediatric clinical cases dataset linking HPO terms, candidate genetic variants, and expert-validated primary diagnoses.
Challenge setup
Evaluates multi-agent AutoGen consensus speed, HPO phenotype matching, and FHIR report output validity.
Challenge input
JSON containing patient HPO terms and candidate genomic variants
AutoGen
Enables multi-agent role playing for consensus diagnostics.
Akash Network
Provides decentralized cloud infrastructure for fast execution.
Evaluated output
JSON FHIR DiagnosticReport containing primary diagnosis and candidate variant
- Validates that DiagnosticReport meets FHIR R4 schema requirements.
- Accuracy of correlating phenotypic HPO terms with causative genes. • target: 0.95 • range: 0-1
- Benchmark: PediatricGenomics-Bench
- Phenotype Match Rate target: 95%
- 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
- AutoGen · Required
- Akash Network · Optional
- Zed · Optional
Evaluation contract
- FHIR Schema Compliance · Weight 1
- Phenotype Match Rate · Weight 1
Metrics
- Phenotype Match Rate · Target 0.95
Benchmarks
- PediatricGenomics-Bench
- biotech
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing patient HPO terms and candidate genomic variants
Output: JSON FHIR DiagnosticReport containing primary diagnosis and candidate variant
Execution harness: Python
Evaluation tools
- AutoGen · Multi-Agent Framework
- Akash Network · Inference Runtime Host
Recipe freeze marker
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