Search AMIA.org

Privacy-preserving data publishing addresses the problem of disclosing sensitive data when mining for useful information. Among existing privacy models, ε-differential privacy provides one of the strongest privacy guarantees and makes no assumptions about an adversary's background knowledge. All existing solutions that ensure ε-differential privacy handle the problem of disclosing relational and set-valued data in a privacy-preserving manner separately. In this paper, we propose an algorithm that considers both relational and [...]

Patient portal use has been associated with favorable outcomes, but we know less about how patients use and benefit from specific patient portal features.

To evaluate an online disease management system supporting patients with uncontrolled type 2 diabetes.

The clinical element model (CEM) is an information model designed for representing clinical information in electronic health records (EHR) systems across organizations. The current representation of CEMs does not support formal semantic definitions and therefore it is not possible to perform reasoning and consistency checking on derived models. This paper introduces our efforts to represent the CEM specification using the Web Ontology Language (OWL). The CEM-OWL representation connects the CEM [...]

The aim of this research was to automate the search of publications concerning adverse drug reactions (ADR) by defining the queries used to search MEDLINE and by determining the required threshold for the number of extracted publications to confirm the drug/event association in the literature.

Drug-drug interaction (DDI) alerting is an important form of clinical decision support, yet physicians often fail to attend to critical DDI warnings due to alert fatigue. We previously described a model for highlighting patients at high risk of a DDI by enhancing alerts with relevant laboratory data. We sought to evaluate the effect of this model on alert adherence in high-risk patients.

The increasing availability of clinical data from electronic medical records (EMRs) has created opportunities for secondary uses of health information. When used in machine learning classification, many data features must first be transformed by discretization.

We discuss the use of structural models for the analysis of biosurveillance related data.

Data-mining algorithms that can produce accurate signals of potentially novel adverse drug reactions (ADRs) are a central component of pharmacovigilance. We propose a signal-detection strategy that combines the adverse event reporting system (AERS) of the Food and Drug Administration and electronic health records (EHRs) by requiring signaling in both sources. We claim that this approach leads to improved accuracy of signal detection when the goal is to produce a highly [...]