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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.

Ensuring the security and appropriate use of patient health information contained within electronic medical records systems is challenging. Observing these difficulties, we present an addition to the explanation-based auditing system (EBAS) that attempts to determine the clinical or operational reason why accesses occur to medical records based on patient diagnosis information. Accesses that can be explained with a reason are filtered so that the compliance officer has fewer suspicious accesses [...]

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 [...]

Online health knowledge resources contain answers to most of the information needs raised by clinicians in the course of care. However, significant barriers limit the use of these resources for decision-making, especially clinicians' lack of time. In this study we assessed the feasibility of automatically generating knowledge summaries for a particular clinical topic composed of relevant sentences extracted from Medline citations.

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.

Ascertainment of potential subjects has been a longstanding problem in clinical research. Various methods have been proposed, including using data in electronic health records. However, these methods typically suffer from scaling effects-some methods work well for large cohorts; others work for small cohorts only.

In order for computers to extract useful information from unstructured text, a concept normalization system is needed to link relevant concepts in a text to sources that contain further information about the concept. Popular concept normalization tools in the biomedical field are dictionary-based. In this study we investigate the usefulness of natural language processing (NLP) as an adjunct to dictionary-based concept normalization.

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 [...]