Challenges / moph
Patient No-Show Prediction 2026
Open for submissionsHealthcareMetric: AUROCmoMinistry of Public Health
QAR 200,000 + fast-track to marketplace
Deadline 2026-10-01 38 participants
Predict outpatient appointment no-shows across PHCC health centres using de-identified booking, weather, and historical attendance features. Winning models will feed an SMS nudge and overbooking optimiser expected to recover thousands of lost clinic slots per month.
Leaderboard
Scored on AUROC| Rank | Team | Organisation | AUROC | Submissions |
|---|---|---|---|---|
| 1 | HBKU HealthML | HBKU | 0.861 | 27 |
| 2 | Wahaj Labs | Wahaj Labs | 0.854 | 14 |
| 3 | Falak Analytics | Falak Analytics | 0.849 | 21 |
| 4 | Sadeem AI | Sadeem AI | 0.843 | 12 |
| 5 | @noora_datasci | independent | 0.836 | 17 |
| 6 | QCRI TabularML | HBKU | 0.829 | 10 |
| 7 | @james_okafor | independent | 0.817 | 13 |
| 8 | @hamad_ml | independent | 0.802 | 6 |
Submit an entry
Drop model predictions (.csv / .jsonl)
Scored automatically inside the sovereign evaluation harness
Submissions open until 2026-10-01
Teams must be registered on the platform. Submissions are evaluated on a held-out national test set and logged to the audit trail.