Machine Learning Models to Predict Risk of Maternal Morbidity and Mortality From Electronic Medical Record Data: Scoping Review

Lavanya Vasudevan   ^{1, 2} , PhD, MPH ;   Mohammad Golam Kibria   ³ , MPH, MBA ;   Lauren M Kucirka   ^{4, 5} , MD ;   Karl Shieh   ⁴ , MHS ;   Mian Wei   ⁶ , MS ;   Safoora Masoumi   ³ , MD ;   Subha Balasubramanian   ⁷ , MPH ;   Ashley Victor   ³ , MPS ;   Jamie L Conklin   ⁸ , MLiS ;   Metin Nafi Gurcan   ⁹ , PhD ;   Alison M Stuebe   ^{4, 10} , MD, MSc ;   David Page   ⁶ , PhD

¹ Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States

² Duke Global Health Institute, Duke University, Durham, NC, United States

³ Carolina Health Informatics Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

⁴ Department of Obstetrics and Gynecology, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

⁵ School of Data Science and Society, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

⁶ Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, United States

⁷ Department of Epidemiology, Gillings School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

⁸ Health Sciences Library, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

⁹ Center for Artificial Intelligence Research, School of Medicine, Wake Forest University, Winston-Salem, NC, United States

¹⁰ Department of Maternal and Child Health, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States