Machine Learning Approach to Injury Monitoring in Children and Adolescents

Abstract Pediatric injuries are common and associated with individual suffering and costs for the healthcare systems and society. The incidence of injuries among children in Switzerland has so far been insufficiently monitored. Emergency department patient records can be a valuable database to assess medically relevant injuries and gain information for monitoring and evidence-based prevention. The talk will outline our stepwise development of a machine-learning program for a pediatric injury monitoring system and lessons learnt. In this feasibility study we draw on electronic case histories of children treated for injuries between 2018 and 2022 at the University Children’s Hospital Zürich emergency department (N= to 30’884). The ML- approach follows different steps: step 1. utility evaluation of electronic pediatric patient records regarding ML and injury monitoring and prevention requirements in a test-sample of 100 electronic pediatric case histories; step 2. choice/adaptation of coding tree; step 3. coder training and inter-coder reliability testing; step 4. manual annotation of a sub-sample (n 1000) of the electronic pediatric patient records, step 5. design and testing of the ML program in an iterative process, and step 6. performance testing in a random sample of pediatric patient records. First lessons learnt indicate that electronic case history data contain relevant data and fulfill requirements for monitoring and prevention purposes. The IDB coding system proved too complex and detailed for the pediatric data base, providing too few examples for the ML-learning process, and needed to be adapted. Initial data analyses indicate the majority of cases are classified as less severe (73%) and only few (<1%) as very severe but the later provide the most detailed data. ML is a promising approach and makes use of hospital data for a national monitoring of pediatric injuries in Switzerland.