A risk identification model for detection of patients at risk of antidepressant discontinuation.

Purpose: Between 30 and 68% of patients prematurely discontinue their antidepressant treatment, posing significant risks to patient safety and healthcare outcomes. Online healthcare forums have the potential to offer a rich and unique source of data, revealing dimensions of antidepressant discontinuation that may not be captured by conventional data sources.

Methods: We analyzed 891 patient narratives from the online healthcare forum, "askapatient.

Direct Versus Indirect Query Performance of ICD-9/-10 Coding to Identify Anaphylaxis.

Background: Anaphylaxis is an often under =diagnosed, severe allergic event for which epidemiological data are sporadic. Researchers have leveraged administrative and claims data algorithms to study large databases of anaphylactic events; however, little longitudinal data analysis is available after transition to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM).

Objective: Study longitudinal trends in anaphylaxis incidence using direct and indirect query methods.

The PsyTAR dataset: From patients generated narratives to a corpus of adverse drug events and effectiveness of psychiatric medications.

The "Psychiatric Treatment Adverse Reactions" (PsyTAR) dataset contains patients' expression of effectiveness and adverse drug events associated with psychiatric medications. The PsyTAR was generated in four phases. In the first phase, a sample of 891 drugs reviews posted by patients on an online healthcare forum, "askapatient.

A systematic approach for developing a corpus of patient reported adverse drug events: A case study for SSRI and SNRI medications.

"Psychiatric Treatment Adverse Reactions" (PsyTAR) corpus is an annotated corpus that has been developed using patients narrative data for psychiatric medications, particularly SSRIs (Selective Serotonin Reuptake Inhibitor) and SNRIs (Serotonin Norepinephrine Reuptake Inhibitor) medications. This corpus consists of three main components: sentence classification, entity identification, and entity normalization. We split the review posts into sentences and labeled them for presence of adverse drug reactions (ADRs) (2168 sentences), withdrawal symptoms (WDs) (438 sentences), sign/symptoms/illness (SSIs) (789 sentences), drug indications (517), drug effectiveness (EF) (1087 sentences), and drug infectiveness (INF) (337 sentences).

Life-Threatening Allergies: Using a Patient-Engaged Approach.

Background: Adolescents at risk for anaphylaxis are a growing concern. Novel training methods are needed to better prepare individuals to manage anaphylaxis in the community.

Introduction: Didactic training as the sole method of anaphylaxis education has been shown to be ineffective.

Using health information technology to engage communities in health, education, and research.

The August 2011 Clinical and Translational Science Awards conference "Using IT to Improve Community Health: How Health Care Reform Supports Innovation" convened four "Think Tank" sessions. Thirty individuals, representing various perspectives on community engagement, attended the "Health information technology (HIT) as a resource to improve community health and education" session, which focused on using HIT to improve patient health, education, and research involvement. Participants discussed a range of topics using a semistructured format.

Accurate determination of imaging modality using an ensemble of text- and image-based classifiers.

Imaging modality can aid retrieval of medical images for clinical practice, research, and education. We evaluated whether an ensemble classifier could outperform its constituent individual classifiers in determining the modality of figures from radiology journals. Seventeen automated classifiers analyzed 77,495 images from two radiology journals.