Jeong E, Osmundson S, Gao C, Edwards DRV, Malin B, Chen Y. Learning the impact of acute and chronic diseases on forecasting neonatal encephalopathy. Computer methods and programs in biomedicine. 2021 Nov;211(211). 106397 p. PMID: 34530389 [PubMed] PMCID: PMC8551018 NIHMSID: NIHMS1741443.
There is a wide range of risk factors predisposing to the onset of neonatal encephalopathy (NE), including maternal antepartum/intrapartum comorbidities or events. However, few studies have investigated the difference in the impact of acute and chronic diseases on forecasting NE, which could assist clinicians in choosing the best course of action to prevent NE or reduce its severity and complications. In this study, we aimed to engineer features based on acute and chronic diseases and assess the differences of the impact of acute and chronic diseases on NE prediction using machine learning models.