Design, construction and evaluation of systems to predict risk in obstetrics.

We present a systematic, practical approach to developing risk prediction systems, suitable for use with large databases of medical information. An important part of this approach is a novel feature selection algorithm which uses the area under the receiver operating characteristic (ROC) curve to measure the expected discriminative power of different sets of predictor variables. We describe this algorithm and use it to select variables to predict risk of a specific adverse pregnancy outcome: failure to progress in labour. Neural network, logistic regression and hierarchical Bayesian risk prediction models are constructed, all of which achieve close to the limit of performance attainable on this prediction task. We show that better prediction performance requires more discriminative clinical information rather than improved modelling techniques. It is also shown that better diagnostic criteria in clinical records would greatly assist the development of systems to predict risk in pregnancy.

Measuring the compressive viscoelastic mechanical properties of human cervical tissue using indentation.

The human cervix is an important mechanical barrier in pregnancy which must withstand the compressive and tensile forces generated from the growing fetus. Premature cervical shortening resulting from premature cervical remodeling and alterations of cervical material properties are known to increase a woman׳s risk of preterm birth (PTB). To understand the mechanical role of the cervix during pregnancy and to potentially develop indentation techniques for in vivo diagnostics to identify women who are at risk for premature cervical remodeling and thus preterm birth, we developed a spherical indentation technique to measure the time-dependent material properties of human cervical tissue taken from patients undergoing hysterectomy. In this study we present an inverse finite element analysis (IFEA) that optimizes material parameters of a viscoelastic material model to fit the stress-relaxation response of excised tissue slices to spherical indentation. Here we detail our IFEA methodology, report compressive viscoelastic material parameters for cervical tissue slices from nonpregnant (NP) and pregnant (PG) hysterectomy patients, and report slice-by-slice data for whole cervical tissue specimens. The material parameters reported here for human cervical tissue can be used to model the compressive time-dependent behavior of the tissue within a small strain regime of 25%.