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We executed the Social Media Mining for Health (SMM4H) 2017 shared tasks to enable the community-driven development and large-scale evaluation of automatic text processing methods for the classification and normalization of health-related text from social media. An additional objective was to publicly release manually annotated data.

To discuss and illustrate the utility of two open collaborative data science platforms, and how they would benefit data science and informatics education.

We describe a scalable platform for research-oriented analyses of routine data in hospitals, which evolved from a state-of-the-art business intelligence architecture for enterprise resource planning. This platform involves an in-memory database management system for data modeling and analytics and a high-performance cluster for more computing-intensive analytical tasks. Setting up platforms for research-oriented analyses is a highly dynamic, time-consuming, and costly process. In some health care institutions, effective research platforms may [...]

We describe the evaluation of a system to create hospital progress notes using voice and electronic health record integration to determine if note timeliness, quality, and physician satisfaction are improved.

It is unclear to what extent simulated versions of real data can be used to assess potential value of new biomarkers added to prognostic risk models. Using data on 4522 women and 3969 men who contributed information to the Framingham CVD risk prediction tool, we develop a simulation model that allows assessment of the added contribution of new biomarkers. The simulated model matches closely the one obtained using real data [...]

Limited data are available on the correlation of mHealth features and statistically significant outcomes. We sought to identify and analyze: types and categories of features; frequency and number of features; and relationship of statistically significant outcomes by type, frequency, and number of features.

Standards such as the Logical Observation Identifiers Names and Codes (LOINC®) are critical for interoperability and integrating data into common data models, but are inconsistently used. Without consistent mapping to standards, clinical data cannot be harmonized, shared, or interpreted in a meaningful context. We sought to develop an automated machine learning pipeline that leverages noisy labels to map laboratory data to LOINC codes.

To demonstrate and test the validity of a novel deep-learning-based system for the automated detection of pulmonary nodules.

Hospitals that routinely share patients are those that most critically need to engage in electronic health information exchange (HIE) with each other to ensure clinical information is available to inform treatment decisions. We surveyed pairs of hospitals in a nationwide sample to describe whether and how hospitals within each hospital referral region (HRR) that have the highest shared patient (HSP) volume engaged in HIE with each other.