(Almost) all of entity resolution.

Whether the goal is to estimate the number of people that live in a congressional district, to estimate the number of individuals that have died in an armed conflict, or to disambiguate individual authors using bibliographic data, all these applications have a common theme-integrating information from multiple sources. Before such questions can be answered, databases must be cleaned and integrated in a systematic and accurate way, commonly known as structured entity resolution (record linkage or deduplication). Here, we review motivational applications and seminal papers that have led to the growth of this area.

Development, Implementation, and Evaluation of an In-Hospital Optimized Early Warning Score for Patient Deterioration.

Identification of patients at risk of deteriorating during their hospitalization is an important concern. However, many off-shelf scores have poor in-center performance. In this article, we report our experience developing, implementing, and evaluating an in-hospital score for deterioration.

Prediction of appointment no-shows using electronic health records.

Appointment no-shows have a negative impact on patient health and have caused substantial loss in resources and revenue for health care systems. Intervention strategies to reduce no-show rates can be more effective if targeted to the subpopulations of patients with higher risk of not showing to their appointments. We use electronic health records (EHR) from a large medical center to predict no-show patients based on demographic and health care features.

Minimal Impact of Implemented Early Warning Score and Best Practice Alert for Patient Deterioration.

Objectives: Previous studies have looked at National Early Warning Score performance in predicting in-hospital deterioration and death, but data are lacking with respect to patient outcomes following implementation of National Early Warning Score. We sought to determine the effectiveness of National Early Warning Score implementation on predicting and preventing patient deterioration in a clinical setting.

Design: Retrospective cohort study.

REGULARIZED BRAIN READING WITH SHRINKAGE AND SMOOTHING.

Functional neuroimaging measures how the brain responds to complex stimuli. However, sample sizes are modest, noise is substantial, and stimuli are high dimensional. Hence, direct estimates are inherently imprecise and call for regularization.