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SmartDaas
Open-source ML pf predicting HIV treatment interruption risk
SmartDaaS predicts HIV treatment interruption risk from routine DHIS2 and EMR exports — no new data collection, no workflow changes. Built for PEPFAR implementing partners across sub-Saharan Africa. - Trained on 27,288 real-world HIV patient records - Temporal AUC 0.772 on held-out future patients - SHAP explainability for every risk score - No data stored — session-only processing - Validated across 192,732 records in 6 countries - MIT licensed
Hi everyone 👋 I'm Kalyani, an independent researcher and creator of SmartDaaS.
I built this after seeing a recurring problem in HIV programmes: treatment interruption is often detected only after patients disengage from care.
SmartDaaS explores whether routine DHIS2 and EMR exports can be turned into practical decision support without requiring new data collection or workflow changes.
Built with privacy-first processing, SHAP explainability, and validated across 192,732 clinical records in 6 countries.
I'd genuinely love feedback and questions.
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About SmartDaas on Product Hunt
“Open-source ML pf predicting HIV treatment interruption risk”
SmartDaas was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #147 on the daily leaderboard. SmartDaaS predicts HIV treatment interruption risk from routine DHIS2 and EMR exports — no new data collection, no workflow changes. Built for PEPFAR implementing partners across sub-Saharan Africa. - Trained on 27,288 real-world HIV patient records - Temporal AUC 0.772 on held-out future patients - SHAP explainability for every risk score - No data stored — session-only processing - Validated across 192,732 records in 6 countries - MIT licensed
SmartDaas was featured in Health & Fitness (82.8k followers), Open Source (68.5k followers), Artificial Intelligence (471.1k followers) and GitHub (41.3k followers) on Product Hunt. Together, these topics include over 162.7k products, making this a competitive space to launch in.
Who hunted SmartDaas?
SmartDaas was hunted by Lakshmi Kalyani Chinthala. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Lakshmi Kalyani Chinthala