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Using Predictive Analytics to Identify Children at High Risk of Defaulting From a Routine Immunization Program: Feasibility Study.

Subhash ChandirDanya Arif SiddiqiOwais Ahmed HussainTahira NiaziMubarak Taighoon ShahVijay Kumar DharmaAli HabibAamir Javed Khan
Published in: JMIR public health and surveillance (2018)
This feasibility study demonstrates that predictive analytics can accurately identify children who are at a higher risk for defaulting on follow-up immunization visits. Correct identification of potential defaulters opens a window for evidence-based targeted interventions in resource limited settings to achieve optimal immunization coverage and timeliness.
Keyphrases
  • young adults
  • big data
  • physical activity
  • quality improvement
  • healthcare
  • cancer therapy
  • risk assessment
  • deep learning