ObjectiveTo construct a demand model for electronic medical record (EMR) data quality in regards to the lifecycle in machine learning (ML)-based disease risk prediction, to guide the implementation of EMR data quality assessment. MethodsReferring to the lifecycle in ML-based predictive model, we explored the demand for EMR data quality. First, we summarized the key data activities involved in each task on predicting disease risk with ML through a literature review. Second, we mapped the data activities in each task to the associated requirements. Finally, we clustered those requirements into four dimensions. ResultsWe constructed a three-layer structured ring to represent the demand model for EMR data quality in ML-based disease risk prediction research. The inner layer shows the seven main tasks in ML-based predictive models: data collection, data preprocessing, feature representation, feature selection and extraction, model training, model evaluation and optimization, and model deployment. The middle layer is the key data activities in each task; and the outer layer represents four dimensions of data quality requirements: operability, completeness, accuracy, and timeliness. ConclusionThe proposed model can guide real-world EMR data governance, improve its quality management, and promote the generation of real-world evidence.