Impact on place of death in cancer patients: a causal exploration in southern Switzerland.

Background: Most terminally ill cancer patients prefer to die at home, but a majority die in institutional settings. Research questions about this discrepancy have not been fully answered. This study applies artificial intelligence and machine learning techniques to explore the complex network of factors and the cause-effect relationships affecting the place of death, with the ultimate aim of developing policies favouring home-based end-of-life care.

A Bayesian network model for predicting pregnancy after in vitro fertilization.

We present a Bayesian network model for predicting the outcome of in vitro fertilization (IVF). The problem is characterized by a particular missingness process; we propose a simple but effective averaging approach which improves parameter estimates compared to the traditional MAP estimation. We present results with generated data and the analysis of a real data set.

Objective way to support embryo transfer: a probabilistic decision.

Study Question: Is it feasible to identify factors that significantly affect the clinical outcome of IVF-ICSI cycles and use them to reliably design a predictor of implantation?

Summary Answer: The Bayesian network (BN) identified top-history embryos, female age and the insemination technique as the most relevant factors for predicting the occurrence of pregnancy (AUC, area under curve, of 0.72). In addition, it could discriminate between no implantation and single or twin implantations in a prognostic model that can be used prospectively.

Artificial defocus for displaying markers in microscopy Z-stacks.

As microscopes have a very shallow depth of field, Z-stacks (i.e. sets of images shot at different focal planes) are often acquired to fully capture a thick sample.