Login / Signup

Development and validation of thromboembolism diagnostic algorithms in children with cancer from real-world data.

Uma H AthaleJacqueline HaltonAnastasia GayowskyAnthony K C ChanJason D Pole
Published in: Pediatric research (2024)
Research in pediatric thrombosis, especially cancer-related thrombosis, is limited mainly due to small-sized studies. Real-world data provide ready access to large and diverse populations. However, there are no validated algorithms for identifying thrombosis in real-world data for children. An algorithm based on combination of thrombosis and anticoagulation utilization codes had 76% sensitivity and 85% specificity to identify diagnosis of thrombosis in children in administrative data. This study provides a valid approach for ascertaining pediatric thrombosis using real-world data and offers a good avenue to advance pediatric thrombosis research.
Keyphrases
  • pulmonary embolism
  • electronic health record
  • machine learning
  • big data
  • young adults
  • deep learning
  • squamous cell carcinoma
  • artificial intelligence
  • papillary thyroid
  • childhood cancer
  • lymph node metastasis