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Many cancer interventional clinical trials are not completed because the required number of eligible patients are not enrolled.

Generalizable, high-throughput phenotyping methods based on supervised machine learning (ML) algorithms could significantly accelerate the use of electronic health records data for clinical and translational research. However, they often require large numbers of annotated samples, which are costly and time-consuming to review. We investigated the use of active learning (AL) in ML-based phenotyping algorithms.

To describe a collaborative approach for developing an electronic health record (EHR) phenotyping algorithm for drug-induced liver injury (DILI).

To determine factors that physicians find encouraging and discouraging about e-prescribing and to compare these factors based on physicians' adoption status, a cross-sectional study was conducted using an internet-based survey administered to a national convenience sample of primary care physicians. A scale was developed to measure factors related to the adoption of e-prescribing. Analysis procedures included exploratory factor analysis, multivariate analysis of variance, and Tukey's post-hoc tests. 443 surveys were [...]

In 2008 we developed a shared health research information network (SHRINE), which for the first time enabled research queries across the full patient populations of four Boston hospitals. It uses a federated architecture, where each hospital returns only the aggregate count of the number of patients who match a query. This allows hospitals to retain control over their local databases and comply with federal and state privacy laws. However, because [...]

To develop an electronic registry of patients with chronic kidney disease (CKD) treated in a nephrology practice in order to provide clinically meaningful measurement and population management to improve rates of blood pressure (BP) control.

To evaluate the complex dynamics involved in implementing electronic health information exchange (HIE) for public health reporting at a state health department, and to identify policy implications to inform similar implementations.

This paper presents a method to improve the navigation and manipulation of radiological images through a sterile hand gesture recognition interface based on attentional contextual cues. Computer vision algorithms were developed to extract intention and attention cues from the surgeon's behavior and combine them with sensory data from a commodity depth camera. The developed interface was tested in a usability experiment to assess the effectiveness of the new interface. An [...]