AMIGO
Challenge
We invite you to develop machine learning (ML) algorithms that can use knowledge graph data and accomplish these points:
A. Accurately predict if a child is healthy or is diagnosed with an illness.
B. Accurately predict patients’ disease category based on the first letter of ICD-10 code system.
C. Correctly Cluster patients based on the features from the omics and clinical data.
D. Offer the capability to be executed federatively using the feature cloud functionality.
Business Outcome
- Outline how the approach can be implemented and scaled to improve patients’ lives.
Technical Architecture
- You are free to use the synthetic data made available for you on Neo4j data base
- You will be able to connect to the Feature Cloud platform and upload your models
- You are free to use both supervised and unsupervised methods.
Technical Architecture
Background
The disease experts at the Dr. von Hauner Children’s Hospital in Munich are working toward establishing an AI platform to improve diagnosis of rare diseases in children.
Their goal is to implement federated learning and create a library of ML models to assist clinicians with patients’ diagnosis.
To protect patients’ privacy, we have already preprocessed and analyzed the data and created knowledge graphs which you can use to build your algorithm.
The knowledge graph data comprise thousands of human phenotype ontology (HPO) features, covering genomic, proteomic, blood values, electronic health records and patient questionnaires.
Additionally, we provide you with a well-crafted unlabeled synthetic data set that closely resembles the structure of the real data.
Joint initiative of
