Outcome prediction of electroconvulsive therapy for depression.

Introduction: We developed and tested a Bayesian network(BN) model to predict ECT remission for depression, with non-response as a secondary outcome.

Methods: We performed a systematic literature search on clinically available predictors. We combined these predictors with variables from a dataset of clinical ECT trajectories (performed in the University Medical Center Utrecht) to create priors and train the BN.

Bayesian network analysis of antidepressant treatment trajectories.

It is currently difficult to successfully choose the correct type of antidepressant for individual patients. To discover patterns in patient characteristics, treatment choices and outcomes, we performed retrospective Bayesian network analysis combined with natural language processing (NLP). This study was conducted at two mental healthcare facilities in the Netherlands.

Information extraction from free text for aiding transdiagnostic psychiatry: constructing NLP pipelines tailored to clinicians' needs.

Background: Developing predictive models for precision psychiatry is challenging because of unavailability of the necessary data: extracting useful information from existing electronic health record (EHR) data is not straightforward, and available clinical trial datasets are often not representative for heterogeneous patient groups. The aim of this study was constructing a natural language processing (NLP) pipeline that extracts variables for building predictive models from EHRs. We specifically tailor the pipeline for extracting information on outcomes of psychiatry treatment trajectories, applicable throughout the entire spectrum of mental health disorders ("transdiagnostic").

Loss of placental thrombomodulin in oocyte donation pregnancies.

Objective: To investigate whether thrombomodulin dysregulation is involved in the development of preeclampsia after oocyte donation (OD). Women who become pregnant after OD are prone to develop preeclampsia, a syndrome characterized by an aberrant immunologic response, hypercoagulability, and endothelial dysfunction. A mediator of inflammation and coagulation is thrombomodulin, which has a possible role to play in this syndrome.

From Glomerular Endothelium to Podocyte Pathobiology in Preeclampsia: a Paradigm Shift.

Preeclampsia is a pregnancy-specific syndrome characterized by renal dysfunction and high blood pressure. When evaluated with light microscopy, the renal lesion of preeclampsia is marked by endothelial cell swelling and the appearance of bloodless glomeruli. However, regarding the pathobiology of renal damage in preeclampsia, attention recently has shifted from the glomerular endothelial cells to the podocytes.