Search AMIA.org

To try to lower patient re-identification risks for biomedical research databases containing laboratory test results while also minimizing changes in clinical data interpretation.

To determine what, if any, opportunity exists in using administrative medical claims data for supplemental reporting to the state infectious disease registry system.

This paper presents a coreference resolution system for clinical narratives. Coreference resolution aims at clustering all mentions in a single document to coherent entities.

Secondary use of clinical text is impeded by a lack of highly effective, low-cost de-identification methods. Both, manual and automated methods for removing protected health information, are known to leave behind residual identifiers. The authors propose a novel approach for addressing the residual identifier problem based on the theory of Hiding In Plain Sight (HIPS).

To test the feasibility of using text mining to depict meaningfully the experience of pain in patients with metastatic prostate cancer, to identify novel pain phenotypes, and to propose methods for longitudinal visualization of pain status.

Point-of-care information needs are common and frequently unmet. One solution to this problem is the use of Infobuttons, which are context-sensitive links from electronic health records (EHR) to knowledge resources, sometimes involving an intermediate broker known as an Infobutton Manager. Health Level Seven (HL7) has developed the Context-Aware Knowledge Retrieval (Infobutton) standard to standardize the integration between EHR systems and knowledge resources. While the standard specifies a set of context [...]

To build an effective co-reference resolution system tailored to the biomedical domain.

We present SHARE, a new system for statistical health information release with differential privacy. We present two case studies that evaluate the software on real medical datasets and demonstrate the feasibility and utility of applying the differential privacy framework on biomedical data.

To evaluate how clinical chemistry test results were assessed by volunteers when presented with four different visualization techniques.

We describe an approach for modeling temporal relationships in a large scale association analysis of electronic health record data. The addition of temporal information can inform hypothesis generation and help to explain the relationships. We applied this approach on a dataset containing 41.2 million time-stamped International Classification of Diseases, Ninth Revision (ICD-9) codes from 1.6 million patients.