Inspiration
Moved by the plight of families battling rare diseases in their children, we created dAIgonstic to simplify and speed up the diagnosis process with the power of AI.
What it does
dAIgonstic employs machine learning to help doctors quickly pinpoint rare diseases in children from clinical data, aiming to start treatment sooner.
How we built it
With a lot of care and magic. Using Python, Cypher to query the Neo4j DB and ML models to classify the disease probability and the disease type.
Challenges we ran into
We faced the complexity of the medical problem, had limited time to develop a solution, and dealt with the unexpected departure of a team member, which tested our adaptability and resourcefulness. Also, the difficulty of using the FeatureCloud platform was an unexpected hurdle.
Accomplishments that we're proud of
We're proud of developing a prototype that respects patient privacy and provides accurate predictions, all within the limited time of a hackathon.
What we learned
We learned a lot about machine learning in healthcare, the importance of data privacy, and the deep need for better diagnostic tools.
What's next for dAIgnositc
We're planning to pilot our tool in clinical settings, gather feedback, improve our algorithms, and seek partnerships for broader impact.
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