رسالة جامعية
PREDICTING NECROTIZING ENTEROCOLITIS IN HOSPITALIZED NEONATES
| العنوان: | PREDICTING NECROTIZING ENTEROCOLITIS IN HOSPITALIZED NEONATES |
|---|---|
| المؤلفون: | Weller, Jennine Hess Dearolf |
| المساهمون: | Caffo, Brian, Haut, Elliott R, Hackam, David J, Bembea, Melania M, King, Betsy A |
| بيانات النشر: | Johns Hopkins University USA |
| سنة النشر: | 2022 |
| المجموعة: | Johns Hopkins University, Baltimore: JScholarship |
| مصطلحات موضوعية: | necrotizing enterocolitis, prediction, machine learning, surgery |
| الوصف: | Necrotizing enterocolitis (NEC), a devastating disease of premature bowel, is challenging to predict. The disease is rare, with incompletely understood pathogenesis, rapid onset and progression, and insufficient diagnostic criteria. Using a systematic review of the literature, a cultivated dataset of published neonatal radiographs, and a publicly available neonatal critical care database, this dissertation examines novel approaches to improve predictions of NEC. First, in a review piece, we summarize surgical care for patients with NEC (Chapter 2). We provide a foundational framework to understanding NEC by describing the diverse presentations of the disease and discussing current best practices to reduce NEC-associated morbidity and mortality. Second, we conduct a systematic review of published prognostic models for predicting NEC onset and progression in hospitalized infants (Chapter 3). We find that published models have fair to poor discrimination of NEC outcomes and high risk of bias, limiting model clinical utility. Third, we develop an image classifier to support surgical resident recognition of pneumatosis intestinalis, a radiographic sign of NEC (Chapter 4). We find that a deep convolutional neural network trained on neonatal abdominal radiographs can successfully detect pneumatosis and performs comparably well to senior surgical residents. Fourth, we use the MIMIC III Clinical Database to develop an early warning score for NEC based on routinely available clinical data during an infant's stay in a neonatal intensive care unit (NICU) (Chapter 5). We find that models accurately predict NEC before disease onset, with first NEC risk detection occurring days previously. Fifth, in a perspective piece, we reflect on the promises and challenges of utilizing machine learning methods in NEC prediction and research (Chapter 6). We advocate for policy and practice changes to improve NEC prediction efforts. Overall, this dissertation highlights strengths and limitations of existing NEC prediction models and offers ... |
| نوع الوثيقة: | thesis |
| وصف الملف: | application/pdf |
| اللغة: | English |
| العلاقة: | http://jhir.library.jhu.edu/handle/1774.2/67490Test |
| الإتاحة: | http://jhir.library.jhu.edu/handle/1774.2/67490Test |
| رقم الانضمام: | edsbas.15E1B73F |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |