ANN validation system for ICU neonatal data

The amount of data generated in the intensive care environment nowadays prohibits the storage of all the information available. The validation process is time consuming, since nurses have to check every certain periods the data acquired from bedside monitors in order to assess their validity and integrity. This work presents an automatic method for data validation in the intensive care environment, based on an artificial intelligence approach, namely artificial neural networks (ANNs). A real world dataset acquired at Beth Israel Deaconess Medical Center (BIDMC) neonatal intensive care unit (NICU) is used to obtain the validation model and assess its performance. The dataset consists of high frequency sampled data of the level of oxygen saturation (SpO2) of neonates. A subset of 100 neonates was considered for modeling purposes. A total of 7,018,662 samples were available, containing 129,075 validated ones. The performance of the validation model, assessed in terms of its AUC, was of up to 0.75. Both the sensitivity and specificity reached acceptable values according to medical review. Future work would involve a prospective study and validation of the methods proposed in this work.