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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.

DNA samples are often processed and sequenced in facilities external to the point of collection. These samples are routinely labeled with patient identifiers or pseudonyms, allowing for potential linkage to identity and private clinical information if intercepted during transmission. We present a cryptographic scheme to securely transmit externally generated sequence data which does not require any patient identifiers, public key infrastructure, or the transmission of passwords.

In 2011, the US Supreme Court decided Sorrell v. IMS Health, Inc., a case that addressed the mining of large aggregated databases and the sale of prescriber data for marketing prescription drugs. The court struck down a Vermont law that required data mining companies to obtain permission from individual providers before selling prescription records that included identifiable physician prescription information to pharmaceutical companies for drug marketing. The decision was based [...]

Word sense disambiguation (WSD) methods automatically assign an unambiguous concept to an ambiguous term based on context, and are important to many text-processing tasks. In this study we developed and evaluated a knowledge-based WSD method that uses semantic similarity measures derived from the Unified Medical Language System (UMLS) and evaluated the contribution of WSD to clinical text classification.

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.

It has been claimed that most research findings are false, and it is known that large-scale studies involving omics data are especially prone to errors in design, execution, and analysis. The situation is alarming because taxpayer dollars fund a substantial amount of biomedical research, and because the publication of a research article that is later determined to be flawed can erode the credibility of an entire field, resulting in a [...]

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.

To examine how clinicians on the receiving end of admission handoffs use electronic health records (EHRs) in preparation for those handoffs and to identify the kinds of impacts such usage may have.