Search AMIA.org

Many research fields, including psychology and basic medical sciences, struggle with poor reproducibility of reported studies. Biomedical and health informatics is unlikely to be immune to these challenges. This paper explores replication in informatics and the unique challenges the discipline faces.

While depression and anxiety are common mental health issues, only a small segment of the population has access to standard one-on-one treatment. The use of smartphone apps can fill this gap. An app recommender system may help improve user engagement of these apps and eventually symptoms.

As available data increases, so does the opportunity to develop risk scores on more refined patient populations. In this paper we assessed the ability to derive a risk score for a patient no-showing to a clinic visit.

To understand whether user reviews of Instant Blood Pressure (IBP), an inaccurate, unregulated BP-measuring app reflected IBP's inaccuracy, to understand drivers for high and low ratings, and to understand if disclaimers prevented medical use.

We describe current practices of ethics-related data governance in large neuro-ICT projects, identify gaps in current practice, and put forward recommendations on how to collaborate ethically in complex regulatory and normative contexts.

This paper describes the unified LOINC/RSNA Radiology Playbook and the process by which it was produced.

To extract drug indications from a commercial drug knowledgebase and determine to what extent drug indications can discriminate between look-alike-sound-alike (LASA) drugs.

Mobile applications for improving diagnostic decision making often lack clinical evaluation. We evaluated if a mobile application improves generalist physicians' appropriate laboratory test ordering and diagnosis decisions and assessed if physicians perceive it as useful for learning.

Medical word sense disambiguation (WSD) is challenging and often requires significant training with data labeled by domain experts. This work aims to develop an interactive learning algorithm that makes efficient use of expert's domain knowledge in building high-quality medical WSD models with minimal human effort.