MaskMap: Decoding the Hidden Spectrum

Solution Details: Our solution, MaskMap, leverages machine learning and data from over 14,000 tokens of user experiences on Reddit. It uses LinearSVC and XGBClassifier models to decode the hidden spectrum of autism.
Stakeholders/Engagement: This solution benefits autistic women, their families, and medical specialists. It also opens opportunities for collaboration with data scientists, AI experts, and mental health advocates.
Benchmark & Alternatives: Current industry benchmarks focus on traditional diagnostic methods. Our solution is innovative as it uses AI and data analysis, a largely unexplored area in autism diagnosis.
Relevance & Alignment: Our approach directly addresses the issue of undiagnosed autism in women, contributing to broader efforts to improve women’s mental health.
Value & Success Criteria: MaskMap offers a new avenue for autism diagnosis, potentially reducing misdiagnoses and improving mental health outcomes. Its success can be measured by its accuracy and its adoption by medical professionals.

Ethical, Inclusion, Diversity & Equality (IDE): MaskMap aligns with IDE principles by addressing a gender disparity in autism diagnosis. We’ll measure this through user feedback and diagnostic outcomes.
Feasibility and Scalability: The solution is feasible, given the availability of data and AI technology. It’s scalable, with potential for multilingual support and application to other mental health conditions.
Assumptions and Dependencies: The solution assumes access to clean, anonymized data. It depends on technological infrastructure and acceptance by the medical community.
Potential Future Development and Direction: We envision MaskMap evolving to include more diverse data sources and improved AI models. We aim for broader adoption within the medical community and expansion to other underdiagnosed conditions.