Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2: a machine learning approach- a retrospective cohort study

• Main Authors: Dylan Young (Author), Bita Houshmand (Author), Chunyi Christie Tan (Author), Abirami Kirubarajan (Author), Ashna Parbhakar (Author), Jazleen Dada (Author), Wendy Whittle (Author), Mara L. Sobel (Author), Luis M. Gomez (Author), Mario Rüdiger (Author), Ulrich Pecks (Author), Peter Oppelt (Author), Joel G. Ray (Author), Sebastian R. Hobson (Author), John W. Snelgrove (Author), Rohan D'Souza (Author), Rasha Kashef (Author), Dafna Sussman (Author)
• Format: Book
• Published: BMC, 2023-08-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Abstract Background Pregnant people are particularly vulnerable to SARS-CoV-2 infection and to ensuing severe illness. Predicting adverse maternal and perinatal outcomes could aid clinicians in deciding on hospital admission and early initiation of treatment in affected individuals, streamlining the...