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A supervised machine learning approach to discover relations between medical problems, treatments, and tests mentioned in electronic medical records.

Many Dutch hospitals have established internal systems for reporting incidents. However, such internal systems do not allow learning from incidents that occur in other hospitals. Therefore a multicenter, information technology (IT) supported reporting system named central medication incidents registration (CMR) was developed. This article describes the architecture, implementation and current status of the CMR in The Netherlands and compare it with similar systems in other countries.

To compare the use of structured reporting software and the standard electronic medical records (EMR) in the management of patients with bladder cancer. The use of a human factors laboratory to study management of disease using simulated clinical scenarios was also assessed.

This paper describes natural-language-processing techniques for two tasks: identification of medical concepts in clinical text, and classification of assertions, which indicate the existence, absence, or uncertainty of a medical problem. Because so many resources are available for processing clinical texts, there is interest in developing a framework in which features derived from these resources can be optimally selected for the two tasks of interest.

The BT-Nurse system uses data-to-text technology to automatically generate a natural language nursing shift summary in a neonatal intensive care unit (NICU). The summary is solely based on data held in an electronic patient record system, no additional data-entry is required. BT-Nurse was tested for two months in the Royal Infirmary of Edinburgh NICU. Nurses were asked to rate the understandability, accuracy, and helpfulness of the computer-generated summaries; they were [...]

To report the frequency, types, and causes of errors associated with outpatient computer-generated prescriptions, and to develop a framework to classify these errors to determine which strategies have greatest potential for preventing them.

The US Vaccine Adverse Event Reporting System (VAERS) collects spontaneous reports of adverse events following vaccination. Medical officers review the reports and often apply standardized case definitions, such as those developed by the Brighton Collaboration. Our objective was to demonstrate a multi-level text mining approach for automated text classification of VAERS reports that could potentially reduce human workload.

Concept extraction is a process to identify phrases referring to concepts of interests in unstructured text. It is a critical component in automated text processing. We investigate the performance of machine learning taggers for clinical concept extraction, particularly the portability of taggers across documents from multiple data sources.

To evaluate the benefit of a health information exchange (HIE) between hospitals, we examine the rate of crossover among neurosurgical inpatients treated at Emory University Hospital (EUH) and Grady Memorial Hospital (GMH) in Atlanta, Georgia. To inform decisions regarding investment in HIE, we develop a methodology analyzing crossover behavior for application to larger more general patient populations.