85% of patients for Rare Diseases screening selected via Danny Analytics

Empowering physicians in real-time to identify eligible patients for Pompe, Gaucher, and Fabry screening via Danny Analytics AI algorithm

Sqilline and a leading pharmaceutical company have partnered to apply Big Data technologies for the screening of patients for rare diseases to expedite diagnosis by finding previously undiagnosed patients.


  • Limited awareness of doctors, patients, and their relatives
  • Symptoms – interpreted in the context of common diseases
  • Average 4.8 Yrs. for a patient with a rare disease to reach an accurate diagnosis


  • Enabling physicians quickly to identify patients for early diagnosis and timely treatment
  • Improves collaboration and awareness of the disease among the hospitals in the country
  • Better clinical and patient-oriented outcomes


  • Embedded ML algorithms allow the platform to extract and search by keywords from the enormous volume of unstructured/free text from the epicrisis
  • Using pre-defined inclusion and exclusion criteria, the eligible patients are selected in real-time
  • Automatically alert physicians when new, high-risk patients are added to an appropriate screening cohort

Machine-learning technologies can offer great opportunities in the process of screening for rare disease patients.

Analyzing through Big Data who may fit the testing criteria can expedite the diagnostic delay.

This can offer patients more effective therapy if started in the early stages of the disease.

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