Mortality Rate Pre-assessment Based on Trips Score
Cansu Dağsuyu *
Department of Industrial Engineering, Cukurova University, 01130, Adana, Turkey
Müfide Narlı
Department of Industrial Engineering, Cukurova University, 01130, Adana, Turkey
Ali Kokangül
Department of Industrial Engineering, Cukurova University, 01130, Adana, Turkey
Nejat Narlı
Department of Pediatrics, Division of Neonatology, Cukurova University, 01130, Adana, Turkey
*Author to whom correspondence should be addressed.
Abstract
Treatment and care for newborn babies are provided at medical centres by personnel with the required education and experience using special equipment. Similar to the situation in many other countries, centres with such concentrations are rare; such a situation has required transportation of newborn babies from the centres where they are located to specialised centres. The medical centre that will transport the newborn cannot determine which case is a greater emergency (prioritised) without visiting and inspecting each newborn. In this study, prioritisation of transportation to a Level III newborn medical centre that also fulfils its own transportation needs was studied. In this study, using basic information regarding the newborn (not requiring laboratory tests) without visiting the newborn, the transportation priority of the newborn and the mortality rates were predicted using feed-forward artificial neural networks with high reliability. Prior to transportation, the state of newborn babies is determined using TRIPS scoring. In this way, when more than one transport call is received at the same time, prioritisation will be performed considering mortality rates. Detecting newborn patients’ medical conditions will also help in planning what type of equipment and transportation vehicles are required.
Keywords: TRIPS, neonatal transport, prediction, artificial neural networks