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Children are a vulnerable population in the operating room, and are particularly at risk of complications from unanticipated hemorrhage. The decision to prepare blood products prior to surgery varies depending on the personal experience of the clinician caring for the patient. We present the first application of a data visualization technique to study large datasets in the context of blood product transfusions at a tertiary pediatric hospital. The visual analytical [...]

Malfunctions or poor usability of devices measuring glucose or delivering insulin are reportable to the FDA. Manufacturers submit 99.9% of these reports. We test online social networks as a complementary source to traditional FDA reporting of device-related adverse events.

Named entity recognition (NER) is one of the fundamental tasks in natural language processing. In the medical domain, there have been a number of studies on NER in English clinical notes; however, very limited NER research has been carried out on clinical notes written in Chinese. The goal of this study was to systematically investigate features and machine learning algorithms for NER in Chinese clinical text.

Adverse drug reaction (ADR) can have dire consequences. However, our current understanding of the causes of drug-induced toxicity is still limited. Hence it is of paramount importance to determine molecular factors of adverse drug responses so that safer therapies can be designed.

The rapidly growing volume of multimodal electrophysiological signal data is playing a critical role in patient care and clinical research across multiple disease domains, such as epilepsy and sleep medicine. To facilitate secondary use of these data, there is an urgent need to develop novel algorithms and informatics approaches using new cloud computing technologies as well as ontologies for collaborative multicenter studies.

The infrastructure for data collection implemented by the National Consortium on Alcohol and NeuroDevelopment in Adolescence (N-CANDA) for data collection comprises several innovative features: (a) secure, asynchronous transfer and persistent storage of collected data via a revision control system; (b) two-stage import into a longitudinal database; and (c) use of a script-controlled web browser for data retrieval from a third-party, web-based neuropsychological test battery. The asynchronous operation of data transmission [...]

Real-time alerting systems typically warn providers about abnormal laboratory results or medication interactions. For more complex tasks, institutions create site-wide 'data warehouses' to support quality audits and longitudinal research. Sophisticated systems like i2b2 or Stanford's STRIDE utilize data warehouses to identify cohorts for research and quality monitoring. However, substantial resources are required to install and maintain such systems. For more modest goals, an organization desiring merely to identify patients with [...]

To systematically review existing literature regarding patient engagement technologies used in the inpatient setting.

Learning of classification models in medicine often relies on data labeled by a human expert. Since labeling of clinical data may be time-consuming, finding ways of alleviating the labeling costs is critical for our ability to automatically learn such models. In this paper we propose a new machine learning approach that is able to learn improved binary classification models more efficiently by refining the binary class information in the training [...]

Usability testing is increasingly being recognized as a way to increase the usability and safety of health information technology (HIT). Medical simulation centers can serve as testing environments for HIT usability studies. We integrated the quality assurance version of our emergency department (ED) electronic health record (EHR) into our medical simulation center and piloted a clinical care scenario in which emergency medicine resident physicians evaluated a simulated ED patient and [...]