> For the complete documentation index, see [llms.txt](https://guides.ohie.org/duc/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://guides.ohie.org/duc/hiv-treatment-continuity-technology-intervention-framework-tif/during-the-visit/proactive-adherence-counselling-interventions/machine-learning-to-predict-interruption-in-treatment-mozambique.md). # Machine Learning to Predict Interruption in Treatment (Mozambique) Data.FI, Mozambique (September 2021) ### 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:** | | | | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Data Elements |

Demographics

Medical history

Publicly available data source

| | 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.

| | Technology Requirements / Interoperability |

Machine learning

Predictive model connected to OpenMRS.

| | Calculations / Algorithms | Precision and recall is used to evaluate model performance. | | 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.

| | Implementation Considerations | Contextual factors help to boost predictive accuracy | | Governance Considerations | |