Prediction of preterm birth in twin pregnancies using machine learning

Preterm birth (PTB) is the leading cause of death in twin pregnancies. A variety of parameters, such as cervical length, maternal medical history, demographics, and obstetric characteristics all have been shown to affect the risk of PTB. However, the relationship is not obvious. Early prediction of PTB in these pregnancies can assist physicians in identifying those patients who may benefit from preventive interventions and closer monitoring. This project aims to use machine learning to create an algorithm that predicts which twin pregnancy is at a risk of PTB. This algorithm is expected to benefit clinicians in managing twin pregnancies by identifying patients who may benefit from preventive interventions. In addition, accurate detection of preterm birth is a prerequisite for any future attempts to develop such new interventions.

Faculty Supervisor:

Dafna Sussman

Student:

Partner:

Sunnybrook Health Sciences Centre

Discipline:

Engineering

Sector:

Health and Related Sciences & Technology

University:

Toronto Metropolitan University

Program: