# Machine Learning to Predict Interruption in Treatment (Mozambique)
### Countries: 
Mozambique
### Intervention Description
Data for Implementation team shared their work with predictive model as part of a software solution connected to OpenMRS, the EMR used at ECHO-supported facilities. They will be creating a software plugin to generate patient risk scores through the EMR.
Presenter: Yoni Friedman, Data.FI
[DUC Meeting Recording](https://archive.org/details/2021.09.14-duc-community-meeting-recording)
**Intervention Details:**
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Data Elements | Demographics
Medical history
Publicly available data source
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Evidence | Model showed strong predictive power achieving 0.65 AUC-PR compared to an underlying IIT rate of 23%.
Learning activities are planned to track several ways in which this project can impact outcomes such as increasing intensity of interventions at high risk patients.
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Technology Requirements / Interoperability | Machine learning
Predictive model connected to OpenMRS.
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Calculations / Algorithms | Precision and recall is used to evaluate model performance. |
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Factors to Scale | ECHO opted to generate risk prediction in raw form. A challenge will be to properly socialize the intrepretation of prediction.
Factors to reach scale – Scalling the intervention include installing the software at additional facilities and should not be technically difficult.
Preparing for updates to OpenMRS will be part of scaling plan.
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Implementation Considerations | Contextual factors help to boost predictive accuracy |
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Governance Considerations | |
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